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Research Paper Examples

Research paper examples are of great value for students who want to complete their assignments timely and efficiently. If you are a student in the university, your first stop in the quest for research paper examples will be the campus library where you can get to view the research sample papers of lecturers and other professionals in diverse fields plus those of fellow students who preceded you in the campus. Many college departments maintain libraries of previous student work, including large research papers, which current students can examine.

Embark on a journey of academic excellence with iResearchNet, your premier destination for research paper examples that illuminate the path to scholarly success. In the realm of academia, where the pursuit of knowledge is both a challenge and a privilege, the significance of having access to high-quality research paper examples cannot be overstated. These exemplars are not merely papers; they are beacons of insight, guiding students and scholars through the complex maze of academic writing and research methodologies.

At iResearchNet, we understand that the foundation of academic achievement lies in the quality of resources at one’s disposal. This is why we are dedicated to offering a comprehensive collection of research paper examples across a multitude of disciplines. Each example stands as a testament to rigorous research, clear writing, and the deep understanding necessary to advance in one’s academic and professional journey.

Access to superior research paper examples equips learners with the tools to develop their own ideas, arguments, and hypotheses, fostering a cycle of learning and discovery that transcends traditional boundaries. It is with this vision that iResearchNet commits to empowering students and researchers, providing them with the resources to not only meet but exceed the highest standards of academic excellence. Join us on this journey, and let iResearchNet be your guide to unlocking the full potential of your academic endeavors.

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Importance of Research Paper Examples

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A Sample Research Paper on Child Abuse

A research paper represents the pinnacle of academic investigation, a scholarly manuscript that encapsulates a detailed study, analysis, or argument based on extensive independent research. It is an embodiment of the researcher’s ability to synthesize a wealth of information, draw insightful conclusions, and contribute novel perspectives to the existing body of knowledge within a specific field. At its core, a research paper strives to push the boundaries of what is known, challenging existing theories and proposing new insights that could potentially reshape the understanding of a particular subject area.

The objective of writing a research paper is manifold, serving both educational and intellectual pursuits. Primarily, it aims to educate the author, providing a rigorous framework through which they engage deeply with a topic, hone their research and analytical skills, and learn the art of academic writing. Beyond personal growth, the research paper serves the broader academic community by contributing to the collective pool of knowledge, offering fresh perspectives, and stimulating further research. It is a medium through which scholars communicate ideas, findings, and theories, thereby fostering an ongoing dialogue that propels the advancement of science, humanities, and other fields of study.

Research papers can be categorized into various types, each with distinct objectives and methodologies. The most common types include:

• Analytical Research Paper: This type focuses on analyzing different viewpoints represented in the scholarly literature or data. The author critically evaluates and interprets the information, aiming to provide a comprehensive understanding of the topic.
• Argumentative or Persuasive Research Paper: Here, the author adopts a stance on a contentious issue and argues in favor of their position. The objective is to persuade the reader through evidence and logic that the author’s viewpoint is valid or preferable.
• Experimental Research Paper: Often used in the sciences, this type documents the process, results, and implications of an experiment conducted by the author. It provides a detailed account of the methodology, data collected, analysis performed, and conclusions drawn.
• Survey Research Paper: This involves collecting data from a set of respondents about their opinions, behaviors, or characteristics. The paper analyzes this data to draw conclusions about the population from which the sample was drawn.
• Comparative Research Paper: This type involves comparing and contrasting different theories, policies, or phenomena. The aim is to highlight similarities and differences, thereby gaining a deeper understanding of the subjects under review.
• Cause and Effect Research Paper: It explores the reasons behind specific actions, events, or conditions and the consequences that follow. The goal is to establish a causal relationship between variables.
• Review Research Paper: This paper synthesizes existing research on a particular topic, offering a comprehensive analysis of the literature to identify trends, gaps, and consensus in the field.

Understanding the nuances and objectives of these various types of research papers is crucial for scholars and students alike, as it guides their approach to conducting and writing up their research. Each type demands a unique set of skills and perspectives, pushing the author to think critically and creatively about their subject matter. As the academic landscape continues to evolve, the research paper remains a fundamental tool for disseminating knowledge, encouraging innovation, and fostering a culture of inquiry and exploration.

Browse Sample Research Papers

iResearchNet prides itself on offering a wide array of research paper examples across various disciplines, meticulously curated to support students, educators, and researchers in their academic endeavors. Each example embodies the hallmarks of scholarly excellence—rigorous research, analytical depth, and clear, precise writing. Below, we explore the diverse range of research paper examples available through iResearchNet, designed to inspire and guide users in their quest for academic achievement.

Anthropology Research Paper Examples

Our anthropology research paper examples delve into the study of humanity, exploring cultural, social, biological, and linguistic variations among human populations. These papers offer insights into human behavior, traditions, and evolution, providing a comprehensive overview of anthropological research methods and theories.

• Archaeology Research Paper
• Forensic Anthropology Research Paper
• Linguistics Research Paper
• Medical Anthropology Research Paper
• Social Problems Research Paper

Art Research Paper Examples

The art research paper examples feature analyses of artistic expressions across different cultures and historical periods. These papers cover a variety of topics, including art history, criticism, and theory, as well as the examination of specific artworks or movements.

• Performing Arts Research Paper
• Music Research Paper
• Architecture Research Paper
• Theater Research Paper
• Visual Arts Research Paper

Cancer Research Paper Examples

Our cancer research paper examples focus on the latest findings in the field of oncology, discussing the biological mechanisms of cancer, advancements in diagnostic techniques, and innovative treatment strategies. These papers aim to contribute to the ongoing battle against cancer by sharing cutting-edge research.

• Breast Cancer Research Paper
• Leukemia Research Paper
• Lung Cancer Research Paper
• Ovarian Cancer Research Paper
• Prostate Cancer Research Paper

Communication Research Paper Examples

These examples explore the complexities of human communication, covering topics such as media studies, interpersonal communication, and public relations. The papers examine how communication processes affect individuals, societies, and cultures.

• Advertising Research Paper
• Journalism Research Paper
• Media Research Paper
• Public Relations Research Paper
• Public Speaking Research Paper

Crime Research Paper Examples

The crime research paper examples provided by iResearchNet investigate various aspects of criminal behavior and the factors contributing to crime. These papers cover a range of topics, from theoretical analyses of criminality to empirical studies on crime prevention strategies.

• Computer Crime Research Paper
• Domestic Violence Research Paper
• Hate Crimes Research Paper
• Organized Crime Research Paper
• White-Collar Crime Research Paper

Criminal Justice Research Paper Examples

Our criminal justice research paper examples delve into the functioning of the criminal justice system, exploring issues related to law enforcement, the judiciary, and corrections. These papers critically examine policies, practices, and reforms within the criminal justice system.

• Capital Punishment Research Paper
• Community Policing Research Paper
• Corporal Punishment Research Paper
• Criminal Investigation Research Paper
• Criminal Justice System Research Paper
• Plea Bargaining Research Paper
• Restorative Justice Research Paper

Criminal Law Research Paper Examples

These examples focus on the legal aspects of criminal behavior, discussing laws, regulations, and case law that govern criminal proceedings. The papers provide an in-depth analysis of criminal law principles, legal defenses, and the implications of legal decisions.

• Actus Reus Research Paper
• Gun Control Research Paper
• Insanity Defense Research Paper
• International Criminal Law Research Paper
• Self-Defense Research Paper

Criminology Research Paper Examples

iResearchNet’s criminology research paper examples study the causes, prevention, and societal impacts of crime. These papers employ various theoretical frameworks to analyze crime trends and propose effective crime reduction strategies.

• Cultural Criminology Research Paper
• Education and Crime Research Paper
• Marxist Criminology Research Paper
• School Crime Research Paper
• Urban Crime Research Paper

Culture Research Paper Examples

The culture research paper examples examine the beliefs, practices, and artifacts that define different societies. These papers explore how culture shapes identities, influences behaviors, and impacts social interactions.

• Advertising and Culture Research Paper
• Material Culture Research Paper
• Popular Culture Research Paper
• Cross-Cultural Studies Research Paper
• Culture Change Research Paper

Economics Research Paper Examples

Our economics research paper examples offer insights into the functioning of economies at both the micro and macro levels. Topics include economic theory, policy analysis, and the examination of economic indicators and trends.

• Budget Research Paper
• Cost-Benefit Analysis Research Paper
• Fiscal Policy Research Paper
• Labor Market Research Paper

Education Research Paper Examples

These examples address a wide range of issues in education, from teaching methods and curriculum design to educational policy and reform. The papers aim to enhance understanding and improve outcomes in educational settings.

• Early Childhood Education Research Paper
• Information Processing Research Paper
• Multicultural Education Research Paper
• Special Education Research Paper
• Standardized Tests Research Paper

Health Research Paper Examples

The health research paper examples focus on public health issues, healthcare systems, and medical interventions. These papers contribute to the discourse on health promotion, disease prevention, and healthcare management.

• AIDS Research Paper
• Alcoholism Research Paper
• Disease Research Paper
• Health Economics Research Paper
• Health Insurance Research Paper
• Nursing Research Paper

History Research Paper Examples

Our history research paper examples cover significant events, figures, and periods, offering critical analyses of historical narratives and their impact on present-day society.

• Adolf Hitler Research Paper
• American Revolution Research Paper
• Ancient Greece Research Paper
• Apartheid Research Paper
• Christopher Columbus Research Paper
• Climate Change Research Paper
• Cold War Research Paper
• Columbian Exchange Research Paper
• Deforestation Research Paper
• Diseases Research Paper
• Earthquakes Research Paper
• Egypt Research Paper

Leadership Research Paper Examples

These examples explore the theories and practices of effective leadership, examining the qualities, behaviors, and strategies that distinguish successful leaders in various contexts.

• Implicit Leadership Theories Research Paper
• Judicial Leadership Research Paper
• Leadership Styles Research Paper
• Police Leadership Research Paper
• Political Leadership Research Paper
• Remote Leadership Research Paper

Mental Health Research Paper Examples

The mental health research paper examples provided by iResearchNet discuss psychological disorders, therapeutic interventions, and mental health advocacy. These papers aim to raise awareness and improve mental health care practices.

• ADHD Research Paper
• Anxiety Research Paper
• Autism Research Paper
• Depression Research Paper
• Eating Disorders Research Paper
• PTSD Research Paper
• Schizophrenia Research Paper
• Stress Research Paper

Political Science Research Paper Examples

Our political science research paper examples analyze political systems, behaviors, and ideologies. Topics include governance, policy analysis, and the study of political movements and institutions.

• American Government Research Paper
• Civil War Research Paper
• Communism Research Paper
• Democracy Research Paper
• Game Theory Research Paper
• Human Rights Research Paper
• International Relations Research Paper
• Terrorism Research Paper

Psychology Research Paper Examples

These examples delve into the study of the mind and behavior, covering a broad range of topics in clinical, cognitive, developmental, and social psychology.

• Artificial Intelligence Research Paper
• Assessment Psychology Research Paper
• Biological Psychology Research Paper
• Clinical Psychology Research Paper
• Cognitive Psychology Research Paper
• Developmental Psychology Research Paper
• Discrimination Research Paper
• Educational Psychology Research Paper
• Environmental Psychology Research Paper
• Experimental Psychology Research Paper
• Intelligence Research Paper
• Learning Disabilities Research Paper
• Personality Psychology Research Paper
• Psychiatry Research Paper
• Psychotherapy Research Paper
• Social Cognition Research Paper
• Social Psychology Research Paper

Sociology Research Paper Examples

The sociology research paper examples examine societal structures, relationships, and processes. These papers provide insights into social phenomena, inequality, and change.

• Family Research Paper
• Demography Research Paper
• Group Dynamics Research Paper
• Quality of Life Research Paper
• Social Change Research Paper
• Social Movements Research Paper
• Social Networks Research Paper

Technology Research Paper Examples

Our technology research paper examples address the impact of technological advancements on society, exploring issues related to digital communication, cybersecurity, and innovation.

• Computer Forensics Research Paper
• Genetic Engineering Research Paper
• History of Technology Research Paper
• Internet Research Paper
• Nanotechnology Research Paper

Other Research Paper Examples

• Abortion Research Paper
• Adoption Research Paper
• Animal Testing Research Paper
• Bullying Research Paper
• Diversity Research Paper
• Divorce Research Paper
• Drugs Research Paper
• Environmental Issues Research Paper
• Ethics Research Paper
• Evolution Research Paper
• Feminism Research Paper
• Food Research Paper
• Gender Research Paper
• Globalization Research Paper
• Juvenile Justice Research Paper
• Law Research Paper
• Management Research Paper
• Philosophy Research Paper
• Public Health Research Paper
• Religion Research Paper
• Science Research Paper
• Social Sciences Research Paper
• Statistics Research Paper
• Other Sample Research Papers

Each category of research paper examples provided by iResearchNet serves as a valuable resource for students and researchers seeking to deepen their understanding of a specific field. By offering a comprehensive collection of well-researched and thoughtfully written papers, iResearchNet aims to support academic growth and encourage scholarly inquiry across diverse disciplines.

Sample Research Papers: To Read or Not to Read?

When you get an assignment to write a research paper, the first question you ask yourself is ‘Should I look for research paper examples?’ Maybe, I can deal with this task on my own without any help. Is it that difficult?

Thousands of students turn to our service every day for help. It does not mean that they cannot do their assignments on their own. They can, but the reason is different. Writing a research paper demands so much time and energy that asking for assistance seems to be a perfect solution. As the matter of fact, it is a perfect solution, especially, when you need to work to pay for your studying as well.

Firstly, if you search for research paper examples before you start writing, you can save your time significantly. You look at the example and you understand the gist of your assignment within several minutes. Secondly, when you examine some sample paper, you get to know all the requirements. You analyze the structure, the language, and the formatting details. Finally, reading examples helps students to overcome writer’s block, as other people’s ideas can motivate you to discover your own ideas.

The significance of research paper examples in the academic journey of students cannot be overstated. These examples serve not only as a blueprint for structuring and formatting academic papers but also as a beacon guiding students through the complex landscape of academic writing standards. iResearchNet recognizes the pivotal role that high-quality research paper examples play in fostering academic success and intellectual growth among students.

Blueprint for Academic Success

Research paper examples provided by iResearchNet are meticulously crafted to demonstrate the essential elements of effective academic writing. These examples offer clear insights into how to organize a paper, from the introductory paragraph, through the development of arguments and analysis, to the concluding remarks. They showcase the appropriate use of headings, subheadings, and the integration of tables, figures, and appendices, which collectively contribute to a well-organized and coherent piece of scholarly work. By studying these examples, students can gain a comprehensive understanding of the structure and formatting required in academic papers, which is crucial for meeting the rigorous standards of academic institutions.

Sparking Ideas and Providing Evidence

Beyond serving as a structural guide, research paper examples act as a source of inspiration for students embarking on their research projects. These examples illuminate a wide array of topics, methodologies, and analytical frameworks, thereby sparking ideas for students’ own research inquiries. They demonstrate how to effectively engage with existing literature, frame research questions, and develop a compelling thesis statement. Moreover, by presenting evidence and arguments in a logical and persuasive manner, these examples illustrate the art of substantiating claims with solid research, encouraging students to adopt a similar level of rigor and depth in their work.

Enhancing Research Skills

Engagement with high-quality research paper examples is instrumental in improving research skills among students. These examples expose students to various research methodologies, from qualitative case studies to quantitative analyses, enabling them to appreciate the breadth of research approaches applicable to their fields of study. By analyzing these examples, students learn how to critically evaluate sources, differentiate between primary and secondary data, and apply ethical considerations in research. Furthermore, these papers serve as a model for effectively citing sources, thereby teaching students the importance of academic integrity and the avoidance of plagiarism.

In essence, research paper examples are a fundamental resource that can significantly enhance the academic writing and research capabilities of students. iResearchNet’s commitment to providing access to a diverse collection of exemplary papers reflects its dedication to supporting academic excellence. Through these examples, students are equipped with the tools necessary to navigate the challenges of academic writing, foster innovative thinking, and contribute meaningfully to the scholarly community. By leveraging these resources, students can elevate their academic pursuits, ensuring their research is not only rigorous but also impactful.

Custom Research Paper Writing Services

In the academic journey, the ability to craft a compelling and meticulously researched paper is invaluable. Recognizing the challenges and pressures that students face, iResearchNet has developed a suite of research paper writing services designed to alleviate the burden of academic writing and research. Our services are tailored to meet the diverse needs of students across all academic disciplines, ensuring that every research paper not only meets but exceeds the rigorous standards of scholarly excellence. Below, we detail the multifaceted aspects of our research paper writing services, illustrating how iResearchNet stands as a beacon of support in the academic landscape.

At iResearchNet, we understand the pivotal role that research papers play in the academic and professional development of students. With this understanding at our core, we offer comprehensive writing services that cater to the intricate process of research paper creation. Our services are designed to guide students through every stage of the writing process, from initial research to final submission, ensuring clarity, coherence, and scholarly rigor.

The Need for Research Paper Writing Services

Navigating the complexities of academic writing and research can be a daunting task for many students. The challenges of identifying credible sources, synthesizing information, adhering to academic standards, and articulating arguments cohesively are significant. Furthermore, the pressures of tight deadlines and the high stakes of academic success can exacerbate the difficulties faced by students. iResearchNet’s research paper writing services are crafted to address these challenges head-on, providing expert assistance that empowers students to achieve their academic goals with confidence.

Why Choose iResearchNet

Selecting the right partner for research paper writing is a pivotal decision for students and researchers aiming for academic excellence. iResearchNet stands out as the premier choice for several compelling reasons, each designed to meet the diverse needs of our clientele and ensure their success.

• Expert Writers : At iResearchNet, we pride ourselves on our team of expert writers who are not only masters in their respective fields but also possess a profound understanding of academic writing standards. With advanced degrees and extensive experience, our writers bring depth, insight, and precision to each paper, ensuring that your work is informed by the latest research and methodologies.
• Top Quality : Quality is the cornerstone of our services. We adhere to rigorous quality control processes to ensure that every paper we deliver meets the highest standards of academic excellence. Our commitment to quality means thorough research, impeccable writing, and meticulous proofreading, resulting in work that not only meets but exceeds expectations.
• Customized Solutions : Understanding that each research project has its unique challenges and requirements, iResearchNet offers customized solutions tailored to your specific needs. Whether you’re grappling with a complex research topic, a tight deadline, or specific formatting guidelines, our team is equipped to provide personalized support that aligns with your objectives.
• Affordable Prices : We believe that access to high-quality research paper writing services should not be prohibitive. iResearchNet offers competitive pricing structures designed to provide value without compromising on quality. Our transparent pricing model ensures that you know exactly what you are paying for, with no hidden costs or surprises.
• Timely Delivery : Meeting deadlines is critical in academic writing, and at iResearchNet, we take this seriously. Our efficient processes and dedicated team ensure that your paper is delivered on time, every time, allowing you to meet your academic deadlines with confidence.
• 24/7 Support : Our commitment to your success is reflected in our round-the-clock support. Whether you have a question about your order, need to communicate with your writer, or require assistance with any aspect of our service, our friendly and knowledgeable support team is available 24/7 to assist you.
• Money-Back Guarantee : Your satisfaction is our top priority. iResearchNet offers a money-back guarantee, ensuring that if for any reason you are not satisfied with the work delivered, you are entitled to a refund. This policy underscores our confidence in the quality of our services and our dedication to your success.

Choosing iResearchNet for your research paper writing needs means partnering with a trusted provider committed to excellence, innovation, and customer satisfaction. Our unparalleled blend of expert writers, top-quality work, customized solutions, affordability, timely delivery, 24/7 support, and a money-back guarantee makes us the ideal choice for students and researchers seeking to elevate their academic performance.

How It Works: iResearchNet’s Streamlined Process

Navigating the process of obtaining a top-notch research paper has never been more straightforward, thanks to iResearchNet’s streamlined approach. Our user-friendly system ensures that from the moment you decide to place your order to the final receipt of your custom-written paper, every step is seamless, transparent, and tailored to your needs. Here’s how our comprehensive process works:

• Place Your Order : Begin your journey to academic success by visiting our website and filling out the order form. Here, you’ll provide details about your research paper, including the topic, academic level, number of pages, formatting style, and any specific instructions or requirements. This initial step is crucial for us to understand your needs fully and match you with the most suitable writer.
• Make Payment : Once your order details are confirmed, you’ll proceed to the payment section. Our platform offers a variety of secure payment options, ensuring that your transaction is safe and hassle-free. Our transparent pricing policy means you’ll know exactly what you’re paying for upfront, with no hidden fees.
• Choose Your Writer : After payment, you’ll have the opportunity to choose a writer from our team of experts. Our writers are categorized based on their fields of expertise, academic qualifications, and customer feedback ratings. This step empowers you to select the writer who best matches your research paper’s requirements, ensuring a personalized and targeted approach to your project.
• Receive Your Work : Our writer will commence work on your research paper, adhering to the specified guidelines and timelines. Throughout this process, you’ll have the ability to communicate directly with your writer, allowing for updates, revisions, and clarifications to ensure the final product meets your expectations. Once completed, your research paper will undergo a thorough quality check before being delivered to you via your chosen method.
• Free Revisions : Your satisfaction is our priority. Upon receiving your research paper, you’ll have the opportunity to review the work and request any necessary revisions. iResearchNet offers free revisions within a specified period, ensuring that your final paper perfectly aligns with your academic requirements and expectations.

Our process is designed to provide you with a stress-free experience and a research paper that reflects your academic goals. From placing your order to enjoying the success of a well-written paper, iResearchNet is here to support you every step of the way.

Our Extras: Enhancing Your iResearchNet Experience

At iResearchNet, we are committed to offering more than just standard research paper writing services. We understand the importance of providing a comprehensive and personalized experience for each of our clients. That’s why we offer a range of additional services designed to enhance your experience and ensure your academic success. Here are the exclusive extras you can benefit from:

• VIP Service : Elevate your iResearchNet experience with our VIP service, offering you priority treatment from the moment you place your order. This service ensures your projects are given first priority, with immediate attention from our team, and direct access to our top-tier writers and editors. VIP clients also benefit from our highest level of customer support, available to address any inquiries or needs with utmost urgency and personalized care.
• Plagiarism Report : Integrity and originality are paramount in academic writing. To provide you with peace of mind, we offer a detailed plagiarism report with every research paper. This report is generated using advanced plagiarism detection software, ensuring that your work is unique and adheres to the highest standards of academic honesty.
• Text Messages : Stay informed about your order’s progress with real-time updates sent directly to your phone. This service ensures you’re always in the loop, providing immediate notifications about key milestones, writer assignments, and any changes to your order status. With this added layer of communication, you can relax knowing that you’ll never miss an important update about your research paper.
• Table of Contents : A well-organized research paper is key to guiding readers through your work. Our service includes the creation of a detailed table of contents, meticulously structured to reflect the main sections and subsections of your paper. This not only enhances the navigability of your document but also presents your research in a professional and academically appropriate format.
• Abstract Page : The abstract page is your research paper’s first impression, summarizing the essential points of your study and its conclusions. Crafting a compelling abstract is an art, and our experts are skilled in highlighting the significance, methodology, results, and implications of your research succinctly and effectively. This service ensures that your paper makes a strong impact from the very beginning.
• Editor’s Check : Before your research paper reaches you, it undergoes a final review by our team of experienced editors. This editor’s check is a comprehensive process that includes proofreading for grammar, punctuation, and spelling errors, as well as ensuring that the paper meets all your specifications and academic standards. This meticulous attention to detail guarantees that your paper is polished, professional, and ready for submission.

To ensure your research paper is of the highest quality and ready for submission, it undergoes a rigorous editor’s check. This final review process includes a thorough examination for any grammatical, punctuation, or spelling errors, as well as a verification that the paper meets all your specified requirements and academic standards. Our editors’ meticulous approach guarantees that your paper is polished, accurate, and exemplary.

By choosing iResearchNet and leveraging our extras, you can elevate the quality of your research paper and enjoy a customized, worry-free academic support experience.

A research paper is an academic piece of writing, so you need to follow all the requirements and standards. Otherwise, it will be impossible to get the high results. To make it easier for you, we have analyzed the structure and peculiarities of a sample research paper on the topic ‘Child Abuse’.

The paper includes 7300+ words, a detailed outline, citations are in APA formatting style, and bibliography with 28 sources.

To write any paper you need to write a great outline. This is the key to a perfect paper. When you organize your paper, it is easier for you to present the ideas logically, without jumping from one thought to another.

In the outline, you need to name all the parts of your paper. That is to say, an introduction, main body, conclusion, bibliography, some papers require abstract and proposal as well.

A good outline will serve as a guide through your paper making it easier for the reader to follow your ideas.

I. Introduction

Ii. estimates of child abuse: methodological limitations, iii. child abuse and neglect: the legalities, iv. corporal punishment versus child abuse, v. child abuse victims: the patterns, vi. child abuse perpetrators: the patterns, vii. explanations for child abuse, viii. consequences of child abuse and neglect, ix. determining abuse: how to tell whether a child is abused or neglected, x. determining abuse: interviewing children, xi. how can society help abused children and abusive families, introduction.

An introduction should include a thesis statement and the main points that you will discuss in the paper.

A thesis statement is one sentence in which you need to show your point of view. You will then develop this point of view through the whole piece of work:

‘The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood.’

Child abuse is a very real and prominent social problem today. The impact of child abuse affects more than one’s childhood, as the psychological and physical injuries often extend well into adulthood. Most children are defenseless against abuse, are dependent on their caretakers, and are unable to protect themselves from these acts.

Childhood serves as the basis for growth, development, and socialization. Throughout adolescence, children are taught how to become productive and positive, functioning members of society. Much of the socializing of children, particularly in their very earliest years, comes at the hands of family members. Unfortunately, the messages conveyed to and the actions against children by their families are not always the positive building blocks for which one would hope.

In 2008, the Children’s Defense Fund reported that each day in America, 2,421 children are confirmed as abused or neglected, 4 children are killed by abuse or neglect, and 78 babies die before their first birthday. These daily estimates translate into tremendous national figures. In 2006, caseworkers substantiated an estimated 905,000 reports of child abuse or neglect. Of these, 64% suffered neglect, 16% were physically abused, 9% were sexually abused, 7% were emotionally or psychologically maltreated, and 2% were medically neglected. In addition, 15% of the victims experienced “other” types of maltreatment such as abandonment, threats of harm to the child, and congenital drug addiction (National Child Abuse and Neglect Data System, 2006). Obviously, this problem is a substantial one.

In the main body, you dwell upon the topic of your paper. You provide your ideas and support them with evidence. The evidence include all the data and material you have found, analyzed and systematized. You can support your point of view with different statistical data, with surveys, and the results of different experiments. Your task is to show that your idea is right, and make the reader interested in the topic.

In this example, a writer analyzes the issue of child abuse: different statistical data, controversies regarding the topic, examples of the problem and the consequences.

Several issues arise when considering the amount of child abuse that occurs annually in the United States. Child abuse is very hard to estimate because much (or most) of it is not reported. Children who are abused are unlikely to report their victimization because they may not know any better, they still love their abusers and do not want to see them taken away (or do not themselves want to be taken away from their abusers), they have been threatened into not reporting, or they do not know to whom they should report their victimizations. Still further, children may report their abuse only to find the person to whom they report does not believe them or take any action on their behalf. Continuing to muddy the waters, child abuse can be disguised as legitimate injury, particularly because young children are often somewhat uncoordinated and are still learning to accomplish physical tasks, may not know their physical limitations, and are often legitimately injured during regular play. In the end, children rarely report child abuse; most often it is an adult who makes a report based on suspicion (e.g., teacher, counselor, doctor, etc.).

Even when child abuse is reported, social service agents and investigators may not follow up or substantiate reports for a variety of reasons. Parents can pretend, lie, or cover up injuries or stories of how injuries occurred when social service agents come to investigate. Further, there is not always agreement about what should be counted as abuse by service providers and researchers. In addition, social service agencies/agents have huge caseloads and may only be able to deal with the most serious forms of child abuse, leaving the more “minor” forms of abuse unsupervised and unmanaged (and uncounted in the statistical totals).

While most laws about child abuse and neglect fall at the state levels, federal legislation provides a foundation for states by identifying a minimum set of acts and behaviors that define child abuse and neglect. The Federal Child Abuse Prevention and Treatment Act (CAPTA), which stems from the Keeping Children and Families Safe Act of 2003, defines child abuse and neglect as, at minimum, “(1) any recent act or failure to act on the part of a parent or caretaker which results in death, serious physical or emotional harm, sexual abuse, or exploitation; or (2) an act or failure to act which presents an imminent risk or serious harm.”

Using these minimum standards, each state is responsible for providing its own definition of maltreatment within civil and criminal statutes. When defining types of child abuse, many states incorporate similar elements and definitions into their legal statutes. For example, neglect is often defined as failure to provide for a child’s basic needs. Neglect can encompass physical elements (e.g., failure to provide necessary food or shelter, or lack of appropriate supervision), medical elements (e.g., failure to provide necessary medical or mental health treatment), educational elements (e.g., failure to educate a child or attend to special educational needs), and emotional elements (e.g., inattention to a child’s emotional needs, failure to provide psychological care, or permitting the child to use alcohol or other drugs). Failure to meet needs does not always mean a child is neglected, as situations such as poverty, cultural values, and community standards can influence the application of legal statutes. In addition, several states distinguish between failure to provide based on financial inability and failure to provide for no apparent financial reason.

Statutes on physical abuse typically include elements of physical injury (ranging from minor bruises to severe fractures or death) as a result of punching, beating, kicking, biting, shaking, throwing, stabbing, choking, hitting (with a hand, stick, strap, or other object), burning, or otherwise harming a child. Such injury is considered abuse regardless of the intention of the caretaker. In addition, many state statutes include allowing or encouraging another person to physically harm a child (such as noted above) as another form of physical abuse in and of itself. Sexual abuse usually includes activities by a parent or caretaker such as fondling a child’s genitals, penetration, incest, rape, sodomy, indecent exposure, and exploitation through prostitution or the production of pornographic materials.

Finally, emotional or psychological abuse typically is defined as a pattern of behavior that impairs a child’s emotional development or sense of self-worth. This may include constant criticism, threats, or rejection, as well as withholding love, support, or guidance. Emotional abuse is often the most difficult to prove and, therefore, child protective services may not be able to intervene without evidence of harm to the child. Some states suggest that harm may be evidenced by an observable or substantial change in behavior, emotional response, or cognition, or by anxiety, depression, withdrawal, or aggressive behavior. At a practical level, emotional abuse is almost always present when other types of abuse are identified.

Some states include an element of substance abuse in their statutes on child abuse. Circumstances that can be considered substance abuse include (a) the manufacture of a controlled substance in the presence of a child or on the premises occupied by a child (Colorado, Indiana, Iowa, Montana, South Dakota, Tennessee, and Virginia); (b) allowing a child to be present where the chemicals or equipment for the manufacture of controlled substances are used (Arizona, New Mexico); (c) selling, distributing, or giving drugs or alcohol to a child (Florida, Hawaii, Illinois, Minnesota, and Texas); (d) use of a controlled substance by a caregiver that impairs the caregiver’s ability to adequately care for the child (Kentucky, New York, Rhode Island, and Texas); and (e) exposure of the child to drug paraphernalia (North Dakota), the criminal sale or distribution of drugs (Montana, Virginia), or drug-related activity (District of Columbia).

One of the most difficult issues with which the U.S. legal system must contend is that of allowing parents the right to use corporal punishment when disciplining a child, while not letting them cross over the line into the realm of child abuse. Some parents may abuse their children under the guise of discipline, and many instances of child abuse arise from angry parents who go too far when disciplining their children with physical punishment. Generally, state statutes use terms such as “reasonable discipline of a minor,” “causes only temporary, short-term pain,” and may cause “the potential for bruising” but not “permanent damage, disability, disfigurement or injury” to the child as ways of indicating the types of discipline behaviors that are legal. However, corporal punishment that is “excessive,” “malicious,” “endangers the bodily safety of,” or is “an intentional infliction of injury” is not allowed under most state statutes (e.g., state of Florida child abuse statute).

Most research finds that the use of physical punishment (most often spanking) is not an effective method of discipline. The literature on this issue tends to find that spanking stops misbehavior, but no more effectively than other firm measures. Further, it seems to hinder rather than improve general compliance/obedience (particularly when the child is not in the presence of the punisher). Researchers have also explained why physical punishment is not any more effective at gaining child compliance than nonviolent forms of discipline. Some of the problems that arise when parents use spanking or other forms of physical punishment include the fact that spanking does not teach what children should do, nor does it provide them with alternative behavior options should the circumstance arise again. Spanking also undermines reasoning, explanation, or other forms of parental instruction because children cannot learn, reason, or problem solve well while experiencing threat, pain, fear, or anger. Further, the use of physical punishment is inconsistent with nonviolent principles, or parental modeling. In addition, the use of spanking chips away at the bonds of affection between parents and children, and tends to induce resentment and fear. Finally, it hinders the development of empathy and compassion in children, and they do not learn to take responsibility for their own behavior (Pitzer, 1997).

One of the biggest problems with the use of corporal punishment is that it can escalate into much more severe forms of violence. Usually, parents spank because they are angry (and somewhat out of control) and they can’t think of other ways to discipline. When parents are acting as a result of emotional triggers, the notion of discipline is lost while punishment and pain become the foci.

In 2006, of the children who were found to be victims of child abuse, nearly 75% of them were first-time victims (or had not come to the attention of authorities prior). A slight majority of child abuse victims were girls—51.5%, compared to 48% of abuse victims being boys. The younger the child, the more at risk he or she is for child abuse and neglect victimization. Specifically, the rate for infants (birth to 1 year old) was approximately 24 per 1,000 children of the same age group. The victimization rate for children 1–3 years old was 14 per 1,000 children of the same age group. The abuse rate for children aged 4– 7 years old declined further to 13 per 1,000 children of the same age group. African American, American Indian, and Alaska Native children, as well as children of multiple races, had the highest rates of victimization. White and Latino children had lower rates, and Asian children had the lowest rates of child abuse and neglect victimization. Regarding living arrangements, nearly 27% of victims were living with a single mother, 20% were living with married parents, while 22% were living with both parents but the marital status was unknown. (This reporting element had nearly 40% missing data, however.) Regarding disability, nearly 8% of child abuse victims had some degree of mental retardation, emotional disturbance, visual or hearing impairment, learning disability, physical disability, behavioral problems, or other medical problems. Unfortunately, data indicate that for many victims, the efforts of the child protection services system were not successful in preventing subsequent victimization. Children who had been prior victims of maltreatment were 96% more likely to experience another occurrence than those who were not prior victims. Further, child victims who were reported to have a disability were 52% more likely to experience recurrence than children without a disability. Finally, the oldest victims (16–21 years of age) were the least likely to experience a recurrence, and were 51% less likely to be victimized again than were infants (younger than age 1) (National Child Abuse and Neglect Data System, 2006).

Child fatalities are the most tragic consequence of maltreatment. Yet, each year, children die from abuse and neglect. In 2006, an estimated 1,530 children in the United States died due to abuse or neglect. The overall rate of child fatalities was 2 deaths per 100,000 children. More than 40% of child fatalities were attributed to neglect, but physical abuse also was a major contributor. Approximately 78% of the children who died due to child abuse and neglect were younger than 4 years old, and infant boys (younger than 1) had the highest rate of fatalities at 18.5 deaths per 100,000 boys of the same age in the national population. Infant girls had a rate of 14.7 deaths per 100,000 girls of the same age (National Child Abuse and Neglect Data System, 2006).

One question to be addressed regarding child fatalities is why infants have such a high rate of death when compared to toddlers and adolescents. Children under 1 year old pose an immense amount of responsibility for their caretakers: they are completely dependent and need constant attention. Children this age are needy, impulsive, and not amenable to verbal control or effective communication. This can easily overwhelm vulnerable parents. Another difficulty associated with infants is that they are physically weak and small. Injuries to infants can be fatal, while similar injuries to older children might not be. The most common cause of death in children less than 1 year is cerebral trauma (often the result of shaken-baby syndrome). Exasperated parents can deliver shakes or blows without realizing how little it takes to cause irreparable or fatal damage to an infant. Research informs us that two of the most common triggers for fatal child abuse are crying that will not cease and toileting accidents. Both of these circumstances are common in infants and toddlers whose only means of communication often is crying, and who are limited in mobility and cannot use the toilet. Finally, very young children cannot assist in injury diagnoses. Children who have been injured due to abuse or neglect often cannot communicate to medical professionals about where it hurts, how it hurts, and so forth. Also, nonfatal injuries can turn fatal in the absence of care by neglectful parents or parents who do not want medical professionals to possibly identify an injury as being the result of abuse.

Estimates reveal that nearly 80% of perpetrators of child abuse were parents of the victim. Other relatives accounted for nearly 7%, and unmarried partners of parents made up 4% of perpetrators. Of those perpetrators that were parents, over 90% were biological parents, 4% were stepparents, and 0.7% were adoptive parents. Of this group, approximately 58% of perpetrators were women and 42% were men. Women perpetrators are typically younger than men. The average age for women abusers was 31 years old, while for men the average was 34 years old. Forty percent of women who abused were younger than 30 years of age, compared with 33% of men being under 30. The racial distribution of perpetrators is similar to that of victims. Fifty-four percent were white, 21% were African American, and 20% were Hispanic/Latino (National Child Abuse and Neglect Data System, 2006).

There are many factors that are associated with child abuse. Some of the more common/well-accepted explanations are individual pathology, parent–child interaction, past abuse in the family (or social learning), situational factors, and cultural support for physical punishment along with a lack of cultural support for helping parents here in the United States.

The first explanation centers on the individual pathology of a parent or caretaker who is abusive. This theory focuses on the idea that people who abuse their children have something wrong with their individual personality or biological makeup. Such psychological pathologies may include having anger control problems; being depressed or having post-partum depression; having a low tolerance for frustration (e.g., children can be extremely frustrating: they don’t always listen; they constantly push the line of how far they can go; and once the line has been established, they are constantly treading on it to make sure it hasn’t moved. They are dependent and self-centered, so caretakers have very little privacy or time to themselves); being rigid (e.g., having no tolerance for differences—for example, what if your son wanted to play with dolls? A rigid father would not let him, laugh at him for wanting to, punish him when he does, etc.); having deficits in empathy (parents who cannot put themselves in the shoes of their children cannot fully understand what their children need emotionally); or being disorganized, inefficient, and ineffectual. (Parents who are unable to manage their own lives are unlikely to be successful at managing the lives of their children, and since many children want and need limits, these parents are unable to set them or adhere to them.)

Biological pathologies that may increase the likelihood of someone becoming a child abuser include having substance abuse or dependence problems, or having persistent or reoccurring physical health problems (especially health problems that can be extremely painful and can cause a person to become more self-absorbed, both qualities that can give rise to a lack of patience, lower frustration tolerance, and increased stress).

The second explanation for child abuse centers on the interaction between the parent and the child, noting that certain types of parents are more likely to abuse, and certain types of children are more likely to be abused, and when these less-skilled parents are coupled with these more difficult children, child abuse is the most likely to occur. Discussion here focuses on what makes a parent less skilled, and what makes a child more difficult. Characteristics of unskilled parents are likely to include such traits as only pointing out what children do wrong and never giving any encouragement for good behavior, and failing to be sensitive to the emotional needs of children. Less skilled parents tend to have unrealistic expectations of children. They may engage in role reversal— where the parents make the child take care of them—and view the parent’s happiness and well-being as the responsibility of the child. Some parents view the parental role as extremely stressful and experience little enjoyment from being a parent. Finally, less-skilled parents tend to have more negative perceptions regarding their child(ren). For example, perhaps the child has a different shade of skin than they expected and this may disappoint or anger them, they may feel the child is being manipulative (long before children have this capability), or they may view the child as the scapegoat for all the parents’ or family’s problems. Theoretically, parents with these characteristics would be more likely to abuse their children, but if they are coupled with having a difficult child, they would be especially likely to be abusive. So, what makes a child more difficult? Certainly, through no fault of their own, children may have characteristics that are associated with child care that is more demanding and difficult than in the “normal” or “average” situation. Such characteristics can include having physical and mental disabilities (autism, attention deficit hyperactivity disorder [ADHD], hyperactivity, etc.); the child may be colicky, frequently sick, be particularly needy, or cry more often. In addition, some babies are simply unhappier than other babies for reasons that cannot be known. Further, infants are difficult even in the best of circumstances. They are unable to communicate effectively, and they are completely dependent on their caretakers for everything, including eating, diaper changing, moving around, entertainment, and emotional bonding. Again, these types of children, being more difficult, are more likely to be victims of child abuse.

Nonetheless, each of these types of parents and children alone cannot explain the abuse of children, but it is the interaction between them that becomes the key. Unskilled parents may produce children that are happy and not as needy, and even though they are unskilled, they do not abuse because the child takes less effort. At the same time, children who are more difficult may have parents who are skilled and are able to handle and manage the extra effort these children take with aplomb. However, risks for child abuse increase when unskilled parents must contend with difficult children.

Social learning or past abuse in the family is a third common explanation for child abuse. Here, the theory concentrates not only on what children learn when they see or experience violence in their homes, but additionally on what they do not learn as a result of these experiences. Social learning theory in the context of family violence stresses that if children are abused or see abuse (toward siblings or a parent), those interactions and violent family members become the representations and role models for their future familial interactions. In this way, what children learn is just as important as what they do not learn. Children who witness or experience violence may learn that this is the way parents deal with children, or that violence is an acceptable method of child rearing and discipline. They may think when they become parents that “violence worked on me when I was a child, and I turned out fine.” They may learn unhealthy relationship interaction patterns; children may witness the negative interactions of parents and they may learn the maladaptive or violent methods of expressing anger, reacting to stress, or coping with conflict.

What is equally as important, though, is that they are unlikely to learn more acceptable and nonviolent ways of rearing children, interacting with family members, and working out conflict. Here it may happen that an adult who was abused as a child would like to be nonviolent toward his or her own children, but when the chips are down and the child is misbehaving, this abused-child-turned-adult does not have a repertoire of nonviolent strategies to try. This parent is more likely to fall back on what he or she knows as methods of discipline.

Something important to note here is that not all abused children grow up to become abusive adults. Children who break the cycle were often able to establish and maintain one healthy emotional relationship with someone during their childhoods (or period of young adulthood). For instance, they may have received emotional support from a nonabusing parent, or they received social support and had a positive relationship with another adult during their childhood (e.g., teacher, coach, minister, neighbor, etc.). Abused children who participate in therapy during some period of their lives can often break the cycle of violence. In addition, adults who were abused but are able to form an emotionally supportive and satisfying relationship with a mate can make the transition to being nonviolent in their family interactions.

Moving on to a fourth familiar explanation for child abuse, there are some common situational factors that influence families and parents and increase the risks for child abuse. Typically, these are factors that increase family stress or social isolation. Specifically, such factors may include receiving public assistance or having low socioeconomic status (a combination of low income and low education). Other factors include having family members who are unemployed, underemployed (working in a job that requires lower qualifications than an individual possesses), or employed only part time. These financial difficulties cause great stress for families in meeting the needs of the individual members. Other stress-inducing familial characteristics are single-parent households and larger family size. Finally, social isolation can be devastating for families and family members. Having friends to talk to, who can be relied upon, and with whom kids can be dropped off occasionally is tremendously important for personal growth and satisfaction in life. In addition, social isolation and stress can cause individuals to be quick to lose their tempers, as well as cause people to be less rational in their decision making and to make mountains out of mole hills. These situations can lead families to be at greater risk for child abuse.

Finally, cultural views and supports (or lack thereof) can lead to greater amounts of child abuse in a society such as the United States. One such cultural view is that of societal support for physical punishment. This is problematic because there are similarities between the way criminals are dealt with and the way errant children are handled. The use of capital punishment is advocated for seriously violent criminals, and people are quick to use such idioms as “spare the rod and spoil the child” when it comes to the discipline or punishment of children. In fact, it was not until quite recently that parenting books began to encourage parents to use other strategies than spanking or other forms of corporal punishment in the discipline of their children. Only recently, the American Academy of Pediatrics has come out and recommended that parents do not spank or use other forms of violence on their children because of the deleterious effects such methods have on youngsters and their bonds with their parents. Nevertheless, regardless of recommendations, the culture of corporal punishment persists.

Another cultural view in the United States that can give rise to greater incidents of child abuse is the belief that after getting married, couples of course should want and have children. Culturally, Americans consider that children are a blessing, raising kids is the most wonderful thing a person can do, and everyone should have children. Along with this notion is the idea that motherhood is always wonderful; it is the most fulfilling thing a woman can do; and the bond between a mother and her child is strong, glorious, and automatic—all women love being mothers. Thus, culturally (and theoretically), society nearly insists that married couples have children and that they will love having children. But, after children are born, there is not much support for couples who have trouble adjusting to parenthood, or who do not absolutely love their new roles as parents. People look askance at parents who need help, and cannot believe parents who say anything negative about parenthood. As such, theoretically, society has set up a situation where couples are strongly encouraged to have kids, are told they will love kids, but then society turns a blind or disdainful eye when these same parents need emotional, financial, or other forms of help or support. It is these types of cultural viewpoints that increase the risks for child abuse in society.

The consequences of child abuse are tremendous and long lasting. Research has shown that the traumatic experience of childhood abuse is life changing. These costs may surface during adolescence, or they may not become evident until abused children have grown up and become abusing parents or abused spouses. Early identification and treatment is important to minimize these potential long-term effects. Whenever children say they have been abused, it is imperative that they be taken seriously and their abuse be reported. Suspicions of child abuse must be reported as well. If there is a possibility that a child is or has been abused, an investigation must be conducted.

Children who have been abused may exhibit traits such as the inability to love or have faith in others. This often translates into adults who are unable to establish lasting and stable personal relationships. These individuals have trouble with physical closeness and touching as well as emotional intimacy and trust. Further, these qualities tend to cause a fear of entering into new relationships, as well as the sabotaging of any current ones.

Psychologically, children who have been abused tend to have poor self-images or are passive, withdrawn, or clingy. They may be angry individuals who are filled with rage, anxiety, and a variety of fears. They are often aggressive, disruptive, and depressed. Many abused children have flashbacks and nightmares about the abuse they have experienced, and this may cause sleep problems as well as drug and alcohol problems. Posttraumatic stress disorder (PTSD) and antisocial personality disorder are both typical among maltreated children. Research has also shown that most abused children fail to reach “successful psychosocial functioning,” and are thus not resilient and do not resume a “normal life” after the abuse has ended.

Socially (and likely because of these psychological injuries), abused children have trouble in school, will have difficulty getting and remaining employed, and may commit a variety of illegal or socially inappropriate behaviors. Many studies have shown that victims of child abuse are likely to participate in high-risk behaviors such as alcohol or drug abuse, the use of tobacco, and high-risk sexual behaviors (e.g., unprotected sex, large numbers of sexual partners). Later in life, abused children are more likely to have been arrested and homeless. They are also less able to defend themselves in conflict situations and guard themselves against repeated victimizations.

Medically, abused children likely will experience health problems due to the high frequency of physical injuries they receive. In addition, abused children experience a great deal of emotional turmoil and stress, which can also have a significant impact on their physical condition. These health problems are likely to continue occurring into adulthood. Some of these longer-lasting health problems include headaches; eating problems; problems with toileting; and chronic pain in the back, stomach, chest, and genital areas. Some researchers have noted that abused children may experience neurological impairment and problems with intellectual functioning, while others have found a correlation between abuse and heart, lung, and liver disease, as well as cancer (Thomas, 2004).

Victims of sexual abuse show an alarming number of disturbances as adults. Some dislike and avoid sex, or experience sexual problems or disorders, while other victims appear to enjoy sexual activities that are self-defeating or maladaptive—normally called “dysfunctional sexual behavior”—and have many sexual partners.

Abused children also experience a wide variety of developmental delays. Many do not reach physical, cognitive, or emotional developmental milestones at the typical time, and some never accomplish what they are supposed to during childhood socialization. In the next section, these developmental delays are discussed as a means of identifying children who may be abused.

There are two primary ways of identifying children who are abused: spotting and evaluating physical injuries, and detecting and appraising developmental delays. Distinguishing physical injuries due to abuse can be difficult, particularly among younger children who are likely to get hurt or receive injuries while they are playing and learning to become ambulatory. Nonetheless, there are several types of wounds that children are unlikely to give themselves during their normal course of play and exploration. These less likely injuries may signal instances of child abuse.

While it is true that children are likely to get bruises, particularly when they are learning to walk or crawl, bruises on infants are not normal. Also, the back of the legs, upper arms, or on the chest, neck, head, or genitals are also locations where bruises are unlikely to occur during normal childhood activity. Further, bruises with clean patterns, like hand prints, buckle prints, or hangers (to name a few), are good examples of the types of bruises children do not give themselves.

Another area of physical injury where the source of the injury can be difficult to detect is fractures. Again, children fall out of trees, or crash their bikes, and can break limbs. These can be normal parts of growing up. However, fractures in infants less than 12 months old are particularly suspect, as infants are unlikely to be able to accomplish the types of movement necessary to actually break a leg or an arm. Further, multiple fractures, particularly more than one on a bone, should be examined more closely. Spiral or torsion fractures (when the bone is broken by twisting) are suspect because when children break their bones due to play injuries, the fractures are usually some other type (e.g., linear, oblique, compacted). In addition, when parents don’t know about the fracture(s) or how it occurred, abuse should be considered, because when children get these types of injuries, they need comfort and attention.

Head and internal injuries are also those that may signal abuse. Serious blows to the head cause internal head injuries, and this is very different from the injuries that result from bumping into things. Abused children are also likely to experience internal injuries like those to the abdomen, liver, kidney, and bladder. They may suffer a ruptured spleen, or intestinal perforation. These types of damages rarely happen by accident.

Burns are another type of physical injury that can happen by accident or by abuse. Nevertheless, there are ways to tell these types of burn injuries apart. The types of burns that should be examined and investigated are those where the burns are in particular locations. Burns to the bottom of the feet, genitals, abdomen, or other inaccessible spots should be closely considered. Burns of the whole hand or those to the buttocks are also unlikely to happen as a result of an accident.

Turning to the detection and appraisal of developmental delays, one can more readily assess possible abuse by considering what children of various ages should be able to accomplish, than by noting when children are delayed and how many milestones on which they are behind schedule. Importantly, a few delays in reaching milestones can be expected, since children develop individually and not always according to the norm. Nonetheless, when children are abused, their development is likely to be delayed in numerous areas and across many milestones.

As children develop and grow, they should be able to crawl, walk, run, talk, control going to the bathroom, write, set priorities, plan ahead, trust others, make friends, develop a good self-image, differentiate between feeling and behavior, and get their needs met in appropriate ways. As such, when children do not accomplish these feats, their circumstances should be examined.

Infants who are abused or neglected typically develop what is termed failure to thrive syndrome. This syndrome is characterized by slow, inadequate growth, or not “filling out” physically. They have a pale, colorless complexion and dull eyes. They are not likely to spend much time looking around, and nothing catches their eyes. They may show other signs of lack of nutrition such as cuts, bruises that do not heal in a timely way, and discolored fingernails. They are also not trusting and may not cry much, as they are not expecting to have their needs met. Older infants may not have developed any language skills, or these developments are quite slow. This includes both verbal and nonverbal means of communication.

Toddlers who are abused often become hypervigilant about their environments and others’ moods. They are more outwardly focused than a typical toddler (who is quite self-centered) and may be unable to separate themselves as individuals, or consider themselves as distinct beings. In this way, abused toddlers cannot focus on tasks at hand because they are too concerned about others’ reactions. They don’t play with toys, have no interest in exploration, and seem unable to enjoy life. They are likely to accept losses with little reaction, and may have age-inappropriate knowledge of sex and sexual relations. Finally, toddlers, whether they are abused or not, begin to mirror their parents’ behaviors. Thus, toddlers who are abused may mimic the abuse when they are playing with dolls or “playing house.”

Developmental delays can also be detected among abused young adolescents. Some signs include the failure to learn cause and effect, since their parents are so inconsistent. They have no energy for learning and have not developed beyond one- or two-word commands. They probably cannot follow complicated directions (such as two to three tasks per instruction), and they are unlikely to be able to think for themselves. Typically, they have learned that failure is totally unacceptable, but they are more concerned with the teacher’s mood than with learning and listening to instruction. Finally, they are apt to have been inadequately toilet trained and thus may be unable to control their bladders.

Older adolescents, because they are likely to have been abused for a longer period of time, continue to get further and further behind in their developmental achievements. Abused children this age become family nurturers. They take care of their parents and cater to their parents’ needs, rather than the other way around. In addition, they probably take care of any younger siblings and do the household chores. Because of these default responsibilities, they usually do not participate in school activities; they frequently miss days at school; and they have few, if any, friends. Because they have become so hypervigilant and have increasingly delayed development, they lose interest in and become disillusioned with education. They develop low self-esteem and little confidence, but seem old for their years. Children this age who are abused are still likely to be unable to control their bladders and may have frequent toileting accidents.

Other developmental delays can occur and be observed in abused and neglected children of any age. For example, malnutrition and withdrawal can be noticed in infants through teenagers. Maltreated children frequently have persistent or untreated illnesses, and these can become permanent disabilities if medical conditions go untreated for a long enough time. Another example can be the consequences of neurological damage. Beyond being a medical issue, this type of damage can cause problems with social behavior and impulse control, which, again, can be discerned in various ages of children.

Once child abuse is suspected, law enforcement officers, child protection workers, or various other practitioners may need to interview the child about the abuse or neglect he or she may have suffered. Interviewing children can be extremely difficult because children at various stages of development can remember only certain parts or aspects of the events in their lives. Also, interviewers must be careful that they do not put ideas or answers into the heads of the children they are interviewing. There are several general recommendations when interviewing children about the abuse they may have experienced. First, interviewers must acknowledge that even when children are abused, they likely still love their parents. They do not want to be taken away from their parents, nor do they want to see their parents get into trouble. Interviewers must not blame the parents or be judgmental about them or the child’s family. Beyond that, interviews should take place in a safe, neutral location. Interviewers can use dolls and role-play to help children express the types of abuse of which they may be victims.

Finally, interviewers must ask age-appropriate questions. For example, 3-year-olds can probably only answer questions about what happened and who was involved. Four- to five-year-olds can also discuss where the incidents occurred. Along with what, who, and where, 6- to 8-year-olds can talk about the element of time, or when the abuse occurred. Nine- to 10-year-olds are able to add commentary about the number of times the abuse occurred. Finally, 11-year-olds and older children can additionally inform interviewers about the circumstances of abusive instances.

A conclusion is not a summary of what a writer has already mentioned. On the contrary, it is the last point made. Taking every detail of the investigation, the researcher makes the concluding point. In this part of a paper, you need to put a full stop in your research. You need to persuade the reader in your opinion.

Never add any new information in the conclusion. You can present solutions to the problem and you dwell upon the results, but only if this information has been already mentioned in the main body.

Child advocates recommend a variety of strategies to aid families and children experiencing abuse. These recommendations tend to focus on societal efforts as well as more individual efforts. One common strategy advocated is the use of public service announcements that encourage individuals to report any suspected child abuse. Currently, many mandatory reporters (those required by law to report abuse such as teachers, doctors, and social service agency employees) and members of communities feel that child abuse should not be reported unless there is substantial evidence that abuse is indeed occurring. Child advocates stress that this notion should be changed, and that people should report child abuse even if it is only suspected. Public service announcements should stress that if people report suspected child abuse, the worst that can happen is that they might be wrong, but in the grander scheme of things that is really not so bad.

Child advocates also stress that greater interagency cooperation is needed. This cooperation should be evident between women’s shelters, child protection agencies, programs for at-risk children, medical agencies, and law enforcement officers. These agencies typically do not share information, and if they did, more instances of child abuse would come to the attention of various authorities and could be investigated and managed. Along these lines, child protection agencies and programs should receive more funding. When budgets are cut, social services are often the first things to go or to get less financial support. Child advocates insist that with more resources, child protection agencies could hire more workers, handle more cases, conduct more investigations, and follow up with more children and families.

Continuing, more educational efforts must be initiated about issues such as punishment and discipline styles and strategies; having greater respect for children; as well as informing the community about what child abuse is, and how to recognize it. In addition, Americans must alter the cultural orientation about child bearing and child rearing. Couples who wish to remain child-free must be allowed to do so without disdain. And, it must be acknowledged that raising children is very difficult, is not always gloriously wonderful, and that parents who seek help should be lauded and not criticized. These kinds of efforts can help more children to be raised in nonviolent, emotionally satisfying families, and thus become better adults.

Bibliography

When you write a paper, make sure you are aware of all the formatting requirements. Incorrect formatting can lower your mark, so do not underestimate the importance of this part.

Organizing your bibliography is quite a tedious and time-consuming task. Still, you need to do it flawlessly. For this reason, analyze all the standards you need to meet or ask professionals to help you with it. All the comas, colons, brackets etc. matter. They truly do.

Bibliography:

• American Academy of Pediatrics: https://www.aap.org/
• Bancroft, L., & Silverman, J. G. (2002). The batterer as parent. Thousand Oaks, CA: Sage.
• Child Abuse Prevention and Treatment Act, 42 U.S.C.A. § 5106g (1998).
• Childhelp: Child Abuse Statistics: https://www.childhelp.org/child-abuse-statistics/
• Children’s Defense Fund: https://www.childrensdefense.org/
• Child Stats.gov: https://www.childstats.gov/
• Child Welfare League of America: https://www.cwla.org/
• Crosson-Tower, C. (2008). Understanding child abuse and neglect (7th ed.). Boston: Allyn & Bacon.
• DeBecker, G. (1999). Protecting the gift: Keeping children and teenagers safe (and parents sane). New York: Bantam Dell.
• Family Research Laboratory at the University of New Hampshire: https://cola.unh.edu/family-research-laboratory
• Guterman, N. B. (2001). Stopping child maltreatment before it starts: Emerging horizons in early home visitation services. Thousand Oaks, CA: Sage.
• Herman, J. L. (2000). Father-daughter incest. Cambridge, MA: Harvard University Press.
• Medline Plus, Child Abuse: https://medlineplus.gov/childabuse.html
• Myers, J. E. B. (Ed.). (1994). The backlash: Child protection under fire. Newbury Park, CA: Sage.
• National Center for Missing and Exploited Children: https://www.missingkids.org/home
• National Child Abuse and Neglect Data System. (2006). Child maltreatment 2006: Reports from the states to the National Child Abuse and Neglect Data System. Washington, DC: U.S. Department of Health and Human Services, Administration for Children and Families.
• New York University Silver School of Social Work: https://socialwork.nyu.edu/
• Pitzer, R. L. (1997). Corporal punishment in the discipline of children in the home: Research update for practitioners. Paper presented at the National Council on Family Relations Annual Conference, Washington, DC.
• RAND, Child Abuse and Neglect: https://www.rand.org/topics/child-abuse-and-neglect.html
• Richards, C. E. (2001). The loss of innocents: Child killers and their victims. Wilmington, DE: Scholarly Resources.
• Straus, M. A. (2001). Beating the devil out of them: Corporal punishment in American families and its effects on children. Edison, NJ: Transaction.
• Thomas, P. M. (2004). Protection, dissociation, and internal roles: Modeling and treating the effects of child abuse. Review of General Psychology, 7(15).
• U.S. Department of Health and Human Services, Administration for Children and Families: https://www.acf.hhs.gov/

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How to Write a Research Methodology for a Research Paper

Crafting a comprehensive research paper can be daunting. Understanding diverse citation styles and various subject areas presents a challenge for many.

Without clear examples, students often feel lost and overwhelmed, unsure of how to start or which style fits their subject.

Explore our collection of expertly written research paper examples. We’ve covered various citation styles and a diverse range of subjects.

So, read on!

• 1. Research Paper Example for Different Formats
• 2. Examples for Different Research Paper Parts
• 3. Research Paper Examples for Different Fields
• 4. Research Paper Example Outline

Research Paper Example for Different Formats

Following a specific formatting style is essential while writing a research paper . Knowing the conventions and guidelines for each format can help you in creating a perfect paper. Here we have gathered examples of research paper for most commonly applied citation styles :

Social Media and Social Media Marketing: A Literature Review

APA Research Paper Example

APA (American Psychological Association) style is commonly used in social sciences, psychology, and education. This format is recognized for its clear and concise writing, emphasis on proper citations, and orderly presentation of ideas.

Here are some research paper examples in APA style:

Research Paper Example APA 7th Edition

Research Paper Example MLA

MLA (Modern Language Association) style is frequently employed in humanities disciplines, including literature, languages, and cultural studies. An MLA research paper might explore literature analysis, linguistic studies, or historical research within the humanities. 

Here is an example:

Found Voices: Carl Sagan

Research Paper Example Chicago

Chicago style is utilized in various fields like history, arts, and social sciences. Research papers in Chicago style could delve into historical events, artistic analyses, or social science inquiries. 

Here is a research paper formatted in Chicago style:

Chicago Research Paper Sample

Research Paper Example Harvard

Harvard style is widely used in business, management, and some social sciences. Research papers in Harvard style might address business strategies, case studies, or social policies.

View this sample Harvard style paper here:

Harvard Research Paper Sample

Examples for Different Research Paper Parts

A research paper has different parts. Each part is important for the overall success of the paper. Chapters in a research paper must be written correctly, using a certain format and structure.

The following are examples of how different sections of the research paper can be written.

Research Proposal

The research proposal acts as a detailed plan or roadmap for your study, outlining the focus of your research and its significance. It's essential as it not only guides your research but also persuades others about the value of your study.

Example of Research Proposal

An abstract serves as a concise overview of your entire research paper. It provides a quick insight into the main elements of your study. It summarizes your research's purpose, methods, findings, and conclusions in a brief format.

Research Paper Example Abstract

Literature Review 

A literature review summarizes the existing research on your study's topic, showcasing what has already been explored. This section adds credibility to your own research by analyzing and summarizing prior studies related to your topic.

Literature Review Research Paper Example

Methodology

The methodology section functions as a detailed explanation of how you conducted your research. This part covers the tools, techniques, and steps used to collect and analyze data for your study.

Methods Section of Research Paper Example

How to Write the Methods Section of a Research Paper

The conclusion summarizes your findings, their significance and the impact of your research. This section outlines the key takeaways and the broader implications of your study's results.

Research Paper Conclusion Example

Research Paper Examples for Different Fields

Research papers can be about any subject that needs a detailed study. The following examples show research papers for different subjects.

History Research Paper Sample

Preparing a history research paper involves investigating and presenting information about past events. This may include exploring perspectives, analyzing sources, and constructing a narrative that explains the significance of historical events.

View this history research paper sample:

Many Faces of Generalissimo Fransisco Franco

Sociology Research Paper Sample

In sociology research, statistics and data are harnessed to explore societal issues within a particular region or group. These findings are thoroughly analyzed to gain an understanding of the structure and dynamics present within these communities. 

Here is a sample:

A Descriptive Statistical Analysis within the State of Virginia

Science Fair Research Paper Sample

A science research paper involves explaining a scientific experiment or project. It includes outlining the purpose, procedures, observations, and results of the experiment in a clear, logical manner.

Here are some examples:

Science Fair Paper Format

What Do I Need To Do For The Science Fair?

Psychology Research Paper Sample

Writing a psychology research paper involves studying human behavior and mental processes. This process includes conducting experiments, gathering data, and analyzing results to understand the human mind, emotions, and behavior.

Here is an example psychology paper:

The Effects of Food Deprivation on Concentration and Perseverance

Art History Research Paper Sample

Studying art history includes examining artworks, understanding their historical context, and learning about the artists. This helps analyze and interpret how art has evolved over various periods and regions.

Check out this sample paper analyzing European art and impacts:

European Art History: A Primer

Research Paper Example Outline

Before you plan on writing a well-researched paper, make a rough draft. An outline can be a great help when it comes to organizing vast amounts of research material for your paper.

Here is an outline of a research paper example:

Here is a downloadable sample of a standard research paper outline:

Research Paper Outline

Want to create the perfect outline for your paper? Check out this in-depth guide on creating a research paper outline for a structured paper!

Good Research Paper Examples for Students

Here are some more samples of research paper for students to learn from:

Fiscal Research Center - Action Plan

Qualitative Research Paper Example

Research Paper Example Introduction

How to Write a Research Paper Example

Research Paper Example for High School

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Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

40 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

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Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

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This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

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Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

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What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

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where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

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As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

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27 Real Primary Research Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

Primary research is a type of academic research that involves collecting new and original data to conduct a study.

Examples of primary research include studies that collect data through interviews, questionnaires, original text analysis, observation, surveys, focus groups, case studies, and ethnography.

It is the opposite of secondary research which involves looking at existing data to identify trends or new insights. Both secondary and primary research are legitimate forms of academic research.

Primary Research Examples

1. interviews.

Interviews involve approaching relevant people and asking them questions to gather their thoughts and opinions on a topic. This can take the form of structured, semi-strutured, and unstructured interviews.

Structured interviews generally do not involve back-and-forth discussion between the researcher and the research participant, while semi-structured and unstructured interviews involve the interviewer asking follow-up questions to dig deeper and elicit more insights.

2. Questionnaires and Surveys

Questionnaires are text-based interviews where a set of questions are written down by the researchers and sent to the research participants. The participants fill out the questionnaires and return them to the researcher.

The researcher then anonymizes the data and analyzes it by looking for trends and patterns across the dataset. They may do this manually or use research tools to find similarities and differences in the responses of the research participants.

A simple questionnaire can take the form of a Likert scale which involves asking a research participant to circle their opinion on a set of pre-determined responses (e.g. ‘Very Likely, Likely, Unlikely, Very Unlikely’). Other questionnaires require participants to write detailed paragraphs responding to questions which can then be analyzed.

One benefit of surveys over interviews is that it’s easier to gather large datasets.

3. Control Group Analysis

Control group analyses involve separating research participants into two groups: the control group and the experimental group.

An intervention is applied to the experimental group. Researchers then observe the results and compare them to the control group to find out the effects of the intervention.

This sort of research is very common in medical research. For example, a new pill on the market might be used on two groups of sick patients to see whether the pill was effective in improving one group’s condition. If so, it may receive approval to go into the market.

4. Observation Studies

Observational studies involve the researchers entering a research setting and recording their naturalistic observations of what they see. These observations can then form the basis of a thesis.

Longer-term observation studies where the researcher is embedded in a community are called ethnographic studies.

Tools for observation studies include simple pen-and-paper written vignettes about a topic, recording with the consent of research participants, or using field measuring devices.

Observational studies in fields like anthropology can lead to rich and detailed explanations of complex phenomena through a process called thick description . However, they’re inherently qualitative, subjective , and small-case studies that often make it difficult to make future predictions or hard scientific findings.

Another research limitation is that the presence of the researcher can sometimes affect the behavior of the people or animals being observed.

Go Deeper: 15 Ethnography Examples

5. Focus Groups

Focus groups are similar to interviews, but involve small groups of research participants interacting with the interviewer and, sometimes, one another.

Focus group research is common, for example, in political research, where political parties commission independent research organizations to collect data about the electorate’s perceptions of the candidates. This can help inform them of how to more effectively position the candidate in advertising and press stops.

The biggest benefit of focus group studies is that they can gather qualitative information from a wider range of research participants than one-to-one interviews. However, the downside is that research participants tend to influence each others’ responses.

See More: Examples of Focus Groups

6. Online Surveys

Online surveys are similar in purpose to offline questionnaires and surveys, but have unique benefits and limitations.

Like offline surveys and questionnaires, they can be in the form of written responses, multiple choice, and Likert scales.

However, they have some key benefits including: capacity to cast a wide net, ease of snowball sampling, and ease of finding participants.

These strengths also present some potential weaknesses: poorly designed online surveys may be corrupted if the sample is not sufficiently vetted and only distributed to non-representative sample sets (of course, this can be offset, depending on the study design).

7. Action Research

Action research involves practitioners conducting just-in-time research in an authentic setting to improve their own practice. The researcher is an active participant who studies the effects of interventions.

It sits in contrast to other forms of primary research in this list, which are mostly conducted by researchers who attempt to detach themselves from the subject of study. Action research, on the other hand, involves a researcher who is also a participant.

Action research is most commonly used in classrooms, where teachers take the role of researchers to improve their own teaching and learning practices. However, action research can be used in other fields as well, particularly healthcare and social work.

Go Deeper: 21 Action Research Examples

8. Discourse and Textual Analysis

Discourse and textual analyses are studies of language and text. They could involve, for example, the collection of a selection of newspaper articles published within a defined timeframe to identify the ideological leanings of the newspapers.

This sort of analysis can also explore the language use of media to study how media constructs stereotypes. The quintessential example is the study of gender identities is Disney texts, which has historically shown how Disney texts promote and normalize gender roles that children could internalize.

Textual analysis is often confused as a type of secondary research. However, as long as the texts are primary sources examined from scratch, it should be considered primary research and not the analysis of an existing dataset.

Go Deeper: 21 Discourse Analysis Examples

9. Multimodal, Visual, and Semiotic Analysis

Discourse and textual analyses traditionally focused on words and written text. But with the increasing presence of visual texts in our lives, scholars had to come up with primary research studies that involved the analysis of multimodal texts .

This led to studies such as semiotics and multimodal discourse analysis. This is still considered primary research because it involves the direct analysis of primary data (such as pictures, posters, and movies).

While these studies tend to borrow significantly from written text analysis, they include methods such as social semiotic to explore how signs and symbols garner meaning in social contexts. This enables scholars to examine, for example, children’s drawings through to famous artworks.

Often, primary research is a more highly-regarded type of research than secondary research because it involves gathering new data.

However, secondary research should not be discounted: the synthesis, categorization, and critique of an existing corpus of research can reveal excellent new insights and help to consolidate academic knowledge and even challenge longstanding assumptions .

References for the mentioned studies (APA Style)

Atack, L., & Rankin, J. (2002). A descriptive study of registered nurses’ experiences with web‐based learning. Journal of Advanced Nursing , 40 (4), 457-465.

Baughcum, A. E., Burklow, K. A., Deeks, C. M., Powers, S. W., & Whitaker, R. C. (1998). Maternal feeding practices and childhood obesity: a focus group study of low-income mothers. Archives of pediatrics & adolescent medicine , 152 (10), 1010-1014.

Calvert, M., & Sheen, Y. (2015). Task-based language learning and teaching: An action-research study. Language Teaching Research , 19 (2), 226-244.

Coker, D. L., Farley-Ripple, E., Jackson, A. F., Wen, H., MacArthur, C. A., & Jennings, A. S. (2016). Writing instruction in first grade: An observational study. Reading and Writing , 29 (5), 793-832.

Cossrow, N. H., Jeffery, R. W., & McGuire, M. T. (2001). Understanding weight stigmatization: A focus group study. Journal of nutrition education , 33 (4), 208-214.

Costello, J. (2006). Dying well: nurses’ experiences of ‘good and bad’deaths in hospital. Journal of advanced nursing , 54 (5), 594-601.

Deckx, L., Mitchell, G., Rosenberg, J., Kelly, M., Carmont, S. A., & Yates, P. (2019). General practitioners’ engagement in end-of-life care: a semi-structured interview study. BMJ Supportive & Palliative Care .

Drew, C. (2013). Elitism for sale: Promoting the elite school online in the competitive educational marketplace. Australian Journal of Education , 57 (2), 174-184.

Erdener, M. A., & Knoeppel, R. C. (2018). Parents’ Perceptions of Their Involvement in Schooling. International Journal of Research in Education and Science , 4 (1), 1-13.

Felicia, O. (2021). A social semiotic analysis of gender power in Nigeria’s newspaper political cartoons. Social Semiotics , 31 (2), 266-281.

Gardner, C. D., Trepanowski, J. F., Del Gobbo, L. C., Hauser, M. E., Rigdon, J., Ioannidis, J. P., … & King, A. C. (2018). Effect of low-fat vs low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion: the DIETFITS randomized clinical trial. Jama , 319 (7), 667-679.

Groves, M. M., & Zemel, P. C. (2000). Instructional technology adoption in higher education: An action research case study. International Journal of Instructional Media , 27 (1), 57.

Gullifer, J., & Tyson, G. A. (2010). Exploring university students’ perceptions of plagiarism: A focus group study. Studies in Higher Education , 35 (4), 463-481.

Karlsson, J., & Juslin, P. N. (2008). Musical expression: An observational study of instrumental teaching. Psychology of music , 36 (3), 309-334.

Liu, D., Huang, Y., Huang, C., Yang, S., Wei, X., Zhang, P., … & Zhang, H. (2022). Calorie restriction with or without time-restricted eating in weight loss. New England Journal of Medicine , 386 (16), 1495-1504.

Martin, F., & Bolliger, D. U. (2018). Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learning , 22 (1), 205-222.

Munro, M. (2018). House price inflation in the news: a critical discourse analysis of newspaper coverage in the UK. Housing Studies , 33 (7), 1085-1105.

O’bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters!. Computers & Education , 74 , 15-25.

Olsen, M., Udo, C., Dahlberg, L., & Boström, A. M. (2022). Older Persons’ Views on Important Values in Swedish Home Care Service: A Semi-Structured Interview Study. Journal of Multidisciplinary Healthcare , 15 , 967.

Ravn, I. M., Frederiksen, K., & Beedholm, K. (2016). The chronic responsibility: a critical discourse analysis of Danish chronic care policies. Qualitative Health Research , 26 (4), 545-554.

Sacks, F. M., Bray, G. A., Carey, V. J., Smith, S. R., Ryan, D. H., Anton, S. D., … & Williamson, D. A. (2009). Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. New England Journal of Medicine , 360 (9), 859-873.

Saltmarsh, S., Chapman, A., Campbell, M., & Drew, C. (2015). Putting “structure within the space”: Spatially un/responsive pedagogic practices in open-plan learning environments. Educational Review , 67 (3), 315-327.

Samaras, G., Bonoti, F., & Christidou, V. (2012). Exploring children’s perceptions of scientists through drawings and interviews. Procedia-Social and Behavioral Sciences , 46 , 1541-1546.

Sengul, K. (2019). Critical discourse analysis in political communication research: a case study of right-wing populist discourse in Australia. Communication Research and Practice , 5 (4), 376-392.

Smahel, D., Machackova, H., Mascheroni, G., Dedkova, L., Staksrud, E., Ólafsson, K., … & Hasebrink, U. (2020). EU Kids Online 2020: Survey results from 19 countries.

Stawarz, K., Preist, C., & Coyle, D. (2019). Use of smartphone apps, social media, and web-based resources to support mental health and well-being: online survey. JMIR mental health , 6 (7), e12546.Towns, M. H., Kreke, K., & Fields, A. (2000). An action research project: Student perspectives on small-group learning in chemistry. Journal of Chemical Education , 77 (1), 111.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “target population.”

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch ).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

Stratified Sampling

During stratified sampling , the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling .

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• The advantage of this method is that it should provide a representative sample.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

Reliability refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

Practical Considerations

Resource Constraints : Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

Diminishing Returns : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.

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Sample: Definition, Types, Formula & Examples

How often do researchers look for the right survey respondents, either for a market research study or an existing survey in the field? The sample or the respondents of this research may be selected from a set of customers or users that are known or unknown.

You may often know your typical respondent profile but don’t have access to the respondents to complete your research study. At such times, researchers and research teams reach out to specialized organizations to access their panel of respondents or buy respondents from them to complete research studies and surveys.

These could be general population respondents that match demographic criteria or respondents based on specific criteria. Such respondents are imperative to the success of research studies.

This article discusses in detail the different types of samples, sampling methods, and examples of each. It also mentions the steps to calculate the size, the details of an online sample, and the advantages of using them.

Content Index

• What is a sample?

Probability sampling methodologies with examples

Non-probability sampling methodologies with examples.

• How to determine a sample size
• Calculating sample size
• Sampling advantages

What is a Sample?

A sample is a smaller set of data that a researcher chooses or selects from a larger population using a pre-defined selection bias method. These elements are known as sample points, sampling units, or observations.

Creating a sample is an efficient method of conducting research . Researching the whole population is often impossible, costly, and time-consuming. Hence, examining the sample provides insights the researcher can apply to the entire population.

For example, if a cell phone manufacturer wants to conduct a feature research study among students in US Universities. An in-depth research study must be conducted if the researcher is looking for features that the students use, features they would like to see, and the price they are willing to pay.

This step is imperative to understand the features that need development, the features that require an upgrade, the device’s pricing, and the go-to-market strategy.

In 2016/17 alone, there were 24.7 million students enrolled in universities across the US. It is impossible to research all these students; the time spent would make the new device redundant, and the money spent on development would render the study useless.

Creating a sample of universities by geographical location and further creating a sample of these students from these universities provides a large enough number of students for research.

Typically, the population for market research is enormous. Making an enumeration of the whole population is practically impossible. The sample usually represents a manageable size of this population. Researchers then collect data from these samples through surveys, polls, and questionnaires and extrapolate this data analysis to the broader community.

LEARN ABOUT: Survey Sampling

Types of Samples: Selection methodologies with examples

The process of deriving a sample is called a sampling method. Sampling forms an integral part of the research design as this method derives the quantitative and qualitative data that can be collected as part of a research study. Sampling methods are characterized into two distinct approaches: probability sampling and non-probability sampling.

Probability sampling is a method of deriving a sample where the objects are selected from a population-based on probability theory. This method includes everyone in the population, and everyone has an equal chance of being selected. Hence, there is no bias whatsoever in this type of sample.

Each person in the population can subsequently be a part of the research. The selection criteria are decided at the outset of the market research study and form an important component of research.

LEARN ABOUT:   Action Research

Probability sampling can be further classified into four distinct types of samples. They are:

• Simple random sampling: The most straightforward way of selecting a sample is simple random sampling . In this method, each member has an equal chance of participating in the study. The objects in this sample population are chosen randomly, and each member has the same probability of being selected. For example, if a university dean would like to collect feedback from students about their perception of the teachers and level of education, all 1000 students in the University could be a part of this sample. Any 100 students can be selected randomly to be a part of this sample.
• Cluster sampling: Cluster sampling is a type of sampling method where the respondent population is divided into equal clusters. Clusters are identified and included in a sample based on defining demographic parameters such as age, location, sex, etc. This makes it extremely easy for a survey creator to derive practical inferences from the feedback. For example, if the FDA wants to collect data about adverse side effects from drugs, they can divide the mainland US into distinctive cluster analysis , like states. Research studies are then administered to respondents in these clusters. This type of generating a sample makes the data collection in-depth and provides easy-to-consume and act-upon, insights.
• Systematic sampling: Systematic sampling is a sampling method where the researcher chooses respondents at equal intervals from a population. The approach to selecting the sample is to pick a starting point and then pick respondents at a pre-defined sample interval. For example, while selecting 1,000 volunteers for the Olympics from an application list of 10,000 people, each applicant is given a count of 1 to 10,000. Then starting from 1 and selecting each respondent with an interval of 10, a sample of 1,000 volunteers can be obtained.
• Stratified random sampling: Stratified random sampling is a method of dividing the respondent population into distinctive but pre-defined parameters in the research design phase. In this method, the respondents don’t overlap but collectively represent the whole population. For example, a researcher looking to analyze people from different socioeconomic backgrounds can distinguish respondents by their annual salaries. This forms smaller groups of people or samples, and then some objects from these samples can be used for the research study.

LEARN ABOUT: Purposive Sampling

The non-probability sampling method uses the researcher’s discretion to select a sample. This type of sample is derived mostly from the researcher’s or statistician’s ability to get to this sample.

This type of sampling is used for preliminary research where the primary objective is to derive a hypothesis about the topic in research. Here each member does not have an equal chance of being a part of the sample population, and those parameters are known only post-selection to the sample.

We can classify non-probability sampling into four distinct types of samples. They are:

• Convenience sampling: Convenience sampling , in easy terms, stands for the convenience of a researcher accessing a respondent. There is no scientific method for deriving this sample. Researchers have nearly no authority over selecting the sample elements, and it’s purely done based on proximity and not representativeness.

This non-probability sampling method is used when there is time and costs limitations in collecting feedback. For example, researchers that are conducting a mall-intercept survey to understand the probability of using a fragrance from a perfume manufacturer. In this sampling method, the sample respondents are chosen based on their proximity to the survey desk and willingness to participate in the research.

• Judgemental/purposive sampling: The judgemental or purposive sampling method is a method of developing a sample purely on the basis and discretion of the researcher purely, based on the nature of the study along with his/her understanding of the target audience. This sampling method selects people who only fit the research criteria and end objectives, and the remaining are kept out.

For example, if the research topic is understanding what University a student prefers for Masters, if the question asked is “Would you like to do your Masters?” anything other than a response, “Yes” to this question, everyone else is excluded from this study.

• Snowball sampling: Snowball sampling or chain-referral sampling is defined as a non-probability sampling technique in which the samples have rare traits. This is a sampling technique in which existing subjects provide referrals to recruit samples required for a research study.

For example, while collecting feedback about a sensitive topic like AIDS, respondents aren’t forthcoming with information. In this case, the researcher can recruit people with an understanding or knowledge of such people and collect information from them or ask them to collect information.

• Quota sampling: Quota sampling is a method of collecting a sample where the researcher has the liberty to select a sample based on their strata. The primary characteristic of this method is that two people cannot exist under two different conditions. For example, when a shoe manufacturer would like to understand millennials’ perception of the brand with other parameters like comfort, pricing, etc. It selects only females who are millennials for this study as the research objective is to collect feedback about women’s shoes.

How to determine a Sample Size

As we have learned above, the right sample size determination is essential for the success of data collection in a market research study. But is there a correct number for the sample size? What parameters decide the sample size? What are the distribution methods of the survey?

To understand all of this and make an informed calculation of the right sample size, it is first essential to understand four important variables that form the basic characteristics of a sample. They are:

• Population size: The population size is all the people that can be considered for the research study. This number, in most cases, runs into huge amounts. For example, the population of the United States is 327 million. But in market research, it is impossible to consider all of them for the research study.
• The margin of error (confidence interval): The margin of error is depicted by a percentage that is a statistical inference about the confidence of what number of the population depicts the actual views of the whole population. This percentage helps towards the statistical analysis in selecting a sample and how much sampling error in this would be acceptable.

LEARN ABOUT: Research Process Steps

• Confidence level: This metric measures where the actual mean falls within a confidence interval. The most common confidence intervals are 90%, 95%, and 99%.
• Standard deviation: This metric covers the variance in a survey. A safe number to consider is .5, which would mean that the sample size has to be that large.

Calculating Sample Size

To calculate the sample size, you need the following parameters.

• Z-score: The Z-score value can be found   here .
• Standard deviation
• Margin of error
• Confidence level

To calculate use the sample size, use this formula:

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Consider the confidence level of 90%, standard deviation of .6 and margin of error, +/-4%

((1.64)2 x .6(.6)) / (.04)2

( 2.68x .0.36) / .0016

.9648 / .0016

603 respondents are needed and that becomes your sample size.

Try our sample size calculator to give population, margin of error calculator , and confidence level.

LEARN MORE: Population vs Sample

Sampling Advantages

As shown above, there are many advantages to sampling. Some of the most significant advantages are:

• Reduced cost & time: Since using a sample reduces the number of people that have to be reached out to, it reduces cost and time. Imagine the time saved between researching with a population of millions vs. conducting a research study using a sample.
• Reduced resource deployment: It is obvious that if the number of people involved in a research study is much lower due to the sample, the resources required are also much less. The workforce needed to research the sample is much less than the workforce needed to study the whole population .
• Accuracy of data: Since the sample indicates the population, the data collected is accurate. Also, since the respondent is willing to participate, the survey dropout rate is much lower, which increases the validity and accuracy of the data.
• Intensive & exhaustive data: Since there are lesser respondents, the data collected from a sample is intense and thorough. More time and effort are given to each respondent rather than collecting data from many people.
• Apply properties to a larger population: Since the sample is indicative of the broader population, it is safe to say that the data collected and analyzed from the sample can be applied to the larger population, which would hold true.

To collect accurate data for research, filter bad panelists, and eliminate sampling bias by applying different control measures. If you need any help arranging a sample audience for your next market research project, contact us at [email protected] . We have more than 22 million panelists across the world!

In conclusion, a sample is a subset of a population that is used to represent the characteristics of the entire population. Sampling is essential in research and data analysis to make inferences about a population based on a smaller group of individuals. There are different types of sampling, such as probability sampling, non-probability sampling, and others, each with its own advantages and disadvantages.

Choosing the right sampling method depends on the research question, budget, and resources is important. Furthermore, the sample size plays a crucial role in the accuracy and generalizability of the findings.

This article has provided a comprehensive overview of the definition, types, formula, and examples of sampling. By understanding the different types of sampling and the formulas used to calculate sample size, researchers and analysts can make more informed decisions when conducting research and data unit of analysis .

Sampling is an important tool that enables researchers to make inferences about a population based on a smaller group of individuals. With the right sampling method and sample size, researchers can ensure that their findings are accurate and generalizable to the population.

Utilize one of QuestionPro’s many survey questionnaire samples to help you complete your survey.

When creating online surveys for your customers, employees, or students, one of the biggest mistakes you can make is asking the wrong questions. Different businesses and organizations have different needs required for their surveys.

If you ask irrelevant questions to participants, they’re more likely to drop out before completing the survey. A questionnaire sample template will help set you up for a successful survey.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on November 21, 2023.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

Prevent plagiarism. Run a free check.

As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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ORIGINAL RESEARCH article

Causal associations between gut microbiota and premature rupture of membranes: a two-sample mendelian randomization study.

• 1 Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China
• 2 Department of Clinical Laboratory, Women and Children's Hospital of Chongqing Medical University, Chongqing, China
• 3 Department of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Gaotan, Chongqing, China

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Previous study has indicated a potential link between gut microbiota and maternal pregnancy outcomes. However, the causal relationship between gut microbiota and premature rupture of membranes (PROM) remains a topic of ongoing debate.A two-sample Mendelian Randomization (MR) study was used to investigate the relationship between gut microbiota and PROM. Genetic data on gut microbiota was obtained from the MiBioGen consortium's largest genome-wide association study (GWAS) (n=14,306). Genetic data on PROM (3011 cases and 104247 controls) were sourced from publicly available GWAS data from the Finnish National Biobank FinnGen consortium. Various methods including Inverse variance weighted (IVW), MR-Egger, simple mode, weighted median, and weighted mode were utilized to assess the causal relationship by calculating the odd ratio (OR) value and confidence interval (CI). Sensitivity analyses for quality control were performed using MR-Egger intercept tests, Cochran's Q tests, and leave-one-out analyses. Results: The IVW method revealed that class Mollicutes (IVW, OR=0.773, 95%CI: 0.61-0.981, pval = 0.034), genus Marvinbryantia (IVW, OR=00.736, 95%CI: 0.555-0.977, pval = 0.034), genus Ruminooccaceae UCG003 (IVW, OR=0.734, 95%CI: 0.568-0.947, pval = 0.017) and phylum Tenericutes (IVW, OR=0.773, 95%CI: 0.566-1.067, pval = 0.034) were associated with a reduced risk of PROM, while genus Collinsella (IVW, OR=1.444, 95%CI: 1.028-2.026, pval = 0.034), genus Intestinibacter (IVW, OR=1.304, 95%CI: 1.047-1.623, pval = 0.018) and genus Turicibacter (IVW, OR=1.282, 95%CI: 1.02-1.611, pval = 0.033) increased the risk of PROM. Based on the other four supplementary methods, six gut microbiota may have a potential effect on PROM. Due to the presence of pleiotropy (pval=0.045), genus Lachnoclostridium should be ruled out. No evidence of horizontal pleiotropy or heterogeneity was found in other microbiota (pval >0.05).In this study, we have discovered a causal relationship between the presence of specific probiotics and pathogens in the host and the risk of PROM. The identification of specific gut microbiota associated with PROM through MR studies offers a novel approach to diagnosing and treating this condition, thereby providing a new strategy for clinically preventing PROM.

Keywords: Gut Microbiota, Premature rupture of membranes, genetic variable, Mendelian randomization, causality

Received: 29 May 2024; Accepted: 12 Aug 2024.

Copyright: © 2024 Zhang, Li, Huang, Zou, Zou, Zhang, Su and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Jia F. Huang, Department of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University, Gaotan, Chongqing, China Qin Zou, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Xin Y. Zhang, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Yan Su, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China Chun L. Li, Department of Clinical Laboratory, Chongqing Health Center for Women and Children, Chongqing, China

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Open Access

Peer-reviewed

Research Article

#ForYou? the impact of pro-ana TikTok content on body image dissatisfaction and internalisation of societal beauty standards

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft, Writing – review & editing

Affiliation Faculty of Business, School of Psychology, Justice and Behavioural Science, Charles Sturt University, Wagga Wagga, New South Wales, Australia

Roles Conceptualization, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Madison R. Blackburn, 
• Rachel C. Hogg

• Published: August 7, 2024
• https://doi.org/10.1371/journal.pone.0307597
• Peer Review
• Reader Comments

Videos glamourising disordered eating practices and body image concerns readily circulate on TikTok. Minimal empirical research has investigated the impact of TikTok content on body image and eating behaviour. The present study aimed to fill this gap in current research by examining the influence of pro-anorexia TikTok content on young women’s body image and degree of internalisation of beauty standards, whilst also exploring the impact of daily time spent on TikTok and the development of disordered eating behaviours. An experimental and cross-sectional design was used to explore body image and internalisation of beauty standards in relation to pro-anorexia TikTok content. Time spent on TikTok was examined in relation to the risk of developing orthorexia nervosa. A sample of 273 female-identifying persons aged 18–28 years were exposed to either pro-anorexia or neutral TikTok content. Pre- and post-test measures of body image and internalisation of beauty standards were obtained. Participants were divided into four groups based on average daily time spent on TikTok. Women exposed to pro-anorexia content displayed the greatest decrease in body image satisfaction and an increase in internalisation of societal beauty standards. Women exposed to neutral content also reported a decrease in body image satisfaction. Participants categorised as high and extreme daily TikTok users reported greater average disordered eating behaviour on the EAT-26 than participants with low and moderate use, however this finding was not statistically significant in relation to orthorexic behaviours. This research has implications for the mental health of young female TikTok users, with exposure to pro-anorexia content having immediate consequences for internalisation and body image dissatisfaction, potentially increasing one’s risk of developing disordered eating beliefs and behaviours.

Citation: Blackburn MR, Hogg RC (2024) #ForYou? the impact of pro-ana TikTok content on body image dissatisfaction and internalisation of societal beauty standards. PLoS ONE 19(8): e0307597. https://doi.org/10.1371/journal.pone.0307597

Editor: Barbara Guidi, University of Pisa, ITALY

Received: November 2, 2023; Accepted: July 8, 2024; Published: August 7, 2024

Copyright: © 2024 Blackburn, Hogg. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data for this study can be found on Figshare via the following link: https://doi.org/10.6084/m9.figshare.25756800.v1 .

Funding: We acknowledge the financial support provided by Charles Sturt University.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Social media is a self-presentation device, a mode of entertainment, and a means of connecting with others [ 1 ], allowing for performance and the performance of identity [ 2 ], with social rewards built into its systems. Five to six years of the average human lifespan are now spent on social media sites [ 3 ] and visual platforms such as Instagram and TikTok increasingly dominate the cultural landscape of social media. Such visually oriented platforms are associated with higher levels of dysfunction in body image [ 4 ], while the COVID-19 pandemic has seen a rise in disordered eating behaviour [ 5 ]. Despite this, the field lacks a clear theoretical framework for understanding how social media usage heightens body image issues [ 6 ] and little research has specifically examined the impacts of TikTok based content. In this research, we sought to explore the impact of pro-anorexia TikTok content on body image satisfaction and internalisation of beauty standards for young women. The forthcoming sections of this literature review will highlight the features of social media content that may be particularly pernicious for young female users and will explore disordered eating and orthorexia in a social media context, concluding with a theoretical analysis of the relationship between social media and body image and internalisation of beauty standards, respectively.

Social media offers instant, quantifiable feedback coupled with idealised online imagery that may intersect with the value adolescents attribute to peer relationships and the sociocultural gender socialisation processes germane to this period of development, creating the “perfect storm” for young social media users, especially females [ 6 ]. In a study of 85 young, largely female eating disorder patients, a rise in awareness of online sites emphasizing thinness as beauty was evident from 2017 to 2020, with 60% of participants indicating that they knew of pro-ana websites and 22% of participants admitting to visiting them [ 7 ]. Research suggests that social media may also trigger those with extant eating disorders while simultaneously influencing healthy individuals to engage in disordered eating behaviour [ 8 ].

“Pro” eating disorder communities, hereafter referred to as “pro-ana” (pro-anorexia) communities, are a particular concern in a social media context. These communities encourage disordered eating, normalise disordered behaviours, and provide a means of connection for individuals who endorse anti-recovery from eating disorders [ 8 ]. Weight-loss tips, excessive exercise routines, and images of emaciated figures are routinely shared in these online communities [ 9 ], with extant research highlighting the association between viewing eating disorder content online and offline eating disorder behaviour [ 8 ]. Women who view pro-ana websites display increased eating disturbances, lowered body satisfaction, an increased drive for thinness, and higher levels of perfectionism when compared to women who have not viewed pro-ana content [ 10 , 11 ]. In research on adolescent girls, Stice [ 12 ] investigated the influence of exposure to media portraying the “thin-ideal” and found that perceived pressure to be thin was a predictor of increased body image dissatisfaction, which in turn led to increases in disordered eating behaviour. In similar research, Green [ 10 ] found that individuals with diagnosed eating disorders reported worsening symptoms after just 10-minutes of exposure to pro-ana content on the online platform, Tumblr.

Disordered eating #ForYou

The most downloaded social application (app) of 2021, TikTok is a social media platform that allows short-form video creation and sharing within a social media context [ 13 ]. Since its launch in 2017, TikTok has had over two billion downloads and has an estimated one billion users, the vast majority of which are children and teenagers [ 14 ]. Unlike other social media platforms where users have greater autonomy over the content generated on their homepage newsfeed, TikTok’s algorithm records data from single users and proposes videos designed to catch a user’s attention specifically, by creating a personalised “For You” page [ 15 ]. This feed will suggest videos from any creator on the platform, not just followed accounts. As such, if a user ‘interacts’ with a video, such as liking, sharing, commenting, or searching for related content, the algorithm will continue to produce similar videos on their “For You” page. The speed with which TikTok content can be created and consumed online may also be key to its impact. Any given social media user could watch more than a thousand videos on TikTok in an hour, creating a reinforcing effect that may have more impact than longer form content from a single creator [ 2 ].

Whilst the popularity of TikTok’s “For You” page has prompted global leaders in social media to build their own recommended content features, this feature remains most pronounced on TikTok. The “For You” page is the homepage of TikTok where users spend the majority of their time, compared to other social media platforms where homepages consist of a curation of content from followed accounts. Instagram’s explore page continues to emphasise established influencer culture and promote accounts of public figures or influencers with large followings. Contrastingly, TikTok’s unique algorithm makes content discoverability an even playing field, as any user’s content has the potential to reach a vast audience regardless of follower count or celebrity status. TikTok users therefore have less control over their homepage newsfeed compared to other social media platforms where users elect who they follow.

Unlike other social media platforms that implicitly showcase body ideals, TikTok contains explicit eating disorder content [ 16 ], while the “For You” page means that simply interacting with health and fitness videos can lead to unintended exposure to disordered eating content. Even seemingly benign “fitspiration” content may have psychological consequences for viewers. Beyond explicit pro-ana content, #GymTok and #FoodTok are two popular areas of content that provide a forum for users to create and consume content around their and others’ daily eating routines, weight loss transformations, and workout routines [ 2 ]. TikTok also frequently features content promoting clean eating, detox cleanses, and limited ingredient diets reflective of the current “food as medicine” movement of western culture [ 17 ], otherwise known as orthorexia. Despite efforts to ban such pro-ana related content, some videos easily circumvent controls [ 18 ], in part because many TikTok creators are non-public figures who are not liable to the backlash or cancellation that a public figure might receive for circulating socially irresponsible content.

Orthorexia: The rise of ‘healthy’ eating pathologies

Psychological analyses of eating disorders have historically focused on restrictive eating and the binge-purge cycle, however, more recently “positive” interests in nutrition have been examined. Orthorexia nervosa is characterised by a restrictive diet, ritualized patterns of eating, and rigid avoidance of foods deemed unhealthy or impure that consumes an individual’s focus [ 19 ]. Despite frequent observation of this distinct behavioural pattern by clinicians, orthorexia has received limited empirical attention and is not formally recognised as a psychiatric disorder [ 19 ]. Orthorexia and anorexia nervosa share traits of perfectionism, high trait anxiety, a high need to exert control, plus the potential for significant weight loss [ 19 ]. Termed ‘the disorder that cannot be diagnosed’ due to limited consensus around its features and the line between healthy and pathological eating practices, orthorexia mirrors the narrative of neoliberal self-improvement culture, wherein the body is treated as a site of performance and transformation.

Orthorexic restrictions and obsessions are routinely interpreted as signs of morality, health consciousness, and wellness [ 20 , 21 ]. Social media wellness influencers have played a significant role in normalising “clean [disordered] eating”. As one example, Turner and Lefevre [ 22 ] conducted an online survey of social media users following health food accounts and found that higher Instagram use was associated with a greater tendency towards orthorexia, with the prevalence of orthorexia among the study population at 49%, substantially higher than the general population (<1%). Similar health and food-related content on TikTok may provoke orthorexic tendencies among TikTok users, however, limited research has investigated orthorexic eating behaviour in the context of TikTok. The current study aims to bridge this gap in the literature around TikTok use and orthorexic tendencies. Disordered eating behaviour in the present study was measured by two separate but related constructs. ‘Restrictive’ disordered eating relates to dieting, oral control, and bulimic symptoms, whilst ‘healthy’ disordered eating constitutes orthorexic-like preoccupation with health food.

Theoretical analysis of body image and social media

An established risk factor in the development and maintenance of disordered eating behaviour is negative body image. Body image is a multidimensional construct that represents an individual’s perceptions and attitudes about their physical-self and encompasses an evaluative function through which individuals compare perceptions of their actual “self” to “ideal” images [ 23 ]. This comparison may produce feelings of dissatisfaction about one’s own body image if a significant discrepancy exists between the actual and ideal self-image [ 23 ]. Body image is not necessarily congruent with actual physique, with research demonstrating that women categorised as having a healthy body mass index (BMI) nonetheless report dissatisfaction with their weight and engage in restrictive dietary behaviours to reduce their weight [ 24 ]. In addition, body image dissatisfaction is considered normative in Western society, particularly among adolescent women [ 25 ]. This may be attributable to the constant flow of media that exposes women to unrealistic images of thinness idealized within society [ 26 ].

One theoretical framework for understanding social media’s relationship with body image is the Social Comparison Theory, proposed by Festinger [ 27 ] who suggests that people naturally evaluate themselves in comparison to others via upward or downward social comparisons. Research supports the notion that women who frequently engage in maladaptive upward appearance-related social comparisons are more likely to experience body image dissatisfaction and disordered eating [ 25 , 28 ], while visual exposure to thin bodies may detrimentally modulate one’s level of body image satisfaction [ 29 – 31 ]. In their study of undergraduate females, Engeln-Maddox [ 29 ] found that participants made upward social comparisons to images of thin models which were strongly associated with decreases in body image satisfaction and internalisation of thinness. Similarly, Tiggemann [ 32 ] found that adolescents who spent more time watching television featuring attractive actors and actresses reported an increased desire for thinness, theorised to be a result of increased social comparison to attractive media personalities.

The Transactional Model [ 33 ] extends Social Comparison Theory by emphasising the multifaceted and complex nature of social media influences on body image. This model acknowledges that individual differences may predispose a person to utilise social media for gratification, and highlights that as time spent on social media increases, so too does body image dissatisfaction [ 33 ]. In line with this, a recent review of literature by Frieiro Padín and colleagues [ 34 ] indicated that time spent on social media was strongly correlated with eating disorder psychopathologies, as well as heightened body image concerns, internalisation of the thin ideal, and lower levels of self-esteem. Time on social media also correlated with heightened body image concerns to a far greater extent than general internet usage [ 35 , 36 ].

Body image ideals are not static. The traditional ideal of rib-protruding bodies from the 90s, known colloquially as “heroin chic”, have recently shifted to a celebration of the “slim-thicc” figure, consisting of a cinched, flat waist with curvy hips, ample breasts, and large behinds [ 37 ]. The “slim-thicc” aesthetic allows women to be bigger than previous body ideals, yet this figure is arguably more unattainable than the thin-ideal as surgical intervention is commonly needed to achieve it, depending on genetics and body type. The idealisation of the “slim-thicc” figure is highlighted by the “Brazilian butt lift” (BBL), a potentially life-threatening procedure that is nonetheless the fastest growing category of plastic surgery, doubling in growth over the past five years, despite the life-threatening potential of the procedure [ 38 ]. Research suggests that the slim-thicc ideal is no less damaging nor threatening of body image than the thin-ideal. Indeed, in experimental research on body ideals, McComb and Mills [ 39 ] found that the greatest body dissatisfaction levels in female undergraduate students were observed among those exposed to imagery of the slim-thicc physique, relative to that exhibited by those exposed to the thin-ideal and fit-ideal physique, as well as the control condition.

Recent body ideals have also favoured muscular thin presentations, considered to represent health and fitness as evident in the “#fitspiration” Instagram hashtag that features over 65 million images [ 40 ]. Fitspiration has the potential to positively influence women’s health and wellbeing by promoting exercise engagement and healthy eating, yet various content analyses of fitspiration images highlight aspects of fitspiration that warrant concern [see 40 , 41 ]. Notably, fitspiration typically showcases only one body type and women whose bodies do not meet this standard may experience body dissatisfaction [ 40 ], while the gamification of exercise, such as receiving likes for every ten sit-ups, segues with the intensive self-control and competitiveness that often underpins eating disorders and eating disorder communities [ 1 ].

In recent experimental research, Pryde and Prichard [ 42 ] examined the effect of exposure to fitspiration TikTok content on the body dissatisfaction, appearance comparison, and mood of young Australian women. Viewing fitspiration TikTok videos led to increased negative mood and increased appearance comparison but did not impact body dissatisfaction. This finding contradicts previous research and may be due to fitspiration content showcasing body functionality rather than aesthetic, which may lead to positive outcomes for viewers. The fitspiration content used by Pryde and Prichard [ 42 ] did not contain the harmful themes regularly found in other forms of fitspiration content. Appearance comparison was significant in the relationship between TikTok content and body dissatisfaction and mood, suggesting that this may be a key mechanism through which fitspiration content leads to negative body image outcomes and supporting the notion that fitspiration promotes a focus on appearance rather than health.

Body image dissatisfaction among women is associated with co-morbid psychological disturbances and the development of disordered eating behaviours [ 43 , 44 ]. A large body of research indicates that higher levels of both general and appearance-related social comparison are associated with disordered eating in undergraduate populations [ 10 , 28 , 45 – 48 ]. As one example, Lindner et al. [ 46 ] investigated the impact of the female-to-male ratio of college campuses on female students’ engagement in social comparison and eating pathology. Their findings lend support to the Social Comparison Theory, indicating that the highest levels of eating pathology and social comparison were found among women attending colleges with predominantly female undergraduate populations. A strong relationship was also found between eating pathology and engagement in appearance-related social comparisons independent of actual weight. Lindner et al. [ 46 ] surmised that these results suggest social comparison and eating pathology behaviours are due to students’ perceptual distortions of their own bodies, potentially fostered by pressures exerted from peers to be thin.

Similarly, Corning et al. [ 45 ] investigated the social comparison behaviours of women with eating disorder symptoms and their asymptomatic peers. Results illustrated that a greater tendency to engage in everyday social comparison predicted the presence of eating disorder symptoms, while women with eating disorder symptoms made significantly more social comparisons of their own bodies. Such findings are supported by subsequent research, with Hamel et al. [ 28 ] finding that adolescents with a diagnosed eating disorder engaged in significantly more body-related social comparison than adolescents diagnosed with a depressive disorder or no diagnosis. Body-related social comparison was also significantly positively correlated with disordered eating behaviours. While extant research has focused upon social comparison as it has occurred through traditional media outlets, less research has investigated the facilitation of social comparison through social media platforms, particularly contemporary platforms such as TikTok.

Theoretical analysis of internalisation processes and social media

The extent to which one’s body image is impacted by images and messages conveyed by the media is determined by the degree to which these images and messages are internalised. Some may argue that social media platforms are distinct from what occurs in “real” life, creating fewer opportunities for internalisation to occur. Yet as Pierce [ 2 ] argues, platforms such as TikTok create their own realities, allowing users to explore their identities, form relationships, engage with culture and world events, and even develop new patterns of speech and writing. TikTok trends commonly infiltrate society, underscoring the impact of social media on life beyond the online world and thus a sociocultural analysis of TikTok is warranted. Sociocultural theories suggest that society portrays thinness as the ideal body shape for women, resulting in an internationalisation of the “thin is good” assumption for women. This in turn results in lowered body image satisfaction and other negative outcomes [ 43 ]. The significance of social influences, including the role of family, peers, and the media, is emphasised by sociocultural theory, with individuals more likely to internalise the thin ideal when they encounter pressuring messages that they are not thin enough from social influences [ 48 ]. Within this theoretical approach, an individual’s degree of thin ideal internalisation is theorised to depend on their acceptance of socially defined ideals of attractiveness and is reflected in their engagement in behaviours that adhere to these socially defined ideals [ 49 ].

Building on this, the tripartite influence model suggests that disordered eating behaviours arise due to pressure from social agents, specifically media, family, and peers. This pressure centres on conforming to appearance ideals and may lead to engagement in social comparison and the internalisation of thin ideals [ 48 ]. This is relevant in a digital context given social media provides endless opportunities for individuals to practice social comparison and for many users, social comparison on TikTok is peer-based as well as media-based. According to the tripartite model, social comparisons have been consistently associated with a higher degree of thin ideal internalisation, self-objectification, drive for thinness, and weight dissatisfaction [ 50 ]. Furthermore, and in contrast to traditional media where social agents are mainly models, celebrities, and movie stars, social agents on social media can include peers, friends, family, and individuals who have a relationship with the individual. Social media content generated by “everyday” people, rather than super models or movie stars, may result in comparisons that are more horizontal in nature. This is particularly evident on TikTok where content creators are rarely famous before creating a TikTok account, often remain micro-influencers after achieving some notoriety, and are usually around the same age as those viewing their content.

Pressure to be thin from alike peers may have a particularly pronounced impact on one’s degree of internalisation of the thinness ideal. Indeed, Stice et al. [ 51 ] found that after listening to young thin women complain about “feeling fat”, their adolescent participant sample reported increased body image dissatisfaction, suggesting that pressure from peers perpetuates the thinness ideal, leading to internalisation of the ideal and subsequent body dissatisfaction. Similarly, it was found that adolescent females were more likely to engage in weight loss behaviour if a high portion of peers with a similar BMI were also engaging in these behaviours, illustrating that appearance pressure exerted by alike peers may result in thin-ideal internalisation and engagement in weight loss behaviours to control body weight and shape [ 52 ]. Such findings raise questions around whether those most similar to us have the greatest impact upon thin-ideal internalisation, body image dissatisfaction, and disordered eating behaviours.

In further support for the tripartite influence model, research by Thompson et al. [ 48 ] indicates that the ideals promoted through social media trends are internalized despite being unattainable, resulting in body image dissatisfaction and disordered eating behaviour. Similarly, Mingoia et al. [ 53 ] found a positive association between the use of social networking sites and thin ideal internalisation in women, indicating that greater use of social networking sites was linked to significantly higher internalisation of the thin ideal. Interestingly, the use of appearance-related features (e.g., posting or viewing photographs or videos) was more strongly related with internalisation than the broad use of social networking sites (e.g., writing status’, messaging features) [ 53 ]. Correlational and experimental research alike has demonstrated that thin ideal internalisation is related to body image dissatisfaction and leads to expressions of disordered eating such as restrictive dieting and binge-purge symptoms [ 31 , 48 , 54 , 55 ]. Subsequent expressions of disordered eating may be seen as an attempt to control weight and body shape to conform to societal beauty standards of thinness [ 51 ].

This sociocultural perspective is exemplified by Grabe et al’s. [ 54 ] meta-analysis of research on the associations between media exposure to women’s body dissatisfaction, internalisation of the thin ideal, and eating behaviours and beliefs, illustrating that exposure to media images propagating the thin ideal is related to and indeed, may lead to body image concerns and increased endorsement of disordered eating behaviours in women. Similarly, Groesz et al. [ 55 ] conducted a meta-analysis to examine experimental manipulations of the thin beauty ideal. They found that body image was significantly more negative after viewing thin media images than after viewing images of average size models, plus size models, or inanimate objects. This effect size was stronger for participants who were more vulnerable to activation of the thinness schema. Groesz et al. [ 55 ] conclude that their results align with the sociocultural theory perspective that media promulgates a thin ideal that in turn provokes body dissatisfaction.

Current research

Existing research has established the relationship between body image dissatisfaction and disordered eating behaviours and social media platforms such as Instagram and Twitter. The unique implications of the TikTok ‘For You Page’, as well as the dominance of peer-created and explicit disordered eating content on TikTok suggests that the influence of this platform warrants specific consideration. This study adds to extant literature by utilising an experimental design to examine the influence of exposure to pro-ana TikTok content on body image and internalisation of societal beauty standards. A cross-sectional design was used to investigate the effect of daily TikTok and the development of disordered eating behaviours. Although body image disturbance and eating disorders are not limited to women, varying sociocultural factors have been implicated in the development of disordered eating behaviour in men and women [ 45 ], while issues facing trans people warrant specific consideration beyond the scope of this study, therefore the present sample contains only female-identifying participants.

Aims and hypotheses.

The current study aimed to investigate the impact of pro-ana TikTok content on young women’s body image satisfaction and internalisation of beauty standards, as well as exploring daily TikTok use and the development of disordered eating behaviour. First, in line with the cross-sectional component of the study, it was hypothesized that women who spend greater time on TikTok per day would report significantly more disordered eating behaviour than women who spend low amounts of time on TikTok per day. Second, it was hypothesized that women in the pro-ana TikTok group would report a significant decrease in body image satisfaction state following exposure to the pro-ana content compared to women in the control group. Third, it was hypothesized that women in the pro-ana Tik Tok group would report increased internalisation of societal beauty standards following exposure to pro-ana TikTok content compared to women in the control group.

Participants

Participants in the current study included 273 women aged between 18 to 28 years sourced from the general population of TikTok users. The predominant country of residence of the sample was Australia, with 15 participants indicating they currently reside outside of Australia. Of the remaining data relating to the two conditions of the study, 126 participants were randomly allocated into the experimental condition, and 147 participants were randomly allocated into the control condition. Snowball sampling was used to recruit participants through social media, online survey sharing platforms, and word-of-mouth, with first-year University students targeted for recruitment by offering class credit in return for participation. Participants could withdraw their consent at any time by exiting the study prior to completion of the survey.

The current study employed a questionnaire set that included a demographic questionnaire, and five scales measuring disordered eating behaviour, body satisfaction, and internalisation of societal beauty standards, as well as perfectionism, the latter of which was not examined in the present study.

Demographic questionnaire.

The demographic questionnaire required participants to answer a series of questions relating to their gender, age, relationship status, ethnicity, country of residence, TikTok usage, and exercise routine. A screening question redirected non-female-identifying persons from the study. Responses to the TikTok usage items were examined cross-sectionally with responses on the EAT-26 and ORTO15 used to examine the influence of daily TikTok use and the presentation of disordered eating behaviours.

Eating attitudes test.

The Eating Attitudes Test (EAT-26, [ 56 ]) is a short form of the original 40-item EAT scale [ 57 ] which measures symptoms and concerns characteristic of eating disorders. The 26-item short-form version of the EAT was utilised in the present study due to its established reliability and validity, and strong correlation with the EAT-40 [ 56 ].

Responses to the 26-items are self-reported using a 6-point Likert scale ranging from Always (3) to Never (0) [ 56 ]. The EAT-26 consists of three subscales including dieting, bulimia and food preoccupation, and oral control. Five behavioural questions are included in Part C of the EAT-26 to determine the presence and frequency of extreme weight-control behaviours including binge eating, self-induced vomiting, laxative usage, and excessive exercise [ 56 ]. Higher scores indicate greater disordered eating behaviour, and those with a total score of 20 or greater are, in clinical contexts, typically highlighted as requiring further assessment and advice of a mental health professional [ 56 ].

Internal consistency of the EAT-26 was established in initial psychometric studies which reported a Cronbach’s alpha of.85 [ 58 ]. For the current study, the Cronbach’s alpha = .91. Previous research has also demonstrated that the EAT-26 has strong test-retest reliability (e.g., 0.84) [ 59 ], as well as acceptable criterion-related validity for differentiating between eating disorder populations and non-disordered populations [ 56 ]. In the current study, the EAT-26 was used to measure disordered eating behaviour, and the cut-off score of 20 and above was adopted to categorise increased disordered eating behaviour. Given how this construct is measured, from this point forward the present study will refer to EAT-26 responses as ‘restrictive’ type disordered eating.

The ORTO-15 is a 15-item screening measure that assesses orthorexia nervosa risk through questions regarding the perceived effects of eating healthy food (e.g. “Do you think that consuming healthy food may improve your appearance?”), eating habits (e.g. “At present, are you alone when having meals?”), and the extent to which concerns about food influence daily life (e.g. “Does the thought of food worry you for more than three hours a day?”) [ 19 ]. Responses are self-reported using a 4-point Likert scale ranging from always , often , sometimes , or never . Individual items are coded and summed to derive a total score. Donini et al. [ 60 ] established a cut off total score of 40; scores below 40 indicate orthorexia behaviours, whilst scores 40 or above reflect normal eating behaviour. This cut off score was determined by Donini et al. [ 60 ] as their results revealed the ORTO-15 demonstrated good predictive capability at the threshold of 40 compared to other potential threshold values.

Although the ORTO-15 is the most widely accepted screening tool to assess orthorexia risk, it is still only partially validated [ 61 ], and inconsistencies of the measures’ reliability and validity exist in current literature. For example, Roncero et al. [ 62 ] estimated that the reliability of the ORTO-15 using Cronbach’s alpha was between 0.20 and 0.23, however, after removing certain items, the reliability coefficients were between 0.74 and 0.83. Contrastingly, Costa and colleagues’ [ 63 ] review of current literature surrounding orthorexia suggested adequate internal consistency (Cronbach’s alpha = 0.83 to 0.91) with all 15-items.

In the present study, a reliability analysis revealed unacceptable reliability for the ORTO-15 (α = .24). Principal components factor analysis identified two factors within the ORTO-15, one relating to dieting and the other to preoccupation with health food. Separate reliability analyses were performed on the items that comprised these two factors and the diet-related items did not have acceptable reliability (α = -.40), whilst the health food-related items bordered on acceptable reliability at α = .63. Consequently, only the health food-related items were retained in the current study following consideration of Pallant’s [ 64 ] assertion that Cronbach alpha values are sensitive to the number of items on a scale and it is therefore common to obtain low values on scales with less than ten items. Pallant [ 64 ] notes that in cases such as this, it is appropriate to report the inter-item correlation of the items, while Briggs and Cheek [ 65 ] advise an optimal range for the inter-item correlation between.2 to.4, with the health food-related items in the current study obtaining an inter-item correlation of.25. Throughout this study, the construct measured by these ORTO-15 items will be referred to as ‘healthy’ type disordered eating to reflect this obsessive health food preoccupation and differentiate between the two disordered eating dependent variables measured in the current study.

Body image states scale.

The Body Image States Scale (BISS) by Cash and colleagues [ 66 ] is a six-item measure of momentary evaluative and affective experiences of one’s own physical appearance. The BISS evaluates the following aspects of current body experience: dissatisfaction-satisfaction with overall physical appearance; dissatisfaction-satisfaction with one’s body size and shape; dissatisfaction-satisfaction with one’s weight; feelings of physical attractiveness-unattractiveness; current feelings about one’s looks relative to how one usually feels; and evaluation of one’s appearance relative to how the average person looks [ 66 ]. Participants responded to these items using a 9-point Likert-type scale which is presented in a negative-to-positive direction for half of the items, and a positive-to-negative direction for the other half [ 66 ]. Respondents were instructed to select the statement that best captured how they felt “ right now at this very moment ”. A total BISS score was calculated by reverse-scoring the three positive-to-negative items, summing the six-items, and finding the mean, with higher total BISS scores indicating more favourable body image states.

During the development and implementation of the BISS, Cash and colleagues [ 66 ] report acceptable internal consistency and moderate stability over time, an anticipated outcome due to the nature of the BISS as a state assessment tool. The BISS was also appropriately correlated with a range of trait measures of body image, highlighting its convergent validity [ 66 ]. Cash and colleagues [ 66 ] also report that the BISS is sensitive to reactions in positive and negative situational contexts and has good construct validity. An acceptable Cronbach’s alpha coefficient of.88 was obtained in the current study.

Sociocultural Attitudes Towards Appearance Questionnaire—4.

The Sociocultural Attitudes Towards Appearance Questionnaire– 4 (SATAQ-4) [ 67 ] is a 22-item self-report questionnaire that assesses the influence of interpersonal and sociocultural appearance ideals on one’s body image, eating disturbance, and self-esteem. Ratings are captured on a 5-point Likert scale which asks participants to specify their level of agreement with each statement by choosing from 1 ( definitely disagree ) through to 5 ( definitely agree ), with higher scores indicative of greater pressure to conform to, or greater internalisation of, interpersonal and sociocultural appearance ideals [ 67 ]. The five subscales of the SATAQ-4 measure: internalisation of thin/low body fat ideals, internalisation of muscular/athletic ideals, influence of pressures from family, influence of pressure from peers, and influence of pressures from the media [ 67 ]. For the purposes of the present study, the questions from the media pressure subscale were modified to enquire specifically about social media rather than traditional forms of media.

Across all samples in Schaefer et al’s. [ 67 ] study, the internal consistency of the five SATAQ-4 subscales is considered acceptable to excellent, with Cronbach’s alpha scores between 0.75 and 0.95. These subscales also displayed good convergent validity with other measures of body satisfaction, eating disorder risk, and self-esteem [ 67 ]. Pearson product-moment correlations between the SATAQ-4 subscales and convergent measures revealed medium to large positive associations with eating disorder symptomology, medium negative associations with body satisfaction, and small negative associations with self-esteem [ 67 ]. A Cronbach’s alpha of.87 was obtained in the present study, demonstrating acceptable internal consistency.

Ethical approval for the present study was granted by the Charles Sturt University Human Research Ethics Committee (Approval number H21155) prior to data collection. Participants were directed to the study via an online link to QuestionPro where they were provided an explanation of the study, their rights, and contact details of relevant support services if they were to become distressed. Participants gave informed consent by clicking on a link that read, “I consent to participate” at the beginning of the survey and then again through the submission of their completed survey. Any incomplete responses were not included in the dataset. Data collection commenced on the 30 th of July 2021 and ceased on the 1st of October 2021. In line with the cross-sectional and descriptive aspects of the research, participants were asked demographic questions about their gender, age, relationship status, ethnicity, country of residence, TikTok usage, and exercise habits. Participants then completed the experimental set in the following order: BISS (pre-test), SATAQ-4 (pre-test), EAT-26, ORTO-15, Experimental intervention (control or experimental TikTok video condition), SATAQ-4 (post-test), BISS (post-test), and debrief. All questionnaires presented to each participant were identical. Measures were not randomised to ensure that body image and internalisation were assessed at both pre- and post-test to evaluate the experimental manipulation.

Participants were randomly allocated to one of two conditions: experimental (pro-ana TikTok video) or control (“normal” TikTok video). Participants allocated to the experimental condition watched a compilation of TikTok videos containing explicit disordered eating messages such as young women restricting their food, displaying gallows humour about their disordered eating behaviour, starving themselves, and providing weight loss tips such as eating ice cubes and chewing gum to curve hunger. Participants in the experimental condition were also exposed to more implicit body image ideals typical of fitspiration-style content. This included thin women displaying their abdomens, cinched waists, dancing in two-piece swimwear, along with workout and juice cleanse videos promising fast weight loss. Participants in the control condition viewed a compilation of TikTok videos containing scenes relating to nature, cooking and recipes, animals, and comedy. After viewing the 7- to 8-minute TikTok video, all participants completed measures of internalisation and body satisfaction again to assess the influence of either the pro-ana TikTok video or the normal TikTok video. The debrief statement made explicit to participants the rationale of the study and explained the non-normative content of the videos shown to the experimental group. A small financial incentive was offered via a prize draw of five vouchers.

Statistical analysis

The data from QuestionPro was collated and analysed using IBM SPSS Statistics software, Version 28. All measures and manipulations in the study have been disclosed, alongside the method of determining the final sample size. No data collection was conducted following analysis of the data. Data for this study is available via the Figshare data repository and can be accessed at https://doi.org/10.6084/m9.figshare.25756800.v1 . This study was not preregistered. Sample size was determined before any data analysis. A priori power analyses were conducted using G*Power to determine the minimum sample sizes required to test the study hypotheses. Results indicated the required sample sizes to achieve 90% power for detecting medium effects, with a significance criterion of α = 0.05, were: N = 108 for the mixed between-within subjects ANOVAs and N = 232 for the one-way between groups ANOVAs. According to these recommendations, adequate statistical power was achieved. All univariate and multivariate assumptions were checked and found to be met. All scales and independent variables were normally distributed.

The analysis of the current study including data screening processes, descriptive statistics, and hypothesis testing will be presented in this section. Hypothesis testing began with two separate mixed between-within subjects analysis of variance models (ANOVAs) to examine the impact of the experimental manipulation on the independent variables of body image and internalisation of appearance ideals and pressure. Finally, the effect of time spent using TikTok daily on restrictive and ‘healthy’ disordered eating behaviour was explored cross-sectionally using two separate one-way between-subjects ANOVAs.

Data screening

Prior to statistical analysis, data were screened for entry errors and missing data. Of the 838 participants who initially consented to participate in the survey, 555 responses were insufficiently complete for data analysis. As participants were permitted to withdraw their consent by exiting the online survey, these results were excluded from all subsequent analyses. Of those that did not complete the study, the majority withdrew during the BISS (pre-test) and the ORTO-15, suggesting that these participants potentially experienced discomfort or distress when asked to reflect on their appearance and their eating behaviours. Of the completed responses, nine were excluded due to not meeting the study’s stated age eligibility and another case was excluded due to disclosure of a previous eating disorder diagnosis. The remaining data set comprised of 273 participants.

Descriptive statistics

Demographic characteristics..

In the current sample, 50% of participants reported being currently single and most participants (83%) were Caucasian, with 71% of participants indicating that they spent up to two hours per day using TikTok. Further demographic information is provided in Table 1 .

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https://doi.org/10.1371/journal.pone.0307597.t001

#ForYou: TikTok consumption demographics.

Participants in the current study reported that entertainment (75%), fashion (59%), beauty/skincare (54%), cooking/recipes (51%) and life hacks/advice (51%) content frequently occurred on their For You page. Largely in keeping with this, participants reported experiencing the most enjoyment from viewing entertainment (84%), life hacks/advice (57%), home renovation (56%), recipes/cooking (56%), and fashion (54%) content on their For You page.

In the current sample, 64% of participants reported being exposed to disordered eating content via their For You page. Only 15% of participants had not been exposed to any negative content themes. Further descriptive For You page content information is displayed below in Table 2 .

https://doi.org/10.1371/journal.pone.0307597.t002

Notably, 43% of the participant sample were frequently exposed to fitness and sports related content and the same percentage of the sample enjoyed seeing this content, suggesting that content broadly aligned with #fitspiration was consumed and appreciated by nearly half of participants. Concerningly, 40–60% of participants had been exposed to negative TikTok content via the For You Page, with content ranging from self-harm and suicidality to violence and illegal activity. No data was collected on the specifics of this content, however, and it is possible that some “negative” content may be framed from a proactive, preventative perspective, and this warrants further consideration.

Hypothesis testing: Cross-sectional analysis

Hypothesis 1: daily tiktok use and disordered eating behaviour..

To test the cross-sectional analysis of this study, two separate one-way between-groups ANOVAs were conducted to explore the impact of daily amount of TikTok use on ‘healthy’ disordered eating and restrictive disordered eating behaviour. This was necessary as time on TikTok was measured categorically. Participants were divided into four groups according to their average daily time spent using TikTok (Low use group: 1 hour or less; Moderate use group: 1–2 hours; High use group: 2–3 hours; Extreme use group: 3+ hours). Homogeneity of variance could be assumed for each ANOVA as indicated by non-significant Levene’s Test Statistics.

There was no statistically significant difference at the p < .05 level in ORTO15 scores for the four TikTok usage groups: F (3, 269) = .38, p = .78, indicating that ‘healthy’ disordered eating did not significantly differ across women who use TikTok for different periods of time per day. The effect size, calculated using eta squared, was.004, which is considered small in Cohen’s [ 68 ] terms. This small effect size is congruent with the non-significant finding.

The second ANOVA measuring differences among EAT-26 scores across the four TikTok usage groups also yielded a non-significant result: F (3, 269) = 1.21, p = .31. Eta squared was calculated as.01, representing a small effect size [ 68 ] consistent with this non-significant result. The means and standard deviations of the four TikTok usage groups across dependent variables of ‘healthy’ and restrictive disordered eating, as measured by the ORTO15 and the EAT-26 respectively, are displayed in Table 3 .

https://doi.org/10.1371/journal.pone.0307597.t003

Hypothesis testing: Experimental analyses

Hypothesis 2: body image satisfaction across groups from pre-test to post-test..

To evaluate the effect of the experimental intervention on body image, a 2 x 2 mixed between-within subjects ANOVA was conducted with condition (experimental vs control) as the between subjects factor and time (pre-manipulation vs post-manipulation) as the within subjects factor. All assumptions were upheld, including homogeneity of variance-covariance as indicated by Box’s M ( p >.001) and Levene’s ( p >.05) tests [ 64 ].

The interaction between condition and time was significant, Wilks’ Lambda = .98, F (1, 271) = 6.83, p = .009, partial eta squared = .03, demonstrating that the change in body image scores from pre-manipulation to post-manipulation was significantly different for the two groups. The body image satisfaction scores for women in both conditions decreased from pre-manipulation to post-manipulation. As anticipated, participants in the experimental condition reported a greater decrease in body image satisfaction than women in the control condition (see Table 4 ). This interaction effect is displayed in Fig 1 .

https://doi.org/10.1371/journal.pone.0307597.g001

https://doi.org/10.1371/journal.pone.0307597.t004

Although not consequential to the testing of the experimental manipulation, statistically significant main effects were also found for time, Wilks’ Lambda = .89, F (1, 271) = 32.99, p = < .001, partial eta squared = .109 and condition, F (1, 271) = 4.42, p = .036, partial eta squared = .016. The means and standard deviations of these main effects are displayed in Table 4 .

Hypothesis 3: Internalisation of societal beauty standards across groups from pre-test to post-test.

A second 2 x 2 mixed between-within subjects ANOVA was conducted to investigate the effect of the experimental manipulation on participants’ internalisation scores. All assumptions for the mixed model ANOVA were met with no violations.

A statistically significant interaction was found between group condition and time, Wilks’ Lambda = .97, F (1, 271) = 8.16, p = .005, partial eta squared = .029. This significant interaction highlights that the change in degree of internalisation at pre-manipulation and post-manipulation is not the same for the two conditions. Interestingly, the internalisation scores for women in the control group decreased from pre-manipulation to post-manipulation, whilst as anticipated, internalisation scores for women in the experimental group increased following exposure to the manipulation (see Table 5 ). This interaction is displayed in Fig 2 .

https://doi.org/10.1371/journal.pone.0307597.g002

https://doi.org/10.1371/journal.pone.0307597.t005

No statistically significant main effects were found for time, Wilks’ Lambda = .987, F (1, 271) = 3.59, p = .059, partial eta squared = .013 or condition, F (1, 271) = 2.65, p = .104, partial eta squared = .010. The means and standard deviations of internalisation scores for each condition at pre-manipulation and post-manipulation are displayed below in Table 5 .

The current study investigated the effect of TikTok content on women’s body image satisfaction and degree of internalisation of appearance ideals, and whether greater TikTok use contributed to increased disordered eating behaviour. In support of the hypotheses, exposure to pro-ana TikTok content significantly decreased participants’ body image satisfaction and increased participants’ degree of internalisation of appearance ideals. The hypothesis that greater daily TikTok use would contribute to increased disordered eating behaviour was not supported, as no statistically significant differences in restrictive disordered eating or ‘healthy’ disordered eating were found between the low, moderate, high, and extreme daily TikTok use groups.

Cross-sectional findings

Daily tiktok use and disordered eating behaviour..

Contrary to expectations, differences among groups on measures of restrictive disordered eating and ‘healthy’ disordered eating did not reach statistical significance. The proposed hypothesis that greater daily TikTok usage would be associated with disordered eating behaviour and attitudes was therefore unsupported. Despite lacking statistical support, participants categorised in the ‘high’ and ‘extreme’ daily TikTok use groups reported an average EAT-26 score of 18.16 and 19.09, respectively. Considering that an EAT-26 cut-off of ≥ 20 indicates potential clinical psychopathology, this mean score illustrates that exposure to TikTok content for two or more hours per day may contribute to a clinical degree of restrictive disordered eating.

The failure of the present study to detect any significant differences in disordered eating behaviours among participants with different TikTok daily usage does not align with the Transactional Model [ 33 ]. According to this model, risk factors such as low self-esteem and high thin ideal internalisation may predispose an individual to seek gratification via social media, resulting in body dissatisfaction and negative affect. The Transactional Model therefore proposes that a positive correlation exists between time spent on social media and body image dissatisfaction. Our findings also do not align with the conclusions Frieiro Padín et al. [ 34 ] drew from their review of the literature, in which a strong connection was identified between time on social media and heightened body image concerns and internalisation of the thin ideal, as well as eating disorder psychopathologies, though a distinction in outcome measures must be noted.

Based on the aforementioned sociocultural theory and previous research [see 28 , 43 , 48 ], it was assumed that increased body dissatisfaction as a result of increased time spent on social media (as stipulated by the Transactional Model), would lead to greater disordered eating behaviour. However, this was not supported statistically in the data. As postulated by Culbert et al. [ 69 ], disordered eating behaviour may instead only be a risk of media exposure if individuals are prone to endorse thin-ideals. Individuals in the present study that reported ‘high’ and ‘extreme’ daily TikTok use may have felt satisfied with their bodies and experienced lower thin-ideal internalisation. This could have potentially buffered the negative effect of greater TikTok content exposure and accounted for the lack of significant differences in disordered eating behaviour between groups. The quantity of TikTok consumption remains a pertinent question for disordered eating behaviour. As per the present study’s brief experimental manipulation, findings suggest that high frequency of daily TikTok use does not necessarily contribute to greater disordered eating behaviour than short exposures to this content.

Content presented to the pro-ana TikTok group included a mix of explicit and implicit pro- eating disorder messages as well as fitspiration content. Fitspiration content presented in the current study included workout videos to achieve a “smaller waist” and “toned abs” where female creators with slim, toned physiques sporting activewear took viewers through a series of exercises, advising viewers that they would “see results in a week”. In the present study, diet-related fitspiration content presented included the concoction of juices to “get rid of belly fat” and advice on the best “diet for a small waist” which requires avoidance of all meat, dairy, junk food, soda, and above all, to make “no excuses”. Fitspiration style content in the current study totalled one-minute, compared to disordered eating themes which totalled six minutes. The integration of these various types of content, although reflective of the For You function in TikTok, impeded our ability to determine the singular impact of fitspiration or disordered eating content, respectively, on body image and internalisation of societal beauty standards, but did reflect social media as it is consumed beyond experimental research settings.

Experimental findings

Tiktok and body image states..

The hypothesis that women exposed to pro-ana TikTok content would experience a significant decrease in body image compared to women who viewed the control TikTok content was supported. The present study found a significant interaction effect of body image between group condition (control vs experimental) and time period (pre-manipulation vs post-manipulation), as well as significant main effects. It is important to note that the statistic of interest in evaluating the success of the experimental manipulation is the interaction effect, thus main effects must be interpreted secondarily and with caution [ 64 ]. Women in the experimental group reported significantly lower body image satisfaction after exposure to the pro-ana TikTok content and compared to women who viewed the control content. This finding corroborates Festinger’s [ 27 ] Social Comparison Theory that posits people naturally evaluate themselves in comparison to others. Exposure to the pro-ana TikTok content, consisting of various thin bodies and messaging around weight loss, may have provided the opportunity for women to engage in maladaptive upward social comparisons, resulting in reduced body image satisfaction. The present study upholds previous findings of Engeln-Maddox, Tiggemann, McComb and Mills, and Gibson [ 29 , 32 , 39 , 70 ] who suggest that visual exposure to thin bodies may adversely affect one’s level of body image satisfaction and extends this research by replicating this finding in the context of a contemporary media platform, TikTok, and by utilising an experimental design.

Contradicting the present study and previous research, Pryde and Prichard [ 42 ] found no significant increase in young women’s body dissatisfaction following exposure to fitspiration TikTok content. A potential explanation for this finding is that the performance of physical movements captured in fitspiration videos may shift the focus of viewers from aesthetics to functionality, highlighting physical competencies and capabilities which has been shown to improve body image satisfaction in young women [ 71 ]. Pryde and Prichard’s [ 42 ] fitspiration content did not include typically occurring harmful themes as the present study did, potentially reducing the negative implications for body image satisfaction of exposure to such content in real world contexts.

Interestingly, women in the control group also reported a statistically significant decrease in body image satisfaction after viewing the neutral TikTok content, a finding that underscores the possible complexity of social media’s influence on body image, as identified in research by Huülsing [ 72 ]. This is an unexpected finding, as the TikTok content displayed to the control group was selected specifically to be unrelated to appearance ideals and pressures. One possible reason for this result is the repetition of administration of the BISS within a short time period. Completing the BISS twice may have caused participants to focus more attention on their body appearance than usual, resulting in more critical appraisals regardless of the experimental stimuli to which they were exposed. This notion aligns with previous research that found focusing on the appearance of body was associated with lower body image satisfaction, whereas focus on the function of the body was associated with more positive body image states [ 71 ].

One potential explanation for this finding is that the control group stimuli was contaminated and produced an unintentional effect on body image scores. Two-minutes of footage within the seven-minute control group TikTok compilation presented the human body including legs, arms, and hands. Although this body-related content was neutral in nature, it may be that even ‘harmless’ representations of the human body are sufficient to elicit a social comparison response in participants or in some capacity, reinforce the #fitspiration motifs commonly depicted on TikTok [ 1 ], therefore impacting body image scores at post-manipulation. This possible explanation has implications for TikTok use and women’s body image, as it suggests that viewing even benign content of human bodies for less than 10-minutes can have an immediate detrimental impact on body image states, even when this content is unrelated to body dissatisfaction, thinness, or weight loss. Furthermore, although a statistically significant body image decrease was detected in the control group, this finding must be interpreted with caution due to the significant interaction effect obtained.

TikTok and internalisation of societal beauty standards.

In accordance with the hypothesis, women in the experimental group reported a significant increase in their degree of internalisation of appearance ideals following exposure to pro-ana TikTok content. Women in the experimental group also reported significantly greater internalisation of appearance ideals than women in the control group. Conversely to the experimental group, internalisation scores of the control group decreased after viewing the neutral TikTok content. These findings are in line with the sociocultural theory, as women reported increased internalisation of societal beauty standards following exposure to media content explicitly and implicitly portraying the thinness ideal. The present study supports Mingoia et al’s. [ 53 ] meta-analysis, which yielded a positive association between social networking site use and the extent of internalisation of the thin ideal and furthers this notion by replicating the finding with TikTok specifically and utilising an experimental design.

In the current study, participants were subject to a single brief exposure of pro-ana TikTok content, whereas most of the sample indicated that their TikTok use was up to two hours per day. This suggests that the degree of internalisation of appearance ideals in participants lives outside of the experiment are likely to be much greater. Mingoia et al. [ 53 ] also found that the use of appearance-related features on social networking sites, such as posting and viewing photos and videos, demonstrated a stronger relationship with the internalisation of the thin ideal than the use of social networking features that were not appearance-related, such as messaging and writing status updates. As TikTok is a video sharing app and most of its content generally features full-body-length camera shots rather than a face or head shot, this finding suggests that TikTok users could potentially internalise body-related societal standards to a greater extent than users of other social media apps that typically feature head shots.

The finding that women internalised societal beauty standards to a greater degree after being exposed to pro-ana TikTok content corroborates the sociocultural theory’s emphasis of the significance of social influences in internalisation. TikTok users may be exposed to all three social influences (i.e., media, peers, and family) simultaneously on a single platform which may encourage internalisation of appearance-ideals in a more profound manner than any of these three influences in isolation. One point of difference between TikTok and other social media apps is that much content on the app is generated by “ordinary” individuals, rather than supermodels or celebrities. This enables blatantly insidious and diet-related content to circulate the app with less policing and scrutiny compared to content produced by an influencer or celebrity who may be more likely to be criticised or cancelled for socially irresponsible messaging and also provides the opportunity for more horizontal social comparisons and peer-to-peer style interactions rather than upward social comparisons.

Indeed, in their study of American teens, Mueller et al. [ 52 ] identified that girls were especially likely to engage in weight loss behaviour if a high proportion of girls with a similar BMI were also engaging in weight loss behaviours. This indicates that internalisation was strongest when appearance-ideals were promoted by alike peers. Due to the fact that much pro-ana TikTok content is created by young women, Mueller et al’s. [ 52 ] finding has problematic implications for the young female users of TikTok, in that harmful diet-related messages could be internalised to a greater extent on TikTok than on other platforms and potentially lead to body image disturbances, disordered eating behaviour, and other negative outcomes among young women.

General discussion

The findings of the current study are important but must also be understood within the broader context of participant’s daily lives beyond their participation in this study. Everyday female-identifying individuals are exposed to a multitude of different sources of information from which body image related stimuli can be drawn. The present study’s experiment was not conducted in a controlled environment due to its online nature, therefore researchers did not have the ability to assess and control for other pieces of body image-related information that participants might have consumed prior to participation that may have been salient for their body image. Further research is required to identify how sustained a change in body image states as measured by the BISS may be over time.

The findings of this study provide some insights into how social media influences disordered eating behaviour and mental health; a theoretical gap in the literature that Choukas-Bradley et al. [ 6 ] highlight as holding back research in this domain. In particular, the findings of the current study indicate that short periods of exposure to disordered TikTok content have an effect, while the high-range EAT-26 scores observed for those who engaged with TikTok for two or more hours a day also raise questions about the duration of exposure. Nonetheless, our findings demonstrate that short exposure periods are sufficient to have a negative effect on body image and internalisation of the thin ideal.

One point that may be readily overlooked in developing a theoretical framework around social media’s influence is that the narrative arc of TikTok videos is such that users are exposed to many short stories in quick succession, which may have a different effect to longer form content from a single content creator. As Pierce [ 2 ] notes, the speed of exposure to overlapping, but separate narratives depicted in successive videos, is an important feature of TikTok content and may contribute to the influence of such platforms on disordered eating and body attitudes. Each piece of content serves as a standalone narrative but may also overlap and interact with the viewer’s experience of the next video they watch to build a cumulative, normalised narrative of disordered body- and eating-practices.

In the current study, participants who engaged with TikTok for two-three hours a day were classified as high users, and those who used TikTok for three or more hours were classed as extreme. These rates of usage may, however, be quite normative, with Santarossa and Woodruff [ 73 ] citing three-four hours a day on social media as normative for their sample of young adults, though notably participants in the current study were only questioned about their TikTok usage, not their general use of social media.

While we examined the effect of pro-ana content in this study, that some changes were observed in the control group as well as the experimental group indicates that the social media environment, characterised as it is by idealisation, instant feedback, and readily available social comparison [ 6 ], may play a general role in diminishing positive body image attitudes and healthy aspirations. This is supported by Tiggemann and Slater’s [ 35 , 36 ] research in which social media usage was found to correlate positively with higher levels of body image concerns, in contrast to time spent on the internet more generally, and this may be particularly true for visually oriented platforms that sensitize viewers to their own appearance and that of others. As noted previously, of the visually-oriented social media platforms, predominantly TikTok and Instagram, videos are commonly framed on TikTok so that the subject’s whole body is visible, particularly in dance videos and in #GymTok content, where on Instagram, cameo style head-shot videos appear more likely to feature, which further suggests that TikTok may provide more body-related stimuli than other platforms, even when the intention of the content does not relate to body-image or #fitspiration.

Importantly, the algorithm on TikTok functions in such a way that those who actively seek out body positivity content may also be exposed to nefarious body-related content such as body checking, a competitive, self-surveillance type of content where users are encouraged to test out their weight by attempting to drink from a glass of water while their arm encircles another’s waist. As McGuigan [ 74 ] reports, watching just one body checking video may result in hundreds more filtering through a user’s For You page, with those actively attempting to seek out positive body image content likely to be inadvertently exposed to disordered content due to the configuration of the algorithm. This function of the For You page is demonstrated in the current study, with 64% of participants reporting having seen disordered eating content on their For You page, higher than any other kind of harmful content, including suicide and bullying. The current study did not assess participants’ consumption of #FoodTok, #GymTok, and #Fitspiration. Engagement with these dimensions of TikTok and the type of content that participants seek out via the search function warrant consideration in future research.

The TikTok algorithm underscores Logrieco et al’s. [ 18 ] findings that even anti-anorexia content can be problematic, especially given complexities in determining and controlling what is performatively problematic, including videos discussing recovery and positive body attitudes that may somewhat paradoxically further body policing and competition among users and consumers of social media content. Furthermore, as Logrieco et al. [ 18 ] highlight, TikTok is replete in both pro-ana and much more implicit body-related content that may be harmful to viewers, not to mention those creating the content, whose experiences also warrant consideration.

Theoretical and practical implications

The present study bridged an important gap in the literature by utilising both experimental and cross-sectional designs to examine the influence of pro-ana TikTok content on users’ body image satisfaction, internalisation of body ideals, and disordered eating behaviours. While the negative impact of social media on body image and eating behaviours has been established in relation to platforms such as Instagram and Twitter, TikTok’s rapid emergence and unique algorithm warrant independent analysis.

The present findings have important theoretical implications for the understanding of sociocultural influences of orthorexia nervosa development. Notably, this study is one of the first to highlight the association between orthorexia nervosa and the tripartite model of disordered eating using an experimental design. The results illustrate that the internalisation of sociocultural appearance ideals predicts the development of ‘healthy’ disordered eating, as suggested by the tripartite theory. Western culture ideals do seem to influence the expression of orthorexic tendencies, thus caution should be exercised by women when interacting with appearance-related TikTok content.

Unlike explicit pro-ana content, which is open to condemnation, the moral and health-related discourses underpinning much body-related content in which thinness and health are espoused as goodness, reflects a new trend in diet culture masquerading as wellness culture [ 20 , 21 ]. Questions are raised around the ethics of social media algorithms when the technologically fostered link between recovery-focused content and disordered-content on TikTok is laid bare, particularly considering that extant research has found individuals with experience of eating disorders often seek out support, safety, and connection online [ 49 ] and in doing so on a platform like TikTok, may be exposed to more disordered eating content than the average user. Given visual social media platforms are associated with higher levels of dysfunction in relation to body image [ 4 ], the policy and ethics of such platforms warrant scrutiny from a variety of stakeholders in management, marketing, technology regulation, with psychology playing an important role in the marketing of these platforms. As traditional journalistic platforms have been subjected to scrutiny and reform, so too must a climate of accountability be established within the social media nexus.

The widespread growth of social media may warrant greater concern than traditional forms of mass media, not only because of the full-time accessibility and diverse range of platforms, but also due to the prevalence of peer-to-peer interactions. According to the social comparison theory, comparison of oneself to others has traditionally considered more removed, higher status influences (e.g., celebrities, actors/actresses, supermodels) as a greater source of pressure than those in the individuals’ natural environment (e.g., family and peers). Re-examination of this theoretical perspective is warranted considering the contemporary challenges of social media and the perpetuation of body image messages from alike peers. Furthermore, a diverse range of “content” may trigger disordered body- and eating-related attitudes, including #fitspiration and #GymTok, which poses challenges for social media platforms in regulating content. The inclusion of orthorexia in the milieu highlights the disordered nature of seemingly benign health practices and social media content.

That TikTok content containing explicit and implicit pro-ana themes may readily remain on the app uncensored exemplifies the importance of protective strategies to build resilience at the individual level. One such protective strategy is shifting focus from body appearance to functionality. Alleva and colleagues [ 71 ] investigated the Expand Your Horizon programme, designed to improve body image by training women to focus on body functionality. They report that women who engaged with the Expand Your Horizon programme experienced greater satisfaction with body image and functionality, body appreciation, and reduced self-objectification compared to women who did not engage with the program. Health professionals involved in the care of women with eating disorders and other mental health issues should also be educated to ensure they are knowledgeable about the social media content their clients may be exposed to, equipping them with skills to engage in conversations about the potential detrimental impacts of viewing pro-ana and other harmful TikTok content [ 53 ].

The administration of such programs in schools, universities, community groups, and clinical settings could prove effectual in the prevention of disordered eating and body image disturbance development and may reduce symptom severity of a pre-established disorder. Such programs must be developed with great care, however, given the propensity for even anti-anorexia content to have a negative effect on those consuming it [ 18 ]. The development of self-compassion may also build resilience in women, with research confirming that self-compassion can be effectively taught [ 75 ]. Subsequently, programs have been developed such as Compassion Focused Therapy (CFT) in which clients are trained to develop more compassionate self-talk during negative thought processes and to foster more constructive thought patterns [ 76 ]. The value of CFT has been established in the literature with both clinical and non-clinical samples and has promising outcomes particularly for those high in self-criticism [ 77 ].

Young women should be provided with media literacy tools that can assist in advancing critical evaluations of the online world. Digital manipulation of advertising and celebrity images is well known to many people, however, this awareness may be lacking regarding social media images, as they are generally disseminated within one’s peer network rather than outside of it [ 33 ]. Media literacy interventions may educate women about how social media perpetuates appearance-ideals that are often unrealistic and unattainable [ 53 ]. As an example, Posavac et al. [ 78 ] revealed that a single media literacy intervention resulted in a reduction in women’s social comparison to body ideals portrayed in the media.

Such interventions might be extended to female-identifying TikTok users to educate them on the manipulation of videos to produce idealised portrayals of the self. Media literacy should be commenced from an early age by teaching children, adolescents, and adults to understand the influence of implicit messages conveyed through social media and to create media content that is responsible and psychologically safe for others [ 79 ]. Increased understanding of messages portrayed by social media content may prevent thin-ideal endorsement and internet misuse. Notably, however, the most effective approach would be to address the problem at its source and increase the regulation of social media companies, rather than upskill users in how to respond to harmful online environments, which creates further labour for the individual while allowing organisations to continue to produce harmful but easily monetizable content.

Limitations and future directions

To meet the requirements to run multivariate analyses, the continuous data of body image and internalisation scores were dichotomised using a median split to create ‘low’ and ‘high’ groups for each variable. Although dichotomisation was necessary to perform appropriate analyses and power analyses deemed the sample size as adequate following performance of the median split, dichotomising these variables may have contributed to a loss of statistical power to detect true effects.

Limitations are implicated in the use of the ORTO-15 in the present study. The ORTO-15 does not account for different lifestyle factors that may alter a participants’ response, such as dietary restrictions, food intolerances, or medical dietary guidelines. The discrepancies in literature surrounding the psychometric properties of the ORTO-15 may be attributable to the lack of established diagnostic criteria of orthorexia nervosa, cultural differences in expressions of eating disorders, and difficulty comparing research results in determining orthorexia nervosa diagnoses due to inconsistencies in testing questions and cut-off values [ 61 ]. Due to unacceptable reliability in the present study, a factor analysis was performed which identified a factor relating to health food preoccupation. This identified factor was used as the ORTO-15 measure and data from these 5-items were used in analyses and referred to throughout the present study as ‘healthy’ disordered eating. Using the 5-items related to ‘healthy’ disordered eating rather than the complete 15-item scale may not have accurately assessed participants’ degree of orthorexic tendencies. Despite these limitations, the ORTO-15 is the only accepted measure of orthorexic tendencies available [ 63 ]. Additionally, more limitations would likely have been encountered by using the full 15-item measure lacking reliability, compared to utilising the 5-item factor with acceptable reliability.

Future studies of TikTok and disordered eating behaviour should incorporate a measure of social comparison to verify whether social comparison is the vehicle through which women experience decreased body image satisfaction after viewing TikTok content. Future research should also examine the influence of TikTok content creation on body image, internalisation of thinness, and disordered eating behaviour and explore the association between what individuals consume on TikTok and the social media content that they produce. This research should be conducted using more diverse samples of women, including transgender women, to determine whether the findings of the present study are relevant for this population given the unique challenges regarding body image and societal beauty standards that they may experience.

Longitudinal studies are also warranted to examine the effect of exposure to pro-ana TikTok content over time, and to assess the effects of pro-ana TikTok content on body image satisfaction and eating disorder symptomology over time. Further research on orthorexia nervosa is needed to establish a more reliable measure of orthorexic tendencies and this would enable future investigation of the impact of pro-ana TikTok content on the development of orthorexia nervosa, as well as individual differences as predisposing factors in the development of orthorexic tendencies. Finally, future research should examine the efficacy of media literacy and self-compassion intervention programs as a protective factor specifically in the TikTok context, where disordered eating messages are more explicit in nature than traditional media and other social media platforms.

The findings of the current study support the notion that pro-ana TikTok content decreases body image satisfaction and increases internalisation of societal beauty standards in young women. This research is timely given reliance on social media for social interaction, particularly for young adults. Our findings indicate that female-identifying TikTok users may experience psychological harm even when explicit pro-ana content is not sought out and even when their TikTok use is time-limited in nature. The findings of this study suggest cultural and organisation change is needed. There is a need for more stringent controls and regulations from TikTok in relation to pro-ana content as well as more subtle forms of disordered eating- and body-related content. Prohibiting or restricting access to pro-ana content on TikTok may reduce the development of disordered eating and the longevity and severity of established eating disorder symptomatology among young women in the TikTok community. There are current steps being taken to delete dangerous content, including blocking searches such as “#anorexia”, however, there are various ways users circumvent these controls and further regulation is required. Unless effective controls are implemented within the platform to prevent the circulation of pro-ana content, female-identifying TikTok users may continue to experience immediate detrimental consequences for body image satisfaction, thin-ideal internalisation, and may experience an increased risk of developing disordered eating behaviours.

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Tailored market research approaches mean better audience understanding.

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Founder and CEO of market research consultancy, Alter Agents ; believer that powerful insights can change businesses.

As a market researcher, I am a student of human behavior. Over the past few years, my coursework has accelerated; keeping up with an ever-changing consumer means going far beyond traditional research approaches. Our clients need more than a shiny, PR-worthy statistic; they need to deeply understand where their target audiences are spending time, when they are most engaged and how to best reach them. This requires a tailored approach to research projects, where many layers and data points can come together to build the picture a brand needs to make better decisions.

Tailored Research Approaches In Action: Two Examples

Two recent projects we completed with clients illustrated the need for tailored research approaches perfectly. While very different from one another, the projects each required a multimodal approach to deliver actionable audience insights.

Diving Deep To Go Beyond Surface Vanity Metrics

We worked with our long-time client Audacy to evaluate consumer sentiment surrounding podcasts. As podcasts continue to rise in popularity, with 42% of Americans over age 12 listening monthly in 2023 (versus only 12% in 2013), according to Edison Research, it is important to understand this segment of the media landscape. However, brands have been cautious about publishing content and advertising in this medium due to its potentially controversial content.

To find out exactly how consumers feel about brand content in the podcast environment, we knew it wasn’t enough to simply send out a questionnaire to the general population. The study instead consisted of interviews with experts in the industry, followed by a representative 6,000-person survey of current listeners and other tasks to obtain data surrounding individuals’ deeper, subconscious perceptions. This study design is illustrative of how we must approach projects to obtain insights that go beyond surface vanity metrics.

A 10,000-Person Study Embedded With In-Context Testing

In a different study, we worked with Snap to find out just how engaged and receptive individuals were while using various social media platforms, including Snapchat. With all the controversy and discontent being poured through social media today, and its propensity to inflame social polarization, it can be hard for brands to find a positive place to connect with audiences inside the social ecosystem. Our previous neuroscience research with Snap clearly showed that people feel more joy, friendship and connection with Snapchat than with other social media platforms.

With the newest study, we wanted to find out how the positive emotions associated with using Snapchat to connect with family and friends impacted how people feel about brands. We explored this by conducting a 10,000-person study across eight countries. In the survey, we embedded in-context testing to collect data about the respondents’ emotions while using various social media platforms. In this way, we were able to test how people’s emotional states affected brand reception and outcomes.

Tailoring Your Research Approach

Adopting a multimodal approach in research projects like these proves invaluable for gaining a nuanced understanding of audience behavior, and places us far beyond the limitations of a single, traditional research method. In your own approach to research, follow some of these basic steps to gain a more holistic understanding:

• Complement the “what” with the “why.” Surveys, or quantitative research, generally provide data about things like what your customers might be doing, how satisfied they are and perhaps what they plan to do in the future. Qualitative approaches, like focus groups and other newer innovative methodologies, can provide the reasons behind the consumer’s actions, and how they feel about things—called sentiment.

• Don’t use a one-size-fits-all approach. Just because a certain mix of methodologies worked for one project doesn’t mean it will work for the next one. Take the time upfront to deeply understand the business objective behind the research project. Lay the groundwork by familiarizing yourself with existing data, such as that uncovered in previous studies, and asking stakeholders what their goals are. Then, choose the methods you will use based on this foundation of knowledge—whether it is neuroscience, behavioral data, trends, qualitative, quantitative, analytics, biometrics or another approach.

• Remember that your audience is complex. No matter the category, no matter the audience, the consumer landscape is becoming more and more complicated. And your research needs to reflect that level of complexity. A simple one-and-done survey can only get you so far, providing a one-dimensional view of topics, people, actions and feelings that are decidedly not that simple. Every decision is made within a web of circumstances, context and much more—you need to understand it all to confidently take action.

A customized, layered approach to market research can help brands continue to find ways to build audience understanding, make well-informed decisions and create meaningful brand connections. Approach research with the mindset of a student—there is always new information to uncover, new data to explore and new discoveries about human behavior just around the corner.

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• Published: 07 August 2024

Molecular mimicry in multisystem inflammatory syndrome in children

• Aaron Bodansky   ORCID: orcid.org/0000-0001-8943-8233 1   na1 ,
• Robert C. Mettelman   ORCID: orcid.org/0000-0002-3527-5616 2   na1 ,
• Joseph J. Sabatino Jr 3 , 4 ,
• Sara E. Vazquez 5 ,
• Janet Chou 6 , 7 ,
• Tanya Novak   ORCID: orcid.org/0000-0002-7115-7545 8 , 9 ,
• Kristin L. Moffitt 7 , 10 ,
• Haleigh S. Miller 5 , 11 ,
• Andrew F. Kung 5 , 11 ,
• Elze Rackaityte   ORCID: orcid.org/0000-0003-3889-8082 5 ,
• Colin R. Zamecnik   ORCID: orcid.org/0000-0002-9477-1388 3 , 4 ,
• Jayant V. Rajan 5 ,
• Hannah Kortbawi 5 , 12 ,
• Caleigh Mandel-Brehm 5 ,
• Anthea Mitchell 13 ,
• Chung-Yu Wang 13 ,
• Aditi Saxena 13 ,
• Kelsey Zorn   ORCID: orcid.org/0000-0003-1227-2137 5 ,
• David J. L. Yu 14 ,
• Mikhail V. Pogorelyy 2 ,
• Walid Awad 2 ,
• Allison M. Kirk   ORCID: orcid.org/0000-0002-4286-3678 2 ,
• James Asaki 15 ,
• John V. Pluvinage   ORCID: orcid.org/0000-0002-9607-2783 4 ,
• Michael R. Wilson   ORCID: orcid.org/0000-0002-8705-5084 3 , 4 ,
• Laura D. Zambrano 16 ,
• Angela P. Campbell   ORCID: orcid.org/0000-0002-2576-482X 16 ,
• Overcoming COVID-19 Network Investigators ,
• Paul G. Thomas   ORCID: orcid.org/0000-0001-7955-0256 2   na2 ,
• Adrienne G. Randolph 7 , 8 , 9   na2 ,
• Mark S. Anderson   ORCID: orcid.org/0000-0002-3093-4758 14 , 17   na2 &
• Joseph L. DeRisi   ORCID: orcid.org/0000-0002-4611-9205 5 , 13   na2  

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• Autoimmunity
• Autoinflammatory syndrome
• Immune tolerance
• Inflammation
• Viral infection

Multisystem inflammatory syndrome in children (MIS-C) is a severe, post-infectious sequela of SARS-CoV-2 infection 1 , 2 , yet the pathophysiological mechanism connecting the infection to the broad inflammatory syndrome remains unknown. Here we leveraged a large set of samples from patients with MIS-C to identify a distinct set of host proteins targeted by patient autoantibodies including a particular autoreactive epitope within SNX8, a protein involved in regulating an antiviral pathway associated with MIS-C pathogenesis. In parallel, we also probed antibody responses from patients with MIS-C to the complete SARS-CoV-2 proteome and found enriched reactivity against a distinct domain of the SARS-CoV-2 nucleocapsid protein. The immunogenic regions of the viral nucleocapsid and host SNX8 proteins bear remarkable sequence similarity. Consequently, we found that many children with anti-SNX8 autoantibodies also have cross-reactive T cells engaging both the SNX8 and the SARS-CoV-2 nucleocapsid protein epitopes. Together, these findings suggest that patients with MIS-C develop a characteristic immune response to the SARS-CoV-2 nucleocapsid protein that is associated with cross-reactivity to the self-protein SNX8, demonstrating a mechanistic link between the infection and the inflammatory syndrome, with implications for better understanding a range of post-infectious autoinflammatory diseases.

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Immunology of SARS-CoV-2 infection in children

Children with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections typically have mild disease 3 , 4 , but can develop a rare life-threatening post-infectious complication known as MIS-C 1 , 2 . MIS-C presents with a distinctive inflammatory signature indicative of altered innate immune responses 5 , 6 , including dysregulation of the mitochondrial antiviral signalling (MAVS) protein pathway 7 . Aberrant adaptive immunity is also involved, with multiple MIS-C-associated autoantibodies reported 8 , 9 , 10 , 11 , 12 . Furthermore, T cell signatures have also been associated with development of MIS-C 13 , 14 , 15 , 16 , which are accompanied by autoimmune-associated B cell expansions 8 . Some autoimmune diseases have been shown to involve tandem cross-reactive B cell and T cell responses. In multiple sclerosis, for example, cross-reactive B cells and T cells have been shown to respond to Epstein–Barr virus protein (EBNA1) and antigens in the human nervous system 17 , 18 , 19 . Decades of research into paraneoplastic autoimmune encephalitis has also demonstrated that autoreactive B cells and T cells can cause disease through coordinated targeting of a shared intracellular antigen and, in certain cases, a shared epitope 20 , 21 , 22 , 23 , 24 , 25 , 26 . Despite intense interest, a pathophysiological link between SARS-CoV-2 and MIS-C remains enigmatic, and identification of disease-specific autoantigens remains incompletely explored. Here children previously infected with SARS-CoV-2 with ( n  = 199) and without ( n  = 45) MIS-C were enrolled and comprehensively evaluated for differential autoreactivity to the entire human and SARS-CoV-2 proteome. Patients with MIS-C were found to have both cross-reactive antibodies and T cells targeting an epitope motif shared by the viral nucleocapsid protein and human SNX8, a protein involved in MAVS antiviral function 27 . These findings suggest that many cases of MIS-C may be triggered by molecular mimicry and could provide a framework for identifying potential cross-reactive epitopes in other autoimmune and inflammatory diseases with predicted viral triggers such as Kawasaki disease 28 , type 1 diabetes mellitus (T1DM) 29 and multiple sclerosis.

Patients with MIS-C have a distinct set of autoreactivities

To explore the hypothesis that MIS-C is driven by an autoreactive process, we evaluated the proteome-wide autoantibody profiles of children with MIS-C ( n  = 199) and children convalescing following asymptomatic or mild SARS-CoV-2 infection without MIS-C ( n  = 45, hereafter referred to as ‘at-risk controls’) using our custom phage immunoprecipitation and sequencing (PhIP-seq) 30 library, which has previously been used to define novel autoimmune syndromes and markers of disease for various conditions 12 , 24 , 25 , 31 , 32 , 33 . Given the inherently heterogeneous nature of antibody repertoires among individuals 34 , the identification of disease-associated autoreactive antigens requires the use of large numbers of cases and controls 12 . To minimize spurious hits, this study includes substantially more patients with MIS-C and controls than similar, previously published studies 8 , 9 , 10 , 12 (Fig. 1a ). Clinical characteristics of this cohort are described in Extended Data Table 1 .

a , Design of the PhIP-seq experiment comparing patients with MIS-C ( n  = 199) and at-risk controls ( n  = 45; children with SARS-CoV-2 infection at least 5 weeks before sample collection without symptoms of MIS-C). Schematics in panel a were created using BioRender ( https://www.biorender.com ). b , Venn diagram highlighting the number of autoantigens identified with statistically significant PhIP-seq enrichment (‘enrichment set’: grey circle; P  < 0.01 on one-sided Kolmogorov–Smirnov test with false discovery rate correction) and autoantigens identified, which contribute to a logistic regression classifier of MIS-C relative to at-risk controls (‘classifier set’: purple circle). There are 35 autoantigens present in both the classifier set and the enrichment set (pink; union of the Venn diagram) of which 30 are exclusive to MIS-C and referred to as the ‘MIS-C set’ (no two controls have low reactivity as defined by the fold-change (FC) signal over the mean of protein A/G beads only (FC > mock-IP) of 3 or greater, and no single control has high reactivity defined as FC > mock-IP greater than 10). LR, logistic regression. c , Receiver operating characteristic curve for the logistic regression classifier showing upper and lower bounds of performance through 1,000 iterations. d , Bar plots with error bars showing logistic regression coefficients for the top 10 autoantigens across 1,000 iterations. The whiskers extend to 1.5 times the interquartile range (IQR) from the quartiles. The boxes represent the IQR, and the centre lines represent the median. e , Hierarchically clustered (Pearson) heatmap showing the PhIP-seq enrichment (FC > mock-IP) for the 30 autoantigens in the MIS-C set in each patient with MIS-C and each at-risk plasma control.

Source Data

For a given set of samples, PhIP-seq can yield dozens to thousands of differential enrichments of phage-displayed peptides. Here logistic regression machine learning was used as an initial unbiased measure of how accurately a set of differentially enriched peptides could classify people with MIS-C and controls—an approach that has been used to classify people with autoimmune polyglandular syndrome type 1 using PhIP-seq data 12 . In all, 107 proteins had logistic regression coefficients greater than zero (‘classifier set’; Fig. 1b ). As this is an unbalanced dataset with a random accuracy less than 50%, we also generated a receiver operating characteristic (ROC) curve. ROC analysis iterated 1,000 times and yielded an average area under the curve (AUC) of 0.94 (Fig. 1c ). Examination of the logistic regression coefficients associated with MIS-C revealed the largest contributions from peptides derived from the ETS repressor factor-like (ERFL), sorting nexin 8 (SNX8) and KDEL endoplasmic reticulum protein retention receptor 1 (KDELR1) coding sequences (Fig. 1d ).

In parallel, a Kolmogorov–Smirnov test was used to define a set of 661 autoreactivities statistically enriched after false discovery rate adjustment for multiple comparisons ( q  < 0.01; ‘enrichment set’). To avoid false positives, the intersection of the classifier set and enrichment set were considered further. Of these 35 hits, peptides derived from 30 different proteins satisfied an additional set of conservative criteria, requiring that none was enriched (fold change over mock-immunoprecipitation (IP) of more than 3) in more than a single control, or was enriched more than 10-fold in any control (‘MIS-C set’; Fig. 1e ).

Previously reported MIS-C autoantibodies

To date, at least 34 autoantigen candidates have been reported to associate with MIS-C 8 , 9 , 10 , 12 . However, we found that only UBE3A (a ubiquitously expressed ubiquitin protein ligase) was differentially enriched in our MIS-C dataset, whereas the remaining 33 were present in a similar proportion of cases with MIS-C and at-risk controls (Extended Data Fig. 1a ). Autoreactivity to UBE3A was independently identified in this study as part of both the classifier and the enrichment sets, but was not included in the final MIS-C set due to the low positive signal present in two controls.

In addition, autoantibodies to the receptor antagonist IL-1RA have been previously reported in 13 of 21 (62%) patients with MIS-C 11 . In this cohort, anti-IL-1RA antibodies were detected by PhIP-seq ( z score > 6 over at-risk control) in six patient samples. To further examine immune reactivity to full-length IL-1RA, sera from 196 of the 199 patients in this study were used to immunoprecipitate [35S]-methionine-radiolabelled IL-1RA (radioligand-binding assay (RLBA)). Positive immunoprecipitation of IL-1RA (defined as more than 3 s.d. above mean of controls) was found in 39 of 196 (19.9%) patients with MIS-C. However, many patients with MIS-C were treated with intravenous immunoglobulin (IVIG), a blood product shown to contain autoantibodies 35 . After removing samples from patients treated with IVIG (61 remaining), the difference between samples from patients with MIS-C (5 of 61, 8.2%) and at-risk controls (1 of 45, 2.2%) was not significant ( P  = 0.299; Extended Data Fig. 1b ).

MIS-C autoantigens lack tissue-specific associations with clinical phenotypes

Consistent with previous MIS-C reports 1 , 5 , this cohort was clinically heterogeneous (Extended Data Table 2 ). To determine whether specific phenotypes, including myocarditis and the requirement of vasopressors, might be associated with specific autoantigens present in the MIS-C set, tissue expression levels were assigned to each autoantigen 36 (Human Protein Atlas; https://proteinatlas.org ), including the amount of expression in cardiomyocytes and the cardiac endothelium. The PhIP-seq signal for patients with MIS-C with a particular phenotype was compared with those patients with MIS-C without the phenotype. Autoantigens with tissue specificity were not enriched in those patients with MIS-C with phenotypes involving said tissue. Similarly, autoantigens associated with myocarditis or vasopressor requirements did not correlate with increased cardiac expression (Extended Data Fig. 1c ).

Orthogonal validation of PhIP-seq autoantigens

Peptides derived from ERFL, SNX8 and KDELR1 carried the largest logistic regression coefficients in the MIS-C classifier. The PhIP-seq results were orthogonally confirmed by RLBAs using full-length ERFL, SNX8 and KDELR1 proteins. Relative to at-risk controls, samples from patients with MIS-C significantly enriched each of the three target proteins ( P  < 1 × 10 −10 for ERFL, SNX8 and KDELR1), consistent with the PhIP-seq assay (Extended Data Fig. 2a ). Using only the RLBA data for these three proteins, MIS-C could be confidently classified (ROC with fivefold cross-validation; 1,000 iterations) from at-risk control sera with an AUC of 0.93, suggesting the potential for molecular diagnostic purposes (Extended Data Fig. 2b ).

As noted, IVIG was administered to 138 of the 199 patients with MIS-C before sample collection and was absent from all 45 at-risk controls. The autoreactivity to the ERFL, SNX8 and KDELR1 proteins from the 61 patients with MIS-C who had not been treated with IVIG before sample collection were compared with the at-risk controls. In contrast to IL-1RA, the differential enrichment of these three proteins remained significant ( P  = 6.69 × 10 −10 , P  = 6.26 × 10 −5 and P  = 0.0001, respectively), suggesting that autoreactivity to ERFL, SNX8 and KDELR1 proteins was not confounded by IVIG treatment (Extended Data Fig. 2c ).

Independent MIS-C cohort validation

To further test the validity of these findings, an independent validation cohort consisting of samples from 24 different patients with MIS-C and 29 children with severe acute COVID-19 was evaluated (acquired via ongoing enrolment of the Overcoming COVID-19 study; Extended Data Table 3 ). Using RLBAs with full-length ERFL, SNX8 and KDELR1 proteins, we found that all three target proteins were significantly enriched compared with both the at-risk controls ( P  = 0.00022, P  = 3.68 × 10 −5 and P  = 2.36 × 10 −5 , respectively) and the patients with severe acute COVID-19 ( P  = 0.0066, P  = 0.00735 and P  = 0.00114, respectively; Extended Data Fig. 2d ). A logistic regression model, trained on the original cohort, classified MIS-C from at-risk controls with an AUC of 0.84, and from severe acute paediatric COVID-19 with an AUC of 0.78 (Extended Data Fig. 2e ). This suggests that autoreactivity to ERFL, SNX8 and KDELR1 is a significant feature of MIS-C that is separable from SARS-CoV-2 exposure and severe acute paediatric COVID-19.

MIS-C autoantibodies target a single epitope within the SNX8 protein

SNX8 is a protein that is 456 amino acids and belongs to a family of sorting nexins involved in endocytosis, endosomal sorting and signalling 37 . Publicly available expression data 36 (Human Protein Atlas) show that SNX8 is widely expressed across various tissues including the brain, heart, gastrointestinal tract, kidneys and skin, with the highest expression in undifferentiated cells and immune cells. Previous work has associated SNX8 with host defence against RNA viruses 27 . ERFL is a poorly characterized 354-amino acid protein. A survey of single-cell RNA sequencing (scRNA-seq) data 36 (Human Protein Atlas) suggests enrichment in plasma cells, B cells and T cells in some tissues. Using a Spearman correlation in principal component analysis (PCA) space based on tissue RNA-seq data 36 (Human Protein Atlas), SNX8 has the second closest expression pattern to ERFL compared with all other coding genes, with a correlation coefficient of 0.81. KDELR1 is a 212-amino acid endoplasmic reticulum–Golgi transport protein essential to lymphocyte development with low tissue expression specificity. All three proteins are predicted to be intracellular, suggesting that putative autoantibodies targeting these proteins are unlikely to be sufficient for disease pathology on their own. However, autoantibodies targeting intracellular antigens are often accompanied by autoreactive T cells specific for the protein from which that antigen was derived, and which targets cell types expressing the protein 22 , 25 , 26 , 38 . We selected SNX8 for further investigation, given its enrichment in immune cells and its putative role in regulating the MAVS pathway in response to RNA virus infection, a pathway implicated in MIS-C pathology 7 .

Full-length SNX8 is represented in this PhIP-seq library by 19 overlapping 49-mer peptides. For all but one patient sample, the peptide fragment spanning amino acid positions 25–73 was the most enriched in the PhIP-seq assay (Fig. 2a ), suggesting a common autoreactive site. A sequential alanine scan was performed to determine the minimal immunoreactive peptide sequence (Fig. 2b ; Methods ). Using samples from six individuals with MIS-C, we determined that the critical region for immunoreactivity was a nonamer spanning positions 51–59 (PSRMQMPQG). Using the wild-type 49-amino acid peptide and the version with the critical region mutated to alanine, 182 of the 199 patients with MIS-C (insufficient sample for the remaining 17) and all 45 controls were assessed for immunoreactivity using a split-luciferase-binding assay (SLBA). We found that samples from 31 of 182 (17.0%) patients with MIS-C immunoprecipitated the wild-type fragment. Of these, 29 (93.5%) failed to immunoprecipitate the mutated peptide, suggesting a common shared autoreactive epitope among nearly all of the patients with MIS-C with anti-SNX8 antibodies (Extended Data Fig. 2f ).

a , PhIP-seq signal (reads per 100,000) for each patient with MIS-C ( n  = 199) and each at-risk control ( n  = 45) across each of the 19 bacteriophage-encoded peptide fragments, which together tile the full-length SNX8 protein. b , SLBA enrichments (normalized antibody indices) for each sequential alanine mutagenesis construct. Constructs were designed with 10 amino acid alanine windows (highlighted in purple) shifted by 5 amino acids until the entire immunodominant SNX8 region (SNX8 fragment 2) was scanned. Values are averages of six separate patients with MIS-C. The identified autoantibody epitope is bounded by vertical grey dotted lines.

Patients with MIS-C have an altered antibody response to the SARS-CoV-2 nucleocapsid protein

To evaluate whether differences exist in the humoral immune response to SARS-CoV-2 infection in patients with MIS-C relative to at-risk controls, we repeated PhIP-seq with 181 of the original 199 patients with MIS-C and all 45 of the at-risk controls using a previously validated library specific for SARS-CoV-2 (ref. 39 ). To discover whether certain fragments were differentially enriched in either patients with MIS-C or at-risk controls, the enrichment of each phage encoded SARS-CoV-2 peptide (38 amino acids each) across all patients with MIS-C and at-risk controls was normalized to 48 healthy controls pre-COVID-19. Three nearly adjacent peptides derived from the SARS-CoV-2 nucleocapsid protein (fragments 5, 8 and 9) were significantly enriched (Kolmogorov–Smirnov test P  < 0.0001 for each). The first peptide (fragment 5), spanning amino acids 77–114, was significantly enriched in the at-risk controls (representing the typical serological response in children), whereas the next two fragments (fragments 8 and 9), spanning amino acids 134–190, were significantly enriched in patients with MIS-C (Fig. 3a,b ). The most differentially reactive region of the SARS-CoV-2 nucleocapsid protein in patients with MIS-C (fragment 8) was termed the MIS-C-associated domain of SARS-CoV-2 (MADS). The PhIP-seq results were orthogonally confirmed using an SLBA measuring the amount of MADS peptide immunoprecipitated with samples from 16 individuals, including 11 patients with MIS-C and 5 at-risk controls (Fig. 3c ). To precisely map the minimal immunoreactive region of MADS in MIS-C samples, peptides featuring a sliding window of ten alanine residues were used as the immunoprecipitation substrate for SLBAs, run in parallel with the SNX8 alanine scanning peptides using sera from three patients with MIS-C (Fig. 3d ). The critical regions identified here in both SNX8 and MADS were highly similar, represented by the (ML)Q(ML)PQG motif (Fig. 3e ).

a , Relative PhIP-seq signal (FC over the mean) of 48 controls who are pre-COVID-19 (FC > pre-COVID-19) in patients with MIS-C ( n  = 181) and at-risk controls ( n  = 45) using a custom phage display library expressing the entire SARS-CoV-2 proteome to different regions of SARS-CoV-2. Only regions with a mean antibody signal of more than 1.5-fold above pre-COVID-19 controls are shown. Antigenicity (sum of the mean FC > pre-COVID-19 in MIS-C and at-risk controls) are represented by darker shades. The length of the bars represents the statistical difference in signal between MIS-C and at-risk controls to a particular region (−log 10 of two-sided Kolmogorov–Smirnov test P values), with upward deflections representing enrichment in MIS-C versus at-risk controls, and downward deflections representing less signal in MIS-C. The asterisk indicates the differentially reactive region of the nucleocapsid (N) protein. b , Bar plots showing the PhIP-seq signal (FC > pre-COVID-19) across the specific region of the SARS-CoV-2 nucleocapsid protein (fragments 4–9) with the most divergent response in MIS-C samples ( n  = 181) relative to at-risk controls ( n  = 45), compared using a two-sided Kolmogorov–Smirnov test (exact P values are shown in the figure). The amino acid sequence of the region with the highest relative enrichment in MIS-C is highlighted in green and referred to as MADS. c , Strip plots and box plots showing MADS SLBA enrichments (normalized antibody indices) in patients with MIS-C ( n  = 11) relative to at-risk controls ( n  = 5). d , SLBA signal (normalized antibody indices) for full sequential alanine mutagenesis scans within the same three individuals for SNX8 (left) and MADS (right). Each identified epitope is bounded by black vertical dotted lines. e , Multiple sequence alignment of SNX8 and MADS epitopes with the amino acid sequence for the similarity region shown (for the text in colour, biochemically similar is in orange, and identical is in red). For the box plots ( b , c ), the whiskers extend to 1.5 times the IQR from the quartiles. The boxes represent the IQR, and the centre lines represent the median.

Patients with MIS-C have significantly increased SNX8 autoreactive T cells

In other autoimmune diseases, autoantibodies often arise to intracellular targets, yet the final effectors of cellular destruction are autoreactive T cells 22 , 26 , 40 . Given evidence that certain subsets of MIS-C are associated with HLA 16 , and that SNX8 is an intracellular protein, we hypothesized that patients with MIS-C with anti-SNX8 antibodies may, in addition to possessing SNX8 autoreactive B cells, also possess autoreactive T cells targeting SNX8-expressing cells. To test this hypothesis, T cells from nine patients with MIS-C (eight from SNX8 autoantibody-positive patients and one who was SNX8 autoantibody negative) and ten at-risk controls (chosen randomly) were exposed to a pool of 15-mer peptides with 11-amino acid overlaps tiling the full-length human SNX8 protein. T cell activation was measured by an activation-induced marker assay, which quantifies upregulation of three cell activation markers: OX40, CD69 and CD137 (ref. 41 ). The percent of T cells activated in response to SNX8 protein was significantly higher in patients with MIS-C than in controls ( P  = 0.00126). Using a positive cut-off of 3 s.d. above the mean of the controls, 7 of the 9 (78%) patients with MIS-C were positive for SNX8-expressing autoreactive T cells, whereas 0 of 10 (0%) controls met these criteria (Fig. 4a ). With respect to CD4 + and CD8 + subgroups, there was an increased signal in patients with MIS-C compared with controls, which did not meet significance ( P  = 0.0711 and P  = 0.0581, respectively; Extended Data Fig. 3a ). The patient with MIS-C who was seronegative for the SNX8 autoantibody was also negative for SNX8 autoreactive T cells.

a , Strip plots and box plots showing the distribution of T cells activated in response to either vehicle (culture media + 0.2% DMSO) or the SNX8 peptide pool (SNX8 peptide + culture media + 0.2% DMSO) in patients with MIS-C ( n  = 9) and controls ( n  = 10). The relative signal was compared using a two-sided Mann–Whitney U -test (exact P values are shown in the figure). The box plot whiskers extend to 1.5 times the IQR from the quartiles, the boxes represent the IQR, and the centre lines represent the median. The dashed line is 3 s.d. above the mean of the controls in the SNX8 pool condition. b , TCRdist similarity network of 48 unique, paired TCRαβ sequences ( n  = 259 sequences) obtained from four patients with MIS-C. CD8 + T cells were sorted from PBMCs directly ex vivo or after 10 days of peptide expansion and staining with A*02:01 or A*02:06 HLA class I tetramers loaded with MADS (LQLPQGITL) and SNX8 (MQMPQGNPL) peptides. Each node represents a unique TCR clonotype. Edges connect nodes with a TCRdist score of less than 150. The dashed lines surround TCR similarity clusters. The node size corresponds to the T cell clone size. Nodes are coloured based on the HLA experiment type (left) or patient (right). TCRs selected for further testing are numbered TCR 1–8. The convergent node is circled in green. c , Specificity of putative cross-reactive TCRs expressed in Jurkat-76 cells by HLA-A*02:01 or HLA-A*02:06 tetramers loaded with MADS (LQLPQGITL) and SNX8 (MQMPQGNPL) peptides. Jurkat-76 (TCR-null) cells were used as tetramer background staining controls. The gate values indicate the frequency of MADS–APC + and/or SNX8–BV421 + cells as the percentage of the total PE + cells (combination staining with MADS–PE and SNX8–PE tetramers). TCRs with confirmed cross-reactivity are indicated in red. Outliers are shown. Flow plots are representative of two independent evaluations. d , Summary of TCR sequencing results of the eight TCRs tested.

HLA type A*02 is more likely to present the shared epitope

MIS-C has been associated with HLA alleles A*02, B*35 and C*04 (ref. 16 ). The Immune Epitope Database and Analysis Resource ( https://IEDB.org ) 42 was used to rank the HLA class I (HLA-I) peptide presentation likelihoods for both SNX8 and SARS-CoV-2 nucleocapsid protein with respect to the MIS-C-associated HLA alleles. The distribution of predicted HLA-I-binding scores for nucleocapsid protein and SNX8 fragments matching the (ML)Q(ML)PQG SNX8/MADS motif relative to fragments lacking a match was compared. For HLA-A*02, predicted HLA-I binding was significantly higher ( P  = 8.78 × 10 −10 for nucleocapsid protein; P  = 0.0112 for SNX8) for fragments containing the putative autoreactive motif. There was no statistical difference for HLA-B*35 and HLA-C*04 predictions (Extended Data Fig. 3b,c ). Of note, of the seven patients with MIS-C with SNX8 autoreactive T cells, at least five were positive for HLA-A*02 (Extended Data Fig. 3a ). To experimentally validate HLA-I-binding predictions to SNX8 and MADS peptides, we measured peptide–HLA (pHLA) monomer stability using a β2 microglobulin (β2m) fold test, which is a proxy for pHLA-binding affinity in which anti-β2m staining reports on the strength of the pHLA complex 43 . SNX8 (MQMPQGNPL) and MADS (LQLPQGITL) peptides were loaded onto unfolded HLA-A*02:01, HLA-A*02:06 or HLA-B*35:01 monomers and stained with an anti-β2m fluorescent antibody. Consistent with the IEDB rankings, both HLA-A*02 alleles bound SNX8 and MADS peptides, with HLA-A*02:06 exhibiting the highest pHLA complex stability (Extended Data Fig. 3d ).

T cells from patients with MIS-C are cross-reactive to the SNX8 and nucleocapsid protein similarity regions

Given the prediction that HLA types associated with MIS-C preferentially display peptides containing the similarity regions for both SNX8 and the SARS-CoV-2 nucleocapsid protein, we sought to determine whether cross-reactive T cells were present and whether they were associated with MIS-C. We stimulated peripheral blood mononuclear cells (PBMCs) from three patients with MIS-C and three at-risk controls with peptides from either the SNX8 similarity region (MQMPQGNPL) or the MADS similarity region (LQLPQGITL) for 7 days to enrich for CD8 + T cells reactive to these epitopes. We then built differently labelled HLA-I tetramers loaded with either the SNX8 or MADS peptides and measured binding to T cells (Extended Data Fig. 4a ). We detected cross-reactive CD8 + T cells, which bound both peptide epitopes, in all three patients with MIS-C, whereas no cross-reactive CD8 + T cells were observed in at-risk controls (Extended Data Fig. 4b ).

As SNX8-responsive T cells were observed in patients with MIS-C, we next asked whether the region of SNX8 similar to the SARS-CoV-2 MADS region was sufficient to activate patient T cells. A pool of 20 10-mer peptides with 9-amino acid overlaps centred on the target motif from SNX8 (collectively spanning amino acids 44–72) was used to stimulate PBMCs from two patients with MIS-C and four at-risk controls. Both patients with MIS-C had activation of T cells, whereas none of the four controls had T cell activation (Extended Data Fig. 4c ).

Identification of ex vivo cross-reactive T cell receptors

Having determined that patients with MIS-C, but not controls, contained putative SNX8/MADS cross-reactive CD8 + T cells, we next sought to identify T cell receptor (TCR) sequences with specificity for both the SARS-CoV-2 MADS and the host SNX8 epitopes. To do this, PBMCs were obtained during the first 72 h of hospital admission from four study participants with HLA-A*02 and confirmed MIS-C (one individual previously identified as having putative cross-reactive T cells, and three new patients). Given that MIS-C PBMCs represent a scarce resource, we chose to expand one aliquot of PBMCs from each of the four participants (distinct from our previous peptide expansion protocol; see  Methods ) to maximize the chances of isolating putative cross-reactive TCRs. Although the frequency of ex vivo autoantigen-specific CD8 + T cells are extraordinarily low in peripheral blood, even for bona fide T cell-mediated autoimmune diseases such as T1DM 38 and multiple sclerosis 44 , 45 , we nevertheless utilized the remaining PBMCs from each participant for direct ex vivo analysis without previous expansion. To isolate the antigen-specific TCRs, participant cells (both ex vivo and following peptide expansion) were stained using the same tetramer-labelling strategy, which previously identified the putative cross-reactive TCRs (Extended Data Fig. 4a ); any cell exhibiting binding to at least two peptide-loaded tetramers was individually sorted and full-length paired TCRα and TCRβ sequences were determined. This resulted in 259 complete TCR sequences, comprising 30 and 18 unique T cell clones from the ex vivo and peptide expansion experiments, respectively. A complete list of TCR sequences is provided (Fig. 4 source data).

Next, we sought to validate the specificity of putative SNX8/MADS cross-reactive TCRs identified from the tetramer sorting, and further analyse features of the recovered TCRs. Because clusters of similar TCRs tend to recognize similar peptide antigens, a TCR similarity network was constructed from all 259 full-length TCR sequences using a previously established TCR distance metric (TCRdist) 46 , 47 (Fig. 4b and Extended Data Fig. 4d ). In two of the four patients, we identified unique populations of clonally expanded T cells expressing putative cross-reactive TCRs directly ex vivo, whereas each of the four patients had at least one ex vivo putative cross-reactive TCR (Fig. 4b ). To confirm the specificity of the TCRs identified in our tetramer sorting, we selected eight TCR sequences for additional validation and generated individual cell lines that stably expressed one TCR of interest (Extended Data Fig. 5a ). These Jurkat-TCR + cell lines were tetramer stained, and cross-reactivity was confirmed in three of the Jurkat-TCR + cell lines (TCR 1, 7 and 8; Fig. 4c ). Of these validated cross-reactive TCRs, two were obtained from ex vivo PBMCs from patients with MIS-C including TCR 7, which was clonally expanded. The minimum ex vivo frequency of TCR 7 alone was more than 1 in 25,000 (6 of 140,035) circulating CD8 + T cells. The two cross-reactive TCRs obtained from the ex vivo isolation were derived from the same participant, utilize the same TRAV gene ( TRAV1-2 ) with identical CDR3α sequences and clustered with three additional sequences in the TCRdist space, one of which was also clonally expanded, suggesting that this patient had an active expansion of a large cluster of SNX8/MADS cross-reactive CD8 + T cells (Fig. 4d ). Furthermore, we note a cluster of two similar TCRs obtained from ex vivo sampling of different participants (patients 2 and 4) with different HLA types (‘convergent node’; circled in green in Fig. 4b ). Although these putative cross-reactive TCRs were not evaluated further, the cluster suggests that TCR specificities to these epitopes may converge across individuals.

The remaining five Jurkat-TCR + cell lines (TCR 2–6) exhibited single specificity to the MADS tetramer with four of five coming from the peptide expansion. To evaluate possible interference between tetramers, which can arise when pHLA–TCR-binding affinities differ, Jurkat-TCR + cell lines were stained with individual tetramers. The results confirm that four of these TCRs are indeed reactive only to MADS (Extended Data Fig. 5b ). However, TCR 2, although showing strong binding preference to MADS, also bound the individual SNX8 tetramer, suggesting that the higher affinity for MADS may outcompete binding to the SNX8 tetramer in some cases. This observation is in line with the notion that autoreactive cross-reactive TCRs with lower relative affinities to autoantigens may escape thymic negative selection. Finally, because the original tetramer experiments were based on an early 2020 SARS-CoV-2 minor variant sequence (LQLPQGITL), all eight Jurkat-TCR + cell lines were also stained with HLA tetramers loaded with the SARS-CoV-2 Wuhan MADS sequence (LQLPQGTTL). In all cases, the Jurkat-TCR + cells bound the Wuhan MADS tetramer, consistent with the notion that T cells encoding these and other similar TCRs may be capable of responding to multiple SARS-CoV-2 strains (Extended Data Fig. 5c ).

RNA expression profile of SNX8  during SARS-CoV-2 infection

As previously discussed, SNX8 is expressed across multiple tissues, but is highest in immune cells, consistent with its role in defending against RNA viruses via recruitment of MAVS 27 . To further investigate the potential impact of combined B cell and T cell autoimmunity to SNX8 following SARS-CoV-2 infection, we used scRNA-seq to analyse SNX8 expression in PBMCs from patients with severe, mild or asymptomatic SARS-CoV-2 infection or influenza infection and uninfected healthy controls 48 . Following SARS-CoV-2 infection, SNX8 had the highest mean expression in classical and non-classical monocytes and B cells (Extended Data Fig. 6a,b ) and was elevated in individuals infected with SARS-CoV-2 compared with those who were uninfected (Extended Data Fig. 6c ). Within myeloid lineage cells, SNX8 expression correlated with MAVS expression and OAS1 and OAS2 (which encode two known regulators of the MAVS pathway implicated in MIS-C pathogenesis 7 ) expression (Extended Data Fig. 6d ). Conversely, SNX8 expression is inversely correlated to SARS-CoV-2 infection severity. This follows a similar pattern to OAS1 and OAS2 . However, unlike OAS1 , OAS2 and MAVS , SNX8 is preferentially expressed during SARS-CoV-2 infection compared with influenza virus infection (Extended Data Fig. 6e ).

The SARS-CoV-2 pandemic largely spared children from severe disease. One rare but notable exception is MIS-C, an enigmatic and life-threatening syndrome. Previous studies have surfaced numerous associations, but have failed to identify a direct mechanistic link between SARS-CoV-2 and MIS-C. In this study, 199 samples from patients with MIS-C and 45 paediatric at-risk controls were analysed using customized human and SARS-CoV-2 proteome PhIP-seq libraries. Targeted follow-up experiments from these assays ultimately revealed that patients with MIS-C preferentially had antibodies targeting the epitope motif (ML)Q(ML)PQG shared by both the SARS-CoV-2 nucleocapsid protein and the human protein SNX8. Cross-reactive CD8 + T cells targeting both regions were detected in patients with MIS-C, but not in controls, suggesting that these CD8 + T cells may contribute to immune dysregulation through the inappropriate targeting of immune cells expressing SNX8. We found evidence that the (ML)Q(ML)PQG epitope motif elicits both B cell and T cell reactivity; further study of this epitope convergence is warranted.

These findings help to connect several important known aspects of MIS-C pathophysiology and draw parallels to other diseases in which exposure to a new antigen leads to autoimmunity, such as paraneoplastic autoimmune disease or cross-reactive epitopes between Epstein–Barr virus and host proteins in multiple sclerosis 17 , 18 , 19 , 22 , 26 . An expansion of T cells expressing TCRβ variable gene 11-2 ( TRBV11-2 ) has been shown in MIS-C 8 , 15 , 16 ; however, the underlying driver remains unknown. Although we did not observe an overrepresentation of TRBV11-2 in our putative cross-reactive TCR dataset, we did identify two expanded TRBV11-2 + clones ( n  = 6 and n  = 2) sequenced directly from ex vivo samples. Although SNX8 is a relatively understudied protein, it has been linked to the function and activity of MAVS 27 . Dysregulation of the MAVS antiviral pathway, by inborn errors of immunity, has been shown to underlie certain cases of MIS-C 7 . The most straightforward connection linking MIS-C to SNX8 may be through an inappropriate autoimmune response against tissues with elevated MAVS pathway expression. These results are the first to directly link the initial SARS-CoV-2 infection and the subsequent development of MIS-C. We propose that MIS-C may be the result of multiple uncommon events converging. The initial insult is probably the formation of a combined B cell and T cell response that preferentially targets a particular motif within the MADS region of the SARS-CoV-2 nucleocapsid protein. In a subset of individuals, these B cell and T cell responses cross-react to the self-protein SNX8. This cross-reactive motif has strong binding characteristics for the MIS-C-associated HLA-A*02 (ref. 16 ), further indicating that this may be an important risk factor in the development of MIS-C.

Using conservative criteria (3 s.d. greater than controls by targeted immunoprecipitation of the epitope-containing peptide), at least 17% of sera from patients with MIS-C are autoreactive for SNX8; however, approximately 37% of sera from patients with MIS-C yielded detectable enrichment compared with controls in the entire dataset. Because we only tested for a single epitope target, we are unable to determine the upper limits of the in vivo frequency of cross-reactive CD8 + T cells in patients with MIS-C. Our results suggest that the frequency of these cross-reactive CD8 + T cells is within the range of 1 in 10,000–100,000 CD8 + T cells. This substantially exceeds the frequency of antigen-specific autoreactive CD8 + T cells found in peripheral circulation in bona fide T cell-mediated autoimmune diseases such as T1DM 38 and multiple sclerosis 44 , 45 . Similar to T1DM, the autoreactive and cross-reactive CD8 + T cells in patients with MIS-C may be found at far greater abundance within peripheral tissues known to be affected by the disease 38 . Even accounting for these limitations, our results describe a subset of MIS-C, indicating that other mechanisms probably exist. Antibodies to ERFL are present in many children with MIS-C who do not have autoreactivity to SNX8, and ERFL has a highly similar tissue RNA expression profile as SNX8 (second-most similar among all known proteins; Human Protein Atlas) 36 . If autoreactive T cells to ERFL indeed exist, they would be predicted to engage a nearly identical set of cells and tissues. It is important to also consider that MIS-C prevalence has rapidly decreased as an increasing number of children have developed immunity through vaccination and natural SARS-CoV-2 infection. We speculate that perhaps this could be related to the strong deviation of the anti-SARS-CoV-2 immune response away from the critical MADS region of the nucleocapsid protein that we have identified, to other major epitopes such as those in the spike protein through vaccination and past infection 49 . Supporting this notion is recent CDC surveillance, which noted that more than 80% (92 of 112) of individuals with MIS-C in 2023 were in unvaccinated children (but vaccine eligible), and that the majority of children who developed MIS-C despite previous vaccination probably had waned immunity 50 .

MIS-C is complex, and more work will be required to fully understand this syndrome. The results of this study, and specifically the development of combined cross-reactive B cells and T cells, build on other notable examples of molecular mimicry; however, the mechanisms by which the presence of a cross-reactive epitope forces a break in tolerance remain unclear. Our results shed light on how one post-infectious disease (MIS-C) develops, yielding insights that may help better explain, diagnose and ultimately treat a range of additional conditions associated with infections.

Patients were recruited through the prospectively enrolling multicentre Overcoming COVID-19 and Taking on COVID-19 Together study in the USA. All patients meeting clinical criteria were included in the study, and therefore no statistical methods were used to predetermine sample size and no blinding or randomization of subjects occurred. The study was approved by the central Boston Children’s Hospital Institutional Review Board (IRB) and reviewed by IRBs of participating sites with CDC IRB reliance. A total of 292 patients consented and were enrolled into one of the following independent cohorts between 1 June 2020 and 9 September 2021: 223 patients hospitalized with MIS-C (199 in the primary discovery cohort and 24 in a separate subsequent validation cohort), 29 patients hospitalized for COVID-19 in either an intensive care or step-down unit (referred to as ‘severe acute COVID-19’ in this study) and 45 outpatients (referred to as ‘at-risk controls’ in this study) post-SARS-CoV-2 infections associated with mild or no symptoms. The demographic and clinical data are summarized in Extended Data Tables 1 – 3 . The 2020 US CDC case definition was used to define MIS-C 51 . All patients with MIS-C had positive SARS-CoV-2 serology results and/or positive SARS-CoV-2 test results by reverse transcriptase quantitative PCR. All patients with severe COVID-19 or outpatient SARS-CoV-2 infections had a positive antigen test or nucleic acid amplification test for SARS-CoV-2. For outpatients, samples were collected from 36 to 190 days after the positive test (median of 70 days after a positive test; interquartile range of 56–81 days). For use as controls in the SARS-CoV-2-specific PhIP-seq, plasma from 48 healthy, pre-COVID-19 controls were obtained as deidentified samples from the New York Blood Center. These samples were part of retention tubes collected at the time of blood donations from volunteer donors who provided informed consent for their samples to be used for research.

DNA oligomers for SLBAs

DNA coding for the desired peptides for use in SLBAs were inserted into split luciferase constructs containing a terminal HiBiT tag and synthesized (Twist Biosciences) as DNA oligomers and verified by Twist Biosciences before shipment. Constructs were amplified by PCR using the 5′- AAGCAGAGCTCGTTTAGTGAACCGTCAGA-3′ and 5′-GGCCGGCCGTTTAAACGCTGATCTT-3′ primer pair.

For SNX8, the oligomers coded for the following sequences:

EADPPASDLPTPQAIEPQAIVQQVPAPSRMQMPQGNPLLLSHTLQELLA

AAAAAAAAAATPQAIEPQAIVQQVPAPSRMQMPQGNPLLLSHTLQELLA

EADPPAAAAAAAAAAEPQAIVQQVPAPSRMQMPQGNPLLLSHTLQELLA

EADPPASDLPAAAAAAAAAAVQQVPAPSRMQMPQGNPLLLSHTLQELLA

EADPPASDLPTPQAIAAAAAAAAAAAPSRMQMPQGNPLLLSHTLQELLA

EADPPASDLPTPQAIEPQAIAAAAAAAAAAQMPQGNPLLLSHTLQELLA

EADPPASDLPTPQAIEPQAIVQQVPAAAAAAAAAANPLLLSHTLQELLA

EADPPASDLPTPQAIEPQAIVQQVPAPSRMAAAAAAAAAASHTLQELLA

EADPPASDLPTPQAIEPQAIVQQVPAPSRMQMPQGAAAAAAAAAAELLA

EADPPASDLPTPQAIEPQAIVQQVPAPSRMQMPQGNPLLLAAAAAAAAA

For SARS-CoV-2 nucleocapsid protein, the oligomers coded for the following sequences:

ATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQA

AAAAAAAAAADHIGTRNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQA

ATEGAAAAAAAAAAARNPANNAAIVLQLPQGTTLPKGFYAEGSRGGSQA

ATEGALNTPKAAAAAAAAAANAAIVLQLPQGTTLPKGFYAEGSRGGSQA

ATEGALNTPKDHIGTAAAAAAAAAALQLPQGTTLPKGFYAEGSRGGSQA

ATEGALNTPKDHIGTRNPANAAAAAAAAAAGTTLPKGFYAEGSRGGSQA

ATEGALNTPKDHIGTRNPANNAAIVAAAAAAAAAAKGFYAEGSRGGSQA

ATEGALNTPKDHIGTRNPANNAAIVLQLPQAAAAAAAAAAEGSRGGSQA

ATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPAAAAAAAAAAGSQA

ATEGALNTPKDHIGTRNPANNAAIVLQLPQGTTLPKGFYAAAAAAAAAA

DNA plasmids for RLBAs

For RLBAs, DNA expression plasmids under control of a T7 promoter and with a terminal Myc–DDK tag for the desired protein were utilized. For ERFL, a custom plasmid was ordered from Twist Bioscience in which a Myc–DDK-tagged full-length ERFL sequence under a T7 promoter was inserted into the pTwist Kan High Copy Vector (Twist Bioscience). Twist Bioscience verified a sequence-perfect clone by next-generation sequencing before shipment. Upon receipt, the plasmid was sequence verified by Primordium Labs. For SNX8, a plasmid containing the Myc–DDK-tagged full-length human SNX8 under a T7 promoter was ordered from Origene (RC205847) and was sequence verified by Primordium Labs upon receipt. For KDELR1, a plasmid containing the Myc–DDK-tagged full-length human KDELR1 under a T7 promoter was ordered from Origene (RC205880) and was sequence verified by Primordium Labs upon receipt. For IL1RN, a plasmid containing the Myc–DDK-tagged full-length human IL1RN under a T7 promoter was ordered from Origene (RC218518) and was sequence verified by Primordium Labs upon receipt.

Polypeptide pools for activation-induced marker assays

To obtain polypeptides tiling the full-length SNX8 protein, 15-mer polypeptide fragments with 11-amino acid overlaps were ordered from JPT Peptide Technologies and synthesized. Together, a pool of 130 of these polypeptides (referred to as the ‘SNX8 pool’) spanned all known translated SNX8 (the full-length 465-amino acid SNX8 protein, as well as a unique region of SNX8 isoform 3). A separate pool was designed to cover primarily the region of SNX8 with similarity to the SARS-CoV-2 nucleocapsid protein in high resolution (referred to as the ‘high-resolution epitope pool’). This pool contained 20 10-mers with 9-amino acid overlaps tiling amino acids 44–72 (IVQQVPAPSRMQMPQGNPLLLSHTLQELL) of the full-length SNX8 protein. The sequence of each of these 150 polypeptides was verified by mass spectrometry and purity was calculated by high-performance liquid chromatography (HPLC).

Peptides for tetramer assays

For use in loading tetramers, three peptides were ordered from Genemed Synthesis as 9-mers. LQLPQGTTL and LQLPQGITL correspond to the region of the SARS-CoV-2 nucleocapsid protein with similarity to human SNX8 in the ancestral sequence and a minor variant, respectively. This sequence was verified by mass spectrometry and purity was calculated as 96.61% by HPLC. The other sequence, MQMPQGNPL, corresponds to the region of human SNX8 protein with similarity to the SARS-CoV-2 nucleocapsid protein. This sequence was verified by mass spectrometry and purity was calculated as 95.83% by HPLC.

Human proteome PhIP-seq

Human proteome PhIP-seq was performed following our previously published vacuum-based PhIP-seq protocol 12 ( https://www.protocols.io/view/scaled-high-throughput-vacuum-phip-protocol-ewov1459kvr2/v1 ).

Our human peptidome library consists of a custom-designed phage library of 731,724 unique T7 bacteriophage each presenting a different 49-amino acid peptide on its surface. Collectively, these peptides tile the entire human proteome including all known isoforms (as of 2016) with 25-amino acid overlaps. Of the phage library, 1 ml was incubated with 1 μl of human serum overnight at 4 °C and immunoprecipitated with 25 μl of 1:1 mixed protein A and protein G magnetic beads (10008D and 10009D, Thermo Fisher). These beads were than washed, and the remaining phage–antibody complexes were eluted in 1 ml of Escherichia coli (BLT5403, EMD Millipore) at 0.5–0.7 OD and amplified by growing in a 37 °C incubator. This new phage library was then re-incubated with the serum from the same individual and the previously described protocol was repeated. DNA was then extracted from the final phage library, barcoded, PCR amplified and Illumina adaptors were added. Next-generation sequencing was performed using an Illumina sequencer (Illumina) to a read depth of approximately 1 million per sample.

Human proteome PhIP-seq analysis

All human peptidome analysis (except when specifically stated otherwise) was performed at the gene level, in which all reads for all peptides mapping to the same gene were summed, and 0.5 reads were added to each gene to allow inclusion of genes with zero reads in mathematical analyses. Within each individual sample, reads were normalized by converting to the percentage of total reads. To normalize each sample against background nonspecific binding, a fold change over mock-IP was calculated by dividing the sample read percentage for each gene by the mean read percentage of the same gene for the AG bead-only controls. This fold-change signal was then used for side-by-side comparison between samples and cohorts. Fold-change values were also used to calculate z scores for each patient with MIS-C compared with controls and for each control sample by using all remaining controls. These z scores were used for the logistic-regression feature weighting. In instances of peptide-level analysis, raw reads were normalized by calculating the number of reads per 100,000 reads.

SARS-CoV-2 proteome PhIP-seq

SARS-CoV-2 proteome PhIP-seq was performed as previously described 39 . In brief, 38 amino acid fragments tiling all open reading frames from SARS-CoV-2, SARS-CoV-1 and 7 other CoVs were expressed on T7 bacteriophage with 19-amino acid overlaps. Of the phage library, 1 ml was incubated with 1 μl of human serum overnight at 4 °C and immunoprecipitated with 25 μl of 1:1 mixed protein A and protein G magnetic beads (10008D and 10009D, Thermo Fisher). Beads were washed five times on a magnetic plate using a P1000 multichannel pipette. The remaining phage–antibody complexes were eluted in 1 ml of E. coli (BLT5403, EMD Millipore) at 0.5–0.7 OD and amplified by growing in 37 °C incubator. This new phage library was then re-incubated with the serum of the same individual and the previously described protocol was repeated for a total of three rounds of immunoprecipitations. DNA was then extracted from the final phage library, barcoded, PCR amplified and Illumina adaptors were added. Next-generation sequencing was then performed using an Illumina sequencer (Illumina) to a read depth of approximately 1 million per sample.

Coronavirus proteome PhIP-seq analysis

To account for differing read depths between samples, the total number of reads for each peptide fragment was converted to the number of reads per 100,000 (RPK). To calculate normalized enrichment relative to pre-COVID-19 controls (FC > pre-COVID-19), the RPK for each peptide fragment within each sample was divided by the mean RPK of each peptide fragment among all pre-COVID-19 controls. These FC > pre-COVID-19 values were used for all subsequent analyses as described in the text and figures.

RLBAs were performed as previously described 12 , 32 . In brief, DNA plasmids containing full-length cDNA under the control of a T7 promoter for each of the validated antigens (see ‘DNA plasmids for RLBAs’ above) were verified by Primordium Labs sequencing. The respective DNA templates were used in the T7 TNT in vitro transcription/translation kit (L1170, Promega) using [35S]-methionine (NEG709A, PerkinElmer). Respective protein was column purified on Nap-5 columns (17-0853-01, GE Healthcare), and equal amounts of protein (approximately 35,000 counts per minute) were incubated overnight at 4 °C with 2.5 μl of serum or 1 μl of anti-Myc-positive control antibody (1:10 dilution; 2272S, Cell Signaling Technology). Immunoprecipitation was then performed on 25 μl of Sephadex protein A/G beads (4:1 ratio; GE17-5280-02 and GE17-0618-05, Sigma-Aldrich) in 96-well polyvinylidene difluoride filtration plates (EK-680860, Corning). After thoroughly washing, the counts per minute of immunoprecipitated protein was quantified using a 96-well Microbeta Trilux liquid scintillation plate reader (Perkin Elmer).

SLBA was performed as previously described 52 . A detailed SLBA protocol is available on protocols.io ( https://doi.org/10.17504/protocols.io.4r3l27b9pg1y/v1 ).

In brief, the DNA oligomers listed above (see ‘DNA oligomers for SLBAs’) were amplified by PCR using the primer pairs listed above (see ‘DNA oligomers for SLBAs’). Unpurified PCR product was used as input in the T7 TNT in vitro transcription/translation kit (L1170, Promega) and the Nano-Glo HiBit Lytic Detection System (N3040, Promega) was used to measure relative luciferase units of translated peptides in a luminometre. Equal amounts of protein (in the range of 2 × 10 6 –2 × 10 7 relative luciferase units) were incubated overnight with 2.5 μl patient sera or 1 μl anti-HiBit-positive control antibody (1:10 dilution; CS2006A01, Promega) at 4 °C. Immunoprecipitation was then performed on 25 µl of Sephadex protein A/G beads (1:1 ratio; GE17-5280-02 and GE17-0618-05, Sigma-Aldrich) in 96-well polyvinylidene difluoride filtration plates (EK-680860, Corning). After thoroughly washing, luminescence was measured using the Nano-Glo HiBit Lytic Detection System (N3040, Promega) in a luminometre.

Activation-induced marker assay

PBMCs were obtained from ten patients with MIS-C and ten controls for use in the activation-induced marker assay. PBMCs were thawed, washed, resuspended in serum-free RPMI medium and plated at a concentration of 1 × 10 6 cells per well in a 96-well round-bottom plate. For each individual, PBMCs were stimulated for 24 h with either the SNX8 pool (see above) at a final concentration of 1 mg ml −1 per peptide in 0.2% DMSO or a vehicle control containing 0.2% DMSO only. For four of the controls and two of the patients with MIS-C, there were sufficient PBMCs for an additional stimulation condition using the SNX8 high-resolution epitope pool (see above) also at a concentration of 1 mg ml −1 per peptide in 0.2% DMSO for 24 h. Following the stimulation, cells were washed with FACS buffer (Dulbecco’s PBS without calcium or magnesium, 0.1% sodium azide, 2 mM EDTA and 1% FBS) and stained with the following antibody panel each at 1:100 dilution for 20 min at 4 °C, and then flow cytometry analysis was immediately performed.

For the antibody panel: anti-CD3 Alexa 647 (clone OKT3, 317312, BioLegend), anti-CD4 Alexa 488 (clone OKT4, 317420, BioLegend), anti-CD8 Alexa 700 (clone SK1, 344724, BioLegend), anti-OX-40 (also known as CD134) PE-Dazzle 594 (clone ACT35, 350020, BioLegend), anti-CD69 PE (clone FN-50, 310906, BioLegend), anti-CD137 (also known as 4-1BB) BV421 (clone 4B4-1, 309820, BioLegend), anti-CD14 PerCP-Cy5 (clone HCD14, 325622, BioLegend), anti-CD16 PerCP-Cy5 (clone B73.1, 360712, BioLegend), anti-CD19 PerCP-Cy5 (clone HIB19, 302230, BioLegend) and Live/Dead Dye eFluor 506 (65-0866-14, Invitrogen).

The activation-induced marker analysis was performed using FlowJo software using the gating strategy shown in Extended Data Fig. 7a . All gates were fixed within each condition of each sample. Activated CD4 T cells were defined as those that were co-positive for OX40 and CD137. Activated CD8 T cells were defined as those that were co-positive for CD69 and CD137. Gating thresholds for activation were defined by the outer limits of signal in the vehicle controls allowing for up to two outlier cells. Frequencies were calculated as a percentage of total CD3 + cells (T cells). Two MIS-C samples had insufficient total events captured by flow cytometry (total of 5,099 and 4,919 events, respectively) and were therefore removed from analysis.

Initial tetramer assay

For the initial tetramer assay, see Extended Data Fig. 4a . PBMCs from two patients with MIS-C with HLA-A*02:01 (HLA typed from PAXgene RNAseq, one confirmed by serotyping), one patient with MIS-C with HLA-B*35:01 (HLA typed from PAXgene RNAseq) and three at-risk controls with HLA-A*02.01 (all three identified by serotyping, two of three confirmed by PAXgene RNAseq HLA typing; the other sample did not have genomic DNA available for genotyping) were thawed, washed and put into culture with media containing recombinant human IL-2 at 10 ng ml −1 in 96-well plates. The peptide fragments (details above) LQLPQGITL and MQMPQGNPL were then added to PBMCs to a final concentration of 10 mg ml −1 per peptide and incubated (37 °C at 5% CO 2 ) for 7 days.

Following the 7 days of incubation, a total of eight pHLA class I tetramers were generated from UV-photolabile biotinylated monomers, four each from HLA-A*02:01 and HLA-B*35:01 (NIH Tetramer Core). Peptides were loaded via UV peptide exchange. Tetramerization was carried out using streptavidin conjugated to fluorophores PE and APC or BV421 followed by quenching with 500 µM d -biotin, similar to our previously published methods 44 , 53 . Tetramers were then pooled together as shown below:

For the HLA-A*02:01 pool, the MADS (LQLPQGITL)-loaded PE tetramer, MADS (LQLPQGITL)-loaded APC tetramer, SNX8 (MQMPQGNPL)-loaded PE tetramer and SNX8 (MQMPQGNPL)-loaded BV421 tetramer were used, all with HLA-A*02:01 restriction.

For the HLA-B*35:01 pool, the MADS (LQLPQGITL)-loaded PE tetramer, MADS (LQLPQGITL)-loaded APC tetramer, SNX8 (MQMPQGNPL)-loaded PE tetramer and SNX8 (MQMPQGNPL)-loaded BV421 tetramer were used, all with HLA-B*35:01 restriction.

All PBMCs were then treated with 100 nM dasatinib (StemCell) for 30 min at 37 °C followed by staining (no wash step) with the respective tetramer pool corresponding to their HLA restriction (final concentration of 2–3 µg ml −1 ) for 30 min at 25 °C. Cells were then stained with the following cell-surface markers each at 1:100 dilution for 20 min, followed by immediate analysis on a flow cytometer.

For the surface markers: anti-CD8 Alexa 700 (clone SK1, 357404, BioLegend), anti-CD4 PerCP-Cy5 (clone SK1, 300530, BioLegend), anti-CD14 PerCP-Cy5 (clone HCD14, 325622, BioLegend), anti-CD16 PerCP-Cy5 (clone B73.1, 360712, BioLegend), anti-CD19 PerCP-Cy5 (clone HIB19, 302230, BioLegend) and Live/Dead Dye eFluor 506 (65-0866-14, Invitrogen). Streptavidin was conjugated to PE (S866, Invitrogen), APC (S868, Invitrogen) and BV421 (405225, BioLegend).

The gating strategy is outlined in Extended Data Fig. 7b . A stringent tetramer gating strategy was used to identify cross-reactive T cells, in which CD8 + T cells were required to be triple positive for PE, APC and BV421 labels (that is, a single CD8 T cell bound to PE-conjugated LQLPQGITL and/or PE-conjugated MQMPQGNPL in addition to APC-conjugated LQLPQGITL and BV421-conjugated MQMPQGNPL).

Serotyping was performed using an anti-HLA-A2 antibody (1:100 dilution; FITC anti-human HLA-A2 antibody, clone BB7.2, 343303, BioLegend), and pertinent results are shown in Extended Data Fig. 7c .

Assembly of easYmer monomers and fold testing

For the assembly of HLA class I pHLA easYmer monomers and fold testing, see Fig. 4 . Unfolded, biotinylated easYmer monomers (Immudex) were obtained for HLA-A*02:01 and HLA-A*02:06. SARS-CoV-2 MADS (LQLPQGITL), SARS-CoV-2 Wuhan (LQLPQGTTL) and human SNX8 (MQMPQGNPL) peptides were commercially synthesized (Genscript), diluted to 1 mM in ddH 2 O or DMSO, and loaded onto each easYmer allele according to the manufacturer’s instructions at 18 °C for 48 h. Proper pHLA monomer formation and MADS and SNX8 peptide-binding strength were evaluated for each HLA using a ‘β2m fold test’ relative to negative (no peptide; unloaded monomer) and positive (strong binding peptide; CMV pp65 495–503 (NLVPMVATV)) controls as per the manufacturer’s protocol. In brief, peptide-loaded monomers with a concentration of 500 nM were serially diluted to 9 nM, 3 nM and 1 nM in dilution buffer (1× PBS with 5% glycerol; G5516, Sigma-Aldrich) and incubated with streptavidin beads (6–8 μm; SVP-60-5, Spherotech) at 37 °C for 1 h to allow binding of stable complexes to beads, then washed three times with FACS buffer (1× PBS, 0.5% BSA (A7030, Sigma-Aldrich) and 2 mM EDTA (15575-038, Thermo Fisher Scientific)). Samples were then stained with PE-conjugated anti-human β2m antibody (clone BBM.1, sc-13565, Santa Cruz Biotech) at 1:200 for 30 min at 4 °C, washed three times with FACS buffer and analysed on a 5 Laser 16UV-16V-14B-10YG-8R AURORA spectral cytometer (Cytek). pHLA-binding strength positively correlated with stability and concentration of the pHLA–β2m complex. Therefore, the geometric mean fluorescence intensity of anti-β2m staining in this assay reports on the strength of the pHLA binding compared with the positive and negative controls. We classified binding strength for each HLA and peptide combination based on the fold change in anti-β2m geometric mean fluorescence intensity over the no-peptide negative control at 9 nM. Strong binders were defined at more than 10-fold higher, moderate binders at more than 3-fold, weak binders at more than 1.5-fold and non-binders at less than 1.5-fold change over the negative control. Flow cytometry data were analysed using FlowJo version 10.7.2 software (BD Biosciences).

pHLA tetramer assembly

For the pHLA tetramer assembly, see Fig. 4 . pHLA tetramers were assembled from HLA-A*02:01 and HLA-A*02:06 easYmer monomers (Immudex) with confirmed peptide binding to SARS-CoV-2 MADS (LQLPQGITL), Wuhan (LQLPQGTTL) and SNX8 (MQMPQGNPL) peptides according to the manufacturer’s instructions. In brief, fluorochrome-conjugated streptavidin (0.2 mg ml −1 , PE, 405203, BioLegend; 0.2 mg ml −1 , APC, 405207, BioLegend; and BV421, 405226, BioLegend) was added to loaded monomers at 8 ng per 1 μl pHLA complex (500 nM) in three volumes. After each 1/3 volume addition, samples were mixed and incubated for 15 min at 4 °C in the dark. Assembled tetramers were stored at 4 °C in the dark until use.

Enhanced peptide-specific T cell expansion

For enhanced peptide-specific T cell expansion, see Fig. 4 . PBMCs from MIS-C confirmed participants with HLA-A*02:01 or HLA-A*02:06 were obtained for peptide-specific expansion according to published methods 54 before single-cell sorting of tetramer-positive T cells. On expansion day 0, PBMCs were thawed, counted and seeded onto 96-well round-bottom plates at 100,000 cells per well in 200 μl antigen-presenting cell differentiation media (X-VIVO 15 serum-free haematopoietic cell medium (04-418Q, Lonza) supplemented with human GM-CSF (1,000 IU ml −1 ; 130-095-372, Miltenyi Biotec), human IL-4 (500 IU ml −1 ; 204-IL-010, R&D Systems) and human Flt3-L (50 ng ml −1 ; 308-FKN-025, R&D Systems) final concentrations) and incubated for 24 h at 37 °C and 5% CO 2 . On day 1, 100 μl cell supernatant was replaced with 100 μl Adjuvant Solution (X-VIVO 15 supplemented with R848 (10 μM; tlrl-r848-5, InvivoGen), lipopolysaccharide ( Salmonella minnesota ; 100 ng ml −1 ; tlrl-smlps, InvivoGen) and human IL-1β (10 ng ml −1 ; 201-LB-010, R&D Systems) final concentrations) and pooled MADS (LQLPQGITL) and SNX8 (MQMPQGNPL) peptides at a final concentration of 10 μM each. No-peptide control wells were set up for each sample by adding a 1:2 dilution of DMSO in H 2 O to match the peptide volume and diluent. Cells were incubated for 24 h at 37 °C and 5% CO 2 . On days 2, 4, 7 and 9, 100 μl supernatant was replaced with 100 μl T cell expansion solution: RP-10 (RPMI 1640 (22400-089, Gibco), 10% heat-inactivated human serum AB (100-512, Gemini Bio-Products), 10 mM HEPES, 0.1 mg ml −1 gentamicin (15750-060, Thermo Fisher Scientific) and 1× GlutaMAX (35050-061, Gibco)) supplemented with human IL-2 (10 IU ml −1 ; 202-IL-050, R&D Systems), human IL-7 (10 ng ml −1 ; 207-IL-025, R&D Systems) and human IL-15 (10 ng ml −1 ; 200-15, PeproTech) final concentrations. On day 10, peptide-expanded cells from an individual participant were pooled; cells from no-peptide controls were collected separately.

Single-cell index sorting

Unexpanded PBMCs (direct ex vivo) or peptide-expanded T cells were obtained, washed in 1× PBS and treated with 100 nM dasatinib (CDS023389, Sigma-Aldrich) in 1× PBS for 30 min at 37 °C and 5% CO 2 (ref. 55 ). Cells were then pelleted and resuspended in 50 μl FACS buffer (1× PBS and 0.04% BSA) supplemented with human TruStain FcX blocking buffer (1:10 dilution; 422302, BioLegend), 500 μM d -biotin (B20656, Thermo Fisher Scientific) and a unique tetramer cocktail containing MADS–tetramer–PE (1:10 dilution), MADS–tetramer–APC (1:10 dilution), SNX8–tetramer–PE (1:10 dilution) and SNX8–tetramer–BV421 (1:10 dilution) based on participant HLA type (A*02:01 and A*02:06). Cells were incubated in the dark at 25 °C for 1 h followed by direct addition of 50 μl (100 μl total volume) of FACS supplemented with 500 μM d -biotin and an antibody cocktail containing FITC-conjugated anti-human CD3 (1:20 dilution; clone OKT3, lot B390808, 317306, BioLegend), BV605-conjugated anti-human CD8 (1:20 dilution; clone SK1, lot B371925, 344742, BioLegend), BV510-conjugated anti-human CD4 (1:20 dilution; clone OKT4, lot B375526, 317444, BioLegend), BV510-conjugated anti-human CD14 (1:20 dilution; clone 63D3, lot B390770, 367124, BioLegend), BV510-conjugated anti-human CD16 (1:20 dilution; clone 3G8, lot B372132, 302048, BioLegend), BV510-conjugated anti-human CD19 (1:20 dilution; clone HIB19, lot B390665, 302242, BioLegend) and Ghost Dye Violet 510 Viability Dye (1:400 dilution; lot D0870061322133, 13-0870-T500, Tonbo Biosciences) for 30 min in the dark at 4 °C. Cells were then pelleted, washed twice with 4 ml FACS buffer (containing 500 μM d -biotin), suspended in 500 μl FACS (containing 500 μM d -biotin) and passed through a 45-μM filter before proceeding to single-cell sorting on a Sony SY3200 cell sorter. Individual, live, BV510 dump gate (CD4, CD14, CD16 and CD19)-negative, CD3 + CD8 + T lymphocytes were gated to distinguish tetramer triple-positive cells (PE + APC + BV421 + ) as described in Extended Data Fig. 7d and sorted into individual wells of a 384-well plate loaded with Superscript VILO master mix (11754250, Thermo Fisher Scientific). After sorting, plates were centrifuged at 500 g and stored at −80 °C until processing.

Paired TCRαβ amplification and sequencing

Single-cell paired TCRα and TCRβ chain library preparation and sequencing was performed on T cells sorted into 384-well index plates as previously described 56 . In brief, after reverse transcription of cells sorted in Superscript VILO master mix, cDNA underwent two rounds of nested multiplex PCR amplification using a mix of human V-segment-specific forward primers and human TRAC and TRBC segment-specific reverse primers (see Supplementary Table 1 for primer details). Resulting TCRα and TCRβ amplicons were sequenced on an Illumina MiSeq at 2 × 150-bp read length.

All cultured cell lines were maintained at 37 °C and 5% CO 2 in a humidified incubator. HEK 293T cells (CRL-3216, American Type Culture Collection) were purchased from the American Type Culture Collection and verified commercially. HEK 293T cells were cultured in DMEM (11965-092, Gibco) supplemented with 10% FBS (16140-071, Gibco), 2 mM l -glutamine (25030-081, Gibco) and 100 U ml −1 penicillin–streptomycin (15140-122, Gibco). 2D3 Jurkat J76.7 cells 57 , 58 (TCR-null, CD8 + ) expressing an NFAT–eGFP reporter were kindly provided by F. Fujiki and were cultured in RPMI 1640 (22400-089, Gibco) supplemented with 10% FBS, 2 mM l -glutamine and 100 U ml −1 penicillin–streptomycin. All cell lines were confirmed to be mycoplasma negative during the course of experiments.

TCR repertoire analysis

TCR similarity networks were constructed as previously described 49 , 59 . In brief, to measure the distance between TCRαβ clonotypes, we used the TCRdist algorithm implementation from the CoNGA v0.1.2 Python package 47 . Further analysis was performed using the R language for statistical computing, with merging and subsetting of data performed using the dplyr v1.1.4 package. TCR similarity networks were built using stringdist v0.9.12 and igraph v2.0.3 (ref. 60 ) R packages, and visualized using gephi v0.9.7 (ref. 61 ) software.

TCR reconstruction and cloning

Full-length TCRαβ sequences were reconstructed from V/J gene usage and CDR3 sequences using Stitchr v1.0.0 (ref. 62 ) for each index-sorted T cell. TCRα and TCRβ chain sequences were modified to use murine constant regions and joined by a 2A element from thosea asigna virus (T2A). A sequence encoding mCherry was additionally appended by a 2A element from porcine teschovirus (P2A) as a fluorescent marker of transduction. The full-length gene fragment encoding TCRβ–T2A–TCRα–P2A–mCherry was synthesized and cloned commercially (Genscript) into the lentiviral vector pLVX-EF1α-IRES-Puro (631253, Takara).

Generation of TCR-expressing Jurkat cells

To generate transducing particles packaging individual TCRs of interest (Fig. 4c ), HEK 293T cells were transduced with a pLVX lentiviral vector encoding a unique TCRαβ–mCherry insert, psPAX2 packaging plasmid (plasmid #12260, Addgene) and an pMD2.G envelope plasmid (plasmid #12259, Addgene) at a ratio of 4:3:1. At 24 h and 48 h post-transfection, viral supernatants were harvested, passed through a 0.45-µm SFCA filter (723-9945, Thermo Fisher Scientific), concentrated using Lenti-X Concentrator (631232, Takara) and stored at −80 °C as single-use aliquots. To generate TCR-expressing Jurkat cell lines (Jurkat-TCR + ), 2D3 Jurkat J76.7 cells (TCR-null, CD8 + , NFAT–eGFP reporter) were seeded in a 12-well tissue-culture-treated plate at 1 × 10 6 cells per well in complete RPMI (RPMI 1640, 10% FBS, 2 mM l -glutamine, 100 U ml −1 penicillin–streptomycin) and transduced by adding concentrated lentivirus dropwise to each well. At 48–72 h post-tranduction, puromycin was added at 1 μg ml −1 and cultured for 1 week to select for transduced cells. Jurkat-TCR + cell lines were validated for the presence of correctly folded TCR on the cell surface by flow cytometry using a monoclonal antibody targeting the mouse TCRβ constant region (APC/Fire750-conjugated; clone H57-597, 109246, BioLegend; Extended Data Fig. 5a ). Flow cytometry data were collected on a custom-configured BD Fortessa using FACSDiva software (v8.0.1; Becton Dickinson) and analysed using FlowJo version 10.7.2 software (BD Biosciences).

Specificity validation of putative cross-reactive TCR sequences

The specificity of TCR-expressing Jurkat T cell lines was validated by tetramer staining using the same reagents used for single-cell sorting PBMCs (above). In brief, 1 × 10 6 Jurkat-TCR + cell lines or untransduced Jurkat J76.7 (TCR-null; background control) were washed in 1× PBS and resuspended in 50 μl FACS buffer (1× PBS and 0.04% BSA) and a unique tetramer cocktail containing MADS–tetramer–PE (1:10 dilution), MADS–tetramer–APC (1:10 dilution), SNX8–tetramer–PE (1:10 dilution) and SNX8–tetramer–BV421 (1:10 dilution) based on the restricting HLA type (A*02:01 and A*02:06). Tetramers conjugated to the Wuhan peptide sequence (LQLPQGTTL), including Wuhan–tetramer–PE (1:10 dilution) and Wuhan–tetramer–APC (1:10 dilution), were also tested. A second set of wells were set up in which each individual tetramer was used to stain cells. Cells were incubated in the dark at 25 °C for 30 min after which 50 µl of FACS buffer containing Ghost Dye Violet 510 Viability Dye (1:400 dilution; lot D0870061322133, 13-0870-T500, Tonbo Biosciences) was added for an additional 30-min incubation in the dark at 25 °C. Cells were then washed twice with 1 ml FACS buffer, suspended in 300 μl FACS and analysed by flow cytometry on a custom-configured BD Fortessa using FACSDiva software (v8.0.1; Becton Dickinson). Cell population gating and fluorescence analysis was performed using FlowJo version 10.7.2 software (BD Biosciences) as described in Extended Data Fig. 7e .

scRNA-seq analysis

To assess the cell-type specificity in a relevant disease context, we analysed SNX8 expression from a single-cell sequencing of PBMC samples from patients with severe, mild or asymptomatic COVID-19 infection, influenza virus infection and healthy controls 48 . Gene expression data from 59,572 pre-filtered cells were downloaded from the Gene Expression Omnibus database under accession GSE149689 for analysis and downstream processing with scanpy v1.10.0 (ref. 63 ). Cells with (1) less than 1,000 total counts, (2) less than 800 expressed genes, and (3) more than 3,000 expressed genes were filtered out as further quality control, leaving 42,904 cells for downstream analysis. Gene expression data were normalized to have 10,000 counts per cell and were log1p transformed. Highly variable genes were calculated using the scanpy function highly_variable_genes using Seurat flavor with the default parameters (min_mean = 0.0125, max_mean = 3, and min_disp = 0.5) 64 . Only highly variable genes were used for further analysis. The total number of counts per cell was regressed out, and the gene expression matrix was scaled using the scanpy function scale with max_value = 10. Dimensionality reduction was performed using principal components analysis with 50 principal components. Batch balanced k -nearest neighbours, implemented with scanpy’s function bbknn, was used to compute the top neighbours and normalize batch effects 65 . The batch-corrected cells were clustered using the Leiden algorithm and projected into two dimensions with uniform manifold approximation and projection for visualization. Initial cluster identity was determined by finding marker genes with differential expression analysis performed using a Student’s t -test on log1p-transformed raw counts with the scanpy function rank_genes_groups 66 , 67 .

Statistical methods

All statistical analysis was performed in Python using the Scipy Stats package unless otherwise indicated. For comparisons of distributions of PhIP-seq enrichment between two groups, a non-parametric Kolmogorov–Smirnov test was utilized. For logistic-regression feature weighting, the Scikit-learn package 68 was used, and logistic-regression classifiers were applied to z -scored PhIP-seq values from individuals with MIS-C versus at-risk controls. A liblinear solver was used with L1 regularization, and the model was evaluated using a five-fold cross-validation (four of the five for training, and one of the five for testing). For the RLBAs and SLBAs, first an antibody index was calculated as follows: (sample value – mean blank value)/(positive control antibody values – mean blank values). For the alanine mutagenesis scans, blank values of each construct were combined, and a single mean was calculated. A normalization function was then applied to the experimental samples only (excluding antibody-only controls) to create a normalized antibody index ranging from 0 to 1. Comparisons between two groups of samples were performed using a Mann–Whitney U -test. An antibody was considered to be ‘positive’ when the normalized antibody index in a sample was greater than 3 s.d. above the mean of controls. When comparing two groups of normally distributed data, a Student’s  t -test was performed.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The published article includes all datasets generated or analysed as a part of this study. Individual source data are provided with associated figures (where appropriate) per the data-sharing agreement stipulated under the Ruth L. Kirschstein National Research Service Award Individual Postdoctoral Fellowship (award no. F32AI157296 to R.C.M.). Raw flow cytometry source files can be made available on reasonable request. All PhIP-seq data are publicly available via a Dryad digital repository ( https://doi.org/10.7272/Q6SJ1HVH ). Raw TCR reads are available through the NCBI Sequence Read Archive (SRA) BioProject PRJNA1110271 , with associated BioSample accession numbers SAMN41334731 , SAMN41334732 , SAMN41334730 and SAMN41334729 .  Source data are provided with this paper.

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Acknowledgements

The following members of the Overcoming COVID-19 Network Investigators study group were all closely involved with the design, implementation and oversight of the Overcoming COVID-19 study, as well as collecting patient samples and data: M. Kong, H. Kelley, M. Murdock, C. Colston. K. V. Typpo, K. Irby, R. C. Sanders Jr, M. Yates, C. Smith, N. Z. Cvijanovich, M. S. Zinter, A. B. Maddux, E. Port, R. Mansour, S. Shankman, N. Baig, F. Zorensky, P. S. Espinal, B. Chatani, G. McLaughlin, K. M. Tarquinio, K. Jones, B. M. Coates, C. M. Rowan, A. G. Randolph, M. M. Newhams, S. Kucukak, T. Novak, E. R. McNamara, H. Kyung Moon, T. Kobayashi, J. Melo, S. R. Jackson, M. K. Echon Rosales, C. Young, S. R. Chen, J. Chou, R. Da Costa Aguiar, M. Gutierrez-Arcelus and M. Elkins. The Taking On COVID-19 Together team include: D. Williams, L. Williams, L. Cheng, Y. Zhang, D. Crethers, D. Morley, S. Steltz, K. Zakar, M. A. Armant, F. Ciuculescu, H. R. Flori, M. K. Dahmer, E. R. Levy, S. Behl, N. M. Drapeau, C. V. Hobbs, J. E. Schuster, A. Kietzman, S. Hill, M. L. Cullimore, R. J. McCulloh, S. J. Gertz, S. P. Schwartz, T. C. Walker, R. A. Nofziger, M. A. Staat, C. C. Rohlfs, J. C. Fitzgerald, R. Burnett, J. Bush, E. H. Mack, N. Reed, N. B. Halasa, L. L. Loftis, H. Crandall and K. K. Ampofo. Members of the US Centers for Disease Control and Prevention COVID-19 Response Team on the Overcoming COVID-19 study were L. D. Zambrano, M. M. Patel and A. P. Campbell. The authors acknowledge the New York Blood Center for contributing pre-COVID-19 healthy donor blood samples, which were used as controls for the SARS-CoV-2 library PhIP-seq. The authors acknowledge the contributions of W. Browne and S. Pleasure for their work investigating potential central nervous system-specific autoimmunity in MIS-C; T. Kharel for help designing the Python code used in the analysis; D. Blauvelt for ideas regarding the application of advanced statistics to PhIP-seq data analysis; and S. A. Schattgen for thoughtful discussion of TCR sequencing and TCR similarity network analysis and help with deposition of the TCR sequencing into the Sequence Read Archive. BioRender ( https://biorender.com ) was used to build graphics for Fig. 1a and Extended Data Fig. 4a . This work was supported by the Pediatric Scientist Development Program and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K12-HD000850 to A.B.), and the Chan Zuckerberg Biohub SF (to J.L.D. and M.S.A.). Overcoming COVID-19 Study Network enrolment, patient data and specimen collections were supported by the CDC contracts 75D30120C07725, 75D30121C10297 and 75D30122C13330 from the Centers for Disease Control and Prevention to Boston Children’s Hospital to A.G.R. and the National Institute of Allergy and Infectious Diseases (R01AI154470) to A.G.R. Patient clinical data and specimens also collected at Boston Children’s Hospital for the Taking On COVID-19 Together (TOCT) study were supported in part by the Boston Children’s Hospital Emerging Pathogens and Epidemic Response Cluster of Clinical Research Excellence and the Institutional Centers for Clinical and Translational Research to A.G.R. and K.L.M. P.G.T. is supported by the American Lebanese Syrian Associated Charities at St. Jude Children’s Research Hospital (SJCRH) and funding from the National Institute of Allergy and Infectious Diseases (5R01AI154470-03, 2R01AI136514-06, 3P01AI165077-01S1, 75N93021C00016 and U01 AI144616). R.C.M. is supported by a Ruth L. Kirschstein National Research Service Award Individual Postdoctoral Fellowship award (F32AI157296).

Author information

These authors contributed equally: Aaron Bodansky, Robert C. Mettelman

These authors jointly supervised this work: Paul G. Thomas, Adrienne G. Randolph, Mark S. Anderson, Joseph L. DeRisi

Authors and Affiliations

Department of Pediatrics, Division of Critical Care, University of California San Francisco, San Francisco, CA, USA

Aaron Bodansky & Matt S. Zinter

Department of Host–Microbe Interactions, St. Jude Children’s Research Hospital, Memphis, TN, USA

Robert C. Mettelman, Mikhail V. Pogorelyy, Walid Awad, Allison M. Kirk & Paul G. Thomas

Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

Joseph J. Sabatino Jr, Colin R. Zamecnik & Michael R. Wilson

Department of Neurology, University of California San Francisco, San Francisco, CA, USA

Joseph J. Sabatino Jr, Colin R. Zamecnik, John V. Pluvinage & Michael R. Wilson

Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA

Sara E. Vazquez, Haleigh S. Miller, Andrew F. Kung, Elze Rackaityte, Jayant V. Rajan, Hannah Kortbawi, Caleigh Mandel-Brehm, Kelsey Zorn & Joseph L. DeRisi

Division of Immunology, Department of Pediatrics, Boston, MA, USA

Department of Pediatrics, Harvard Medical School, Boston, MA, USA

Janet Chou, Kristin L. Moffitt & Adrienne G. Randolph

Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, USA

Tanya Novak & Adrienne G. Randolph

Department of Anesthesia, Harvard Medical School, Boston, MA, USA

Department of Pediatric, Division of Infectious Diseases, Boston Children’s Hospital, Boston, MA, USA

Kristin L. Moffitt

Biological and Medical Informatics Program, University of California San Francisco, San Francisco, CA, USA

Haleigh S. Miller & Andrew F. Kung

Medical Scientist Training Program, University of California San Francisco, San Francisco, CA, USA

Hannah Kortbawi

Chan Zuckerberg Biohub SF, San Francisco, CA, USA

Anthea Mitchell, Chung-Yu Wang, Aditi Saxena & Joseph L. DeRisi

Diabetes Center, School of Medicine, University of California San Francisco, San Francisco, CA, USA

David J. L. Yu & Mark S. Anderson

Biomedical Sciences Program, University of California San Francisco, San Francisco, CA, USA

James Asaki

COVID-19 Response Team and Coronavirus and Other Respiratory Viruses Division, Centers for Disease Control and Prevention, Atlanta, GA, USA

Laura D. Zambrano & Angela P. Campbell

Department of Medicine, Division of Endocrinology and Metabolism, University of California San Francisco, San Francisco, CA, USA

Mark S. Anderson

Department of Pediatrics, Division of Critical Care Medicine, Baylor College of Medicine, Houston, TX, USA

• Laura L. Loftis

Department of Pediatrics, Division of Infectious Diseases, University of Mississippi Medical Center, Jackson, MS, USA

Charlotte V. Hobbs

Department of Pediatrics, Division of Critical Care Medicine, Emory University School of Medicine, Children’s Healthcare of Atlanta, Atlanta, GA, USA

Keiko M. Tarquinio

Department of Pediatrics, Division of Pediatric Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

Michele Kong

Department of Anesthesiology and Critical Care, Children’s Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

Julie C. Fitzgerald

Personalized Medicine and Health Outcomes Research, Nicklaus Children’s Hospital, Miami, FL, USA

Paula S. Espinal

Department of Pediatrics, University of North Carolina at Chapel Hill Children’s Hospital, Chapel Hill, NC, USA

Tracie C. Walker & Stephanie P. Schwartz

Department of Pediatrics, Division of Pediatric Critical Care, University of Utah, Primary Children’s Hospital, Salt Lake City, UT, USA

Hillary Crandall

Section of Pediatric Critical Care, Department of Pediatrics, Arkansas Children’s Hospital, Little Rock, AR, USA

Katherine Irby

Department of Pediatrics, Division of Infectious Diseases, University of Cincinnati and Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

Mary Allen Staat

Department of Pediatrics, Division of Pediatric Critical Care Medicine, Indiana University School of Medicine and Riley Hospital for Children, Indianapolis, IN, USA

Courtney M. Rowan

Department of Pediatrics, Division of Pediatric Infectious Diseases, Children’s Mercy Kansas City, Kansas City, MO, USA

Jennifer E. Schuster

Department of Pediatrics, Division of Pediatric Infectious Diseases, Vanderbilt University Medical Center, Nashville, TN, USA

Natasha B. Halasa

Department of Pediatrics, Division of Pediatric Critical Care, Cooperman Barnabas Medical Center, Livingston, NJ, USA

Shira J. Gertz

Division of Pediatric Critical Care Medicine, Medical University of South Carolina, Charleston, SC, USA

Elizabeth H. Mack

Department of Pediatrics, Section of Critical Care Medicine, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA

Aline B. Maddux

Division of Critical Care Medicine, UCSF Benioff Children’s Hospital Oakland, Oakland, CA, USA

Natalie Z. Cvijanovich

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Overcoming COVID-19 Network Investigators

• , Charlotte V. Hobbs
• , Keiko M. Tarquinio
• , Michele Kong
• , Julie C. Fitzgerald
• , Paula S. Espinal
• , Tracie C. Walker
• , Stephanie P. Schwartz
• , Hillary Crandall
• , Katherine Irby
• , Mary Allen Staat
• , Courtney M. Rowan
• , Jennifer E. Schuster
• , Natasha B. Halasa
• , Shira J. Gertz
• , Elizabeth H. Mack
• , Aline B. Maddux
• , Natalie Z. Cvijanovich
•  & Matt S. Zinter

Contributions

A.B., R.C.M., J.J.S.Jr, S.E.V., J.C., P.G.T., A.G.R., M.S.A. and J.L.D. conceptualized the study. A.B., R.C.M., J.J.S.Jr, S.E.V., E.R., C.R.Z., A.F.K., J.V.R., J.C., P.G.T., A.G.R., M.S.A. and J.L.D. curated the methodology. A.B., R.C.M., J.J.S.Jr, S.E.V., A.M., C.-Y.W., A.S., J.V.P., D.J.L.Y., H.K., W.A., A.M.K. and C.M.-B. performed or contributed to experiments. A.B., R.C.M., J.J.S.Jr, H.S.M., A.F.K., J.A. and M.V.P. performed the formal analysis. K.Z., T.N., L.D.Z., A.P.C., A.G.R., K.L.M. and the Overcoming COVID-19 Network Investigators acquired the patient sample and clinical data. T.N., A.G.R. and the Overcoming COVID-19 Network Investigators curated the clinical data. A.B., H.S.M. and J.L.D. wrote the original draft of the manuscript. A.B., R.C.M., J.C., T.N., H.S.M., L.D.Z., A.P.C., P.G.T., A.G.R., M.R.W., M.S.A. and J.L.D. reviewed and edited the manuscript. J.C., P.G.T., A.G.R., M.S.A. and J.L.D. supervised the study.

Corresponding authors

Correspondence to Mark S. Anderson or Joseph L. DeRisi .

Ethics declarations

Competing interests.

J.L.D. reports being a founder and paid consultant for Delve Bio, Inc., and a paid consultant for the Public Health Company and Allen & Co. M.A.S. receives unrelated research funding from the National Institutes of Health, the Centers for Disease Control and Prevention, Cepheid and Merck and unrelated honoria from UpToDate, Inc. M.R.W. receives unrelated research grant funding from Roche/Genentech and Novartis, and received speaking honoraria from Genentech, Takeda, WebMD and Novartis. J.C. reports consulting fees from GLG group, payments from Elsevier for work as an Associate Editor, a patent pending for methods and compositions for treating and preventing T cell-driven diseases, payments related to participation on a Data Safety Monitoring Board or Advisory Board for Enzyvant, and is a member of the Diagnostic Laboratory Immunology Committee of the Clinical Immunology Society. M.S.Z. receives unrelated funding from the National Heart, Lung, and Blood Institute and consults for Sobi. N.B.H. reports unrelated previous grant support from Sanofi and Quidel, and current grant support from Merck. C.V.H. reports being a speaker for Biofire and a reviewer for UpToDate, Inc. and Dynamed.com. A.G.R. receives royalties as a section editor for Pediatric Critical Care Medicine UpToDate, Inc., and received honoraria for MIS-C-related Grand Round Presentations. A.G.R. is also on the medical advisor board of Families Fighting Flu and is Chair of the International Sepsis Forum, which is supported by industry and has received reagents from Illumina, Inc. P.G.T. is on the Scientific Advisory Board of Immunoscape and Shennon Bio, has received research support and personal fees from Elevate Bio, and consulted for 10X Genomics, Illumina, Pfizer, Cytoagents, Merck and JNJ. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention nor the National Institute of Allergy and Infectious Diseases. All other authors declare no competing interests.

Peer review

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Nature thanks Shiv Pillai and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended data fig. 1 previously reported autoantigens and phenotypic associations of novel autoantigens..

a , Heatmap showing distribution of PhIP-Seq enrichments (FC > Mock-IP) of previously reported MIS-C autoantibodies in MIS-C patients ( n  = 199) and at-risk controls (n = 45). (b) Stripplots and boxplots showing distribution of signal (normalized antibody index) for antibodies targeting IL-1 receptor antagonist (IL-1Ra) measured by RLBA in at-risk controls (blue; n  = 45), MIS-C patient samples containing IVIG (red; n  = 135), and MIS-C patient samples without IVIG (green; n  = 61). Dotted line at 3 standard deviations above the mean of controls. Two-sided Mann-Whitney U testing was performed (exact P  values shown in figure). c , Heatmap of P  values (two-sided Kolmogorov-Smirnov testing) for differences in autoantibody enrichment for MIS-C patients ( n  = 199) with versus without each clinical phenotype (numbers vary for each phenotype and are shown in Extended Data Table 2 ). Significant P  values in the negative direction (in which there is increased signal in individuals without the phenotype) are masked (colored as P > 0.05). For each autoantigen, tissue RNA-sequencing data from Human Protein Atlas (Proteinatlas.org) is shown. Amount of expression in cardiac tissue in top row (Very high = nTPM >1000, High=nTPM 100-1000, Moderate=nTPM 10-100, Low=nTPM <10), and predominant tissue type in second-from-top row. Explanations of criteria for MIS-C phenotypes, and distribution of each phenotype within the cohort, can be found in Extended Data Table 2 .

Extended Data Fig. 2 Orthogonally validated autoantibodies classify MIS-C and can be epitope specific.

a , Stripplots and boxplots showing radioligand binding assay (RLBA) values (normalized antibody indices) for each of the top 3 autoantibodies identified by PhIP-Seq logistic regression in individuals with MIS-C ( n  = 197 for ERFL, n  = 196 for SNX8, n = 196 for KDELR1) and each at-risk control ( n  = 45 for ERFL, SNX8, and KDELR1). Two-sided Mann-Whitney U testing performed (exact P  values shown in figure). b , Logistic regression receiver operating characteristic (ROC) curve using RLBA values as input to distinguish MIS-C patients ( n  = 196) from at-risk controls ( n  = 45) iterated 1,000 times. c , Stripplots and boxplots showing RLBA enrichments (normalized antibody indices) only in those MIS-C samples without IVIG ( n  = 61 for ERFL, n  = 60 for SNX8, n  = 60 for KDELR1) relative to at-risk controls ( n  = 45 for ERFL, SNX8, and KDELR1). Two-sided Mann-Whitney U testing performed (exact P  values shown in figure). d , Stripplots abd boxplots showing RLBA enrichments (normalized antibody indices) for ERFL, SNX8, and KDELR1 in an independent cohort of children with MIS-C (red; n  = 24 for each RLBA) compared to children severely ill with acute COVID-19 (yellow; n  = 29 for each RLBA) and at-risk controls (blue; n  = 45 for each RLBA). Two-sided Mann-Whitney U testing performed (exact P  values shown in figure). e , Logistic regression ROC curves for classification of the independent MIS-C cohort ( n  = 24) versus at-risk controls ( n  = 45) (left) and the independent MIS-C ( n  = 24) cohort versus children severely ill with acute COVID-19 ( n  = 29) (right). f , Paired stripplots and boxplots showing SLBA enrichments (normalized antibody indices) in MIS-C patients ( n  = 182) and at-risk controls ( n  = 45) for the full 49 amino acid SNX8 wild-type (WT) polypeptide fragment (lavender) relative to the same SNX8 fragment with alanine mutagenesis of the [PSRMQMPQG] epitope (white). SNX8 WT fragment SLBA values are the means of technical replicates, SNX8 epitope mutagenesis values are from a single experiment. Two-sided Mann-Whitney U testing performed (exact P  values shown in figure). For all boxplots in the figure, the whiskers extend to 1.5 times the interquartile range (IQR) from the quartiles, the boxes represent the IQR, and centre lines represent the median.

Extended Data Fig. 3 HLA associations of SNX8 activated T cells and HLA binding characteristics of peptides containing SNX8/MADS shared epitope motif.

a , Stripplots and boxplots showing distribution of CD4 + , CD8 + , and total T cells which activate in response to either vehicle (culture media + 0.2% DMSO) or SNX8 peptide pool (SNX8 peptide + culture media + 0.2% DMSO) using AIM assay in MIS-C patients ( n  = 9) and controls ( n  = 10). Patient HLA type indicated by color of dot. HLA unpredicted means patient contained none of the MIS-C associated HLA types. Dotted line at 3 standard deviations above the mean of the SNX8 stimulated controls. Two-sided Mann-Whitney U testing was performed (exact P  values shown in figure). b , Computationally predicted HLA class I presentation scores (Immune Epitope Database; IEDB.org) for each possible peptide fragment of full-length SARS-CoV-2 N protein for each of the three MIS-C associated HLA types (A*02, B*35 and C*04) relative to a reference set of HLA-types encompassing over 99% of humans. Those fragments containing the MADS similarity region “LQLPQG” in orange. Data normally distributed; two-sided t-tests were performed (exact  P  values shown in figure). Percent of fragments within each specific HLA type with a score greater than 0.1 (likely to be presented) shown on right. c , Identical analysis but using full length SNX8 protein rather than SARS-CoV-2 NP, and the SNX8 similarity region “MGMPQG” rather than the MADS region “LQLPQG”. Data normally distributed; two-sided t-tests were performed (exact P  values shown in figure). d , HLA binding results from β2m folding assay for SARS-CoV-2 N and SNX8 peptides representative of two independent evaluations. Each peptide tested for binding in HLA-A*02:01, A*02:06, and B*35:01 class I monomers. Data presented as geometric mean fluorescence intensity (gMFI) of PE-conjugated anti-human β2m antibody staining of peptide-HLA monomers relative to negative (no peptide; unloaded HLA monomer) and positive (strong binding peptide; CMV pp65 495-503 [NLVPMVATV]) controls. For all boxplots in the figure, the whiskers extend to 1.5 times the interquartile range (IQR) from the quartiles, the boxes represent the IQR, and centre lines represent the median.

Extended Data Fig. 4 Identification, activation, and HLA restriction, of cross-reactive CD8+ T cells.

a , Gating strategy used to identify CD8 + T cells which bound to SNX8 epitope and/or MADS N protein epitope (CD8 + T cells positive for PE). Representative MIS-C patient and control showing each CD8 + T cell which bound to any tetramer (PE + ) and the relative binding of that T cell to both the SNX8 epitope (BV421 + ) and the MADS N protein epitope (APC + ) identifying cross-reactive T cells (PE + APC + BV421 + ). Schematics in panel a were created using BioRender ( https://www.biorender.com ). b , Stripplots and boxplots showing percentage of CD8 + T cells which are cross-reactive to both SNX8 and MADS in MIS-C patients ( n  = 3) and controls ( n  = 3). Insufficient numbers to perform robust statistical testing. c , Stripplots and boxplots showing percentage of total T cells which activate in response to either vehicle (culture media + 0.2% DMSO) or the SNX8 Epitope (SNX8 Epitope (Materials) + culture media + 0.2% DMSO) in MIS-C patients ( n  = 2) and at-risk controls ( n  = 4) measured by AIM assay. Insufficient numbers to perform robust statistical testing. Dotted line at 3 standard deviations above mean of SNX8 Epitope stimulated controls. d , TCRdist Similarity Network of 48 unique, paired TCRαβ sequences ( n  = 259 sequences) obtained from four patients with MIS-C. CD8 + T cells were sorted from PBMCs directly ex vivo or after 10-days of peptide expansion and staining with A*02:01 or A*02:06 HLA class I tetramers loaded with MADS [LQLPQGITL] and SNX8 [MQMPQGNPL] peptides. Each node represents a unique TCR clonotype. Edges connect nodes with a TCRdist score < 150. Dashed lines surround TCR similarity clusters. Node size corresponds to T cell clone size. Nodes are colored based on HLA restriction. TCRs selected for further testing are numbered TCR #1-8. Convergent node circled green. For all boxplots in the figure, the whiskers extend to 1.5 times the interquartile range (IQR) from the quartiles, the boxes represent the IQR, and centre lines represent the median.

Extended Data Fig. 5 Evaluation of Jurkat-TCR lines.

a , Jurkat-76 cells stably expressing putative cross-reactive TCRs (#1-8) stained with anti-murine TCRβ constant region (mTCRβc). Plots depict frequency of transduced (mCherry + ) Jurkat cells with presence of surface TCR (APC/Fire 750 + ) as a percentage of total live cells. b , Jurkat-TCR + cell lines expressing putative cross-reactive TCRs #1-8 stained with individual or combination of HLA-A*02:01 or A*02:06 tetramers loaded with MADS [LQLPQGITL] and SNX8 [MQMPQGNPL] peptides. Blue contour plots indicate staining with MADS-Tetramer (PE) and MADS-Tetramer (APC); purple contour plots indicate staining with SNX8-Tetramer (PE) and SNX8-Tetramer (BV421); red indicates combined staining with a pool of MADS/SNX8-Tetramer (PE), MADS-Tetramer (APC), and SNX8-Tetramer (BV421). Plots shown are gated from total PE + cells. Plots with confirmed cross-reactive TCRs outlined in red. c , Jurkat-TCR+ cell lines expressing putative cross-reactive TCRs #1-8 stained with individual HLA-A*02:01 or A*02:06 tetramers loaded with MADS Wuhan [LQLPQGTTL] peptide. Gate values indicate frequency of MADS-APC + cells as percentage of total MADS-PE + cells. Outliers shown in contour plots. Flow plots representative of two independent evaluations.

Extended Data Fig. 6 SNX8 expression during viral infection.

a , UMAPs showing SNX8 expression in various peripheral blood cell types during SARS-CoV-2 infection. b , Mean expression and percent of cells expressing SNX8 in peripheral blood subsets during SARS-CoV-2 infection. c , Mean expression and percent of cells expressing SNX8 averaged across all peripheral blood mononuclear cells from SARS-CoV-2 infected individuals without symptoms, with mild symptoms, or with severe disease compared to uninfected controls. d , Mean expression and percent of cells expressing SNX8 , OAS1 , OAS2 , and MAVS in peripheral blood subsets during SARS-CoV-2 infection. e , Relative expression of SNX8 , OAS1 , OAS2 , and MAVS during influenza virus infection compared to different severities of SARS-CoV-2 infection.

Extended Data Fig. 7 Representative flow cytometry gating.

a , Flow cytometry gating strategy for identifying CD4 positive and CD8 positive T cells for the AIM analysis with representative activation induced marker (AIM) assay flow cytometry gating strategy measuring percent of CD4 + T cells which activate (CD137 + OX40 + ) and percent of CD8 + T cells which activate (CD137 + CD69 + ) in response to SNX8 protein. b , Flow cytometry gating strategy for the initial SNX8/MADS tetramer cross-reactivity assay (Extended Data Fig. 4a,b ) showing isolation of PE-tetramer positive CD8 positive T cells. c , Flow cytometry plots showing results of serotyping for the PBMCs used in the initial SNX8/MADS tetramer cross-reactivity assay (Extended Data Fig. 4a,b ) which did not have sufficient cells for genotyping. Shown is the 1 MIS-C patient (far left) and 3 controls (middle 3) which are positive for HLA-A*02 and were used and one control negative for HLA-A*02 (far right) which was not used. d , Index sorting strategy for patient PBMCs from ex vivo and peptide expansion experiments for TCR sequencing. Single cells were sorted from live/lineage (CD4, CD14, CD16, CD19)-negative, CD3 + CD8 + T lymphocytes positive for MADS/SNX8-Tetramer (PE) and MADS-Tetramer (APC) and/or SNX8-Tetramer (BV421). e , Flow cytometry gating strategy to evaluate putative cross-reactive Jurkat-TCRs. Gates include single, live, transduced Jurkat lymphocytes triple positive for MADS/SNX8-(PE), MADS-(APC), and SNX8-(BV421) tetramers shown in Fig. 4 .

Supplementary information

Supplementary table 1.

The complete set of primers used for the single-cell T cell receptor (TCR) sequencing by nested multiplex polymerase chain reaction (PCR).

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Peer review file, source data, source data fig. 1, source data fig. 2, source data fig. 3, source data fig. 4, source data extended data fig. 1, source data extended data fig. 2, source data extended data fig. 3, source data extended data fig. 4, source data extended data fig. 5, rights and permissions.

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Bodansky, A., Mettelman, R.C., Sabatino, J.J. et al. Molecular mimicry in multisystem inflammatory syndrome in children. Nature (2024). https://doi.org/10.1038/s41586-024-07722-4

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