Research methodology vs. research methods
The research methodology or design is the overall strategy and rationale that you used to carry out the research. Whereas, research methods are the specific tools and processes you use to gather and understand the data you need to test your hypothesis.
To further understand research methodology, let’s explore some examples of research methodology:
a. Qualitative research methodology example: A study exploring the impact of author branding on author popularity might utilize in-depth interviews to gather personal experiences and perspectives.
b. Quantitative research methodology example: A research project investigating the effects of a book promotion technique on book sales could employ a statistical analysis of profit margins and sales before and after the implementation of the method.
c. Mixed-Methods research methodology example: A study examining the relationship between social media use and academic performance might combine both qualitative and quantitative approaches. It could include surveys to quantitatively assess the frequency of social media usage and its correlation with grades, alongside focus groups or interviews to qualitatively explore students’ perceptions and experiences regarding how social media affects their study habits and academic engagement.
These examples highlight the meaning of methodology in research and how it guides the research process, from data collection to analysis, ensuring the study’s objectives are met efficiently.
When it comes to writing your study, the methodology in research papers or a dissertation plays a pivotal role. A well-crafted methodology section of a research paper or thesis not only enhances the credibility of your research but also provides a roadmap for others to replicate or build upon your work.
Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:
At the start of a research paper , you would have provided the background of your research and stated your hypothesis or research problem. In this section, you will elaborate on your research strategy.
Begin by restating your research question and proceed to explain what type of research you opted for to test it. Depending on your research, here are some questions you can consider:
a. Did you use qualitative or quantitative data to test the hypothesis?
b. Did you perform an experiment where you collected data or are you writing a dissertation that is descriptive/theoretical without data collection?
c. Did you use primary data that you collected or analyze secondary research data or existing data as part of your study?
These questions will help you establish the rationale for your study on a broader level, which you will follow by elaborating on the specific methods you used to collect and understand your data.
Now that you have told your reader what type of research you’ve undertaken for the dissertation, it’s time to dig into specifics. State what specific methods you used and explain the conditions and variables involved. Explain what the theoretical framework behind the method was, what samples you used for testing it, and what tools and materials you used to collect the data.
Once you have explained the data collection process, explain how you analyzed and studied the data. Here, your focus is simply to explain the methods of analysis rather than the results of the study.
Here are some questions you can answer at this stage:
a. What tools or software did you use to analyze your results?
b. What parameters or variables did you consider while understanding and studying the data you’ve collected?
c. Was your analysis based on a theoretical framework?
Your mode of analysis will change depending on whether you used a quantitative or qualitative research methodology in your study. If you’re working within the hard sciences or physical sciences, you are likely to use a quantitative research methodology (relying on numbers and hard data). If you’re doing a qualitative study, in the social sciences or humanities, your analysis may rely on understanding language and socio-political contexts around your topic. This is why it’s important to establish what kind of study you’re undertaking at the onset.
Now that you have gone through your research process in detail, you’ll also have to make a case for it. Justify your choice of methodology and methods, explaining why it is the best choice for your research question. This is especially important if you have chosen an unconventional approach or you’ve simply chosen to study an existing research problem from a different perspective. Compare it with other methodologies, especially ones attempted by previous researchers, and discuss what contributions using your methodology makes.
No matter how thorough a methodology is, it doesn’t come without its hurdles. This is a natural part of scientific research that is important to document so that your peers and future researchers are aware of it. Writing in a research paper about this aspect of your research process also tells your evaluator that you have actively worked to overcome the pitfalls that came your way and you have refined the research process.
1. Remember who you are writing for. Keeping sight of the reader/evaluator will help you know what to elaborate on and what information they are already likely to have. You’re condensing months’ work of research in just a few pages, so you should omit basic definitions and information about general phenomena people already know.
2. Do not give an overly elaborate explanation of every single condition in your study.
3. Skip details and findings irrelevant to the results.
4. Cite references that back your claim and choice of methodology.
5. Consistently emphasize the relationship between your research question and the methodology you adopted to study it.
To sum it up, what is methodology in research? It’s the blueprint of your research, essential for ensuring that your study is systematic, rigorous, and credible. Whether your focus is on qualitative research methodology, quantitative research methodology, or a combination of both, understanding and clearly defining your methodology is key to the success of your research.
Once you write the research methodology and complete writing the entire research paper, the next step is to edit your paper. As experts in research paper editing and proofreading services , we’d love to help you perfect your paper!
Here are some other articles that you might find useful:
Found this article helpful?
This is very simplified and direct. Very helpful to understand the research methodology section of a dissertation
Leave a Comment: Cancel reply
Your email address will not be published.
Your organization needs a technical editor: here’s why, your guide to the best ebook readers in 2024, writing for the web: 7 expert tips for web content writing.
Subscribe to our Newsletter
Get carefully curated resources about writing, editing, and publishing in the comfort of your inbox.
How to Copyright Your Book?
If you’ve thought about copyrighting your book, you’re on the right path.
© 2024 All rights reserved
Terms & conditions.
As the Christmas season is upon us, we find ourselves reflecting on the past year and those who we have helped to shape their future. It’s been quite a year for us all! The end of the year brings no greater joy than the opportunity to express to you Christmas greetings and good wishes.
At this special time of year, Research Prospect brings joyful discount of 10% on all its services. May your Christmas and New Year be filled with joy.
We are looking back with appreciation for your loyalty and looking forward to moving into the New Year together.
"Claim this offer"
In unfamiliar and hard times, we have stuck by you. This Christmas, Research Prospect brings you all the joy with exciting discount of 10% on all its services.
Offer valid till 5-1-2024
We love being your partner in success. We know you have been working hard lately, take a break this holiday season to spend time with your loved ones while we make sure you succeed in your academics
Discount code: RP23720
Published by Nicolas at March 21st, 2024 , Revised On March 12, 2024
Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one.
Table of Contents
Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings.
Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.
Here is why Research methodology is important in academic and professional settings.
Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.
In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.
In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.
This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.
For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.
Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.
In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount.
Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions.
Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.
Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:
To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.
Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:
Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:
Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:
Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions.
Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.
Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.
Key Characteristics:
Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.
Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.
Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.
Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.
Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.
A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.
Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.
Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.
Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.
Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.
Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.
Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.
Exploratory design.
Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.
Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.
Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.
Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.
Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.
Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.
Cross-sectional design.
Cross-sectional designs collect data from participants at a single point in time.
Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.
Longitudinal designs involve the collection of data from the same participants over an extended period.
Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.
Experimental design.
Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.
Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.
Non-experimental designs observe and describe phenomena without manipulating variables.
Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.
Effective data collection is fundamental to the success of any research endeavour.
Objective Design:
Structured Format:
Pilot Testing:
Sampling Strategy:
Establishing Rapport:
Open-Ended Questions:
Active Listening:
Ethical Considerations:
1. participant observation.
Immersive Participation:
Field Notes:
Ethical Awareness:
Objective Observation:
Data Reliability:
Contextual Understanding:
1. using existing data.
Identifying Relevant Archives:
Data Verification:
Ethical Use:
Incomplete or Inaccurate Archives:
Temporal Bias:
Access Limitations:
Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.
Sampling bias:.
Measurement error:.
Timeline pressures:.
Selection bias:.
Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:
Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.
A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.
A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.
Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.
Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.
Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.
Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.
What is the research methodology.
Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.
Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.
To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.
In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.
Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.
Want to score band 9 on your IELTS essay? Here are some tips and IELTS sample essays to help you get the score you want.
Learn everything about meta synthesis literature review in this comprehensive guide. From definition and process to its types and challenges.
Craft a compelling scholarship motivation letter by showcasing your passion, achievements, and future goals concisely and impactfully.
Ready to place an order?
Learning resources.
Run a free plagiarism check in 10 minutes, automatically generate references for free.
Published on 25 February 2019 by Shona McCombes . Revised on 10 October 2022.
Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
It should include:
Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.
How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, frequently asked questions about methodology.
The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.
Correct my document today
Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .
It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.
You can start by introducing your overall approach to your research. You have two options here.
What research problem or question did you investigate?
And what type of data did you need to achieve this aim?
Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?
Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .
In order to be considered generalisable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.
Here, explain how you operationalised your concepts and measured your variables. Discuss your sampling method or inclusion/exclusion criteria, as well as any tools, procedures, and materials you used to gather your data.
Surveys Describe where, when, and how the survey was conducted.
Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.
Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.
The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.
The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on 4–8 July 2022, between 11:00 and 15:00.
Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.
In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.
Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)
Interviews or focus groups Describe where, when, and how the interviews were conducted.
Participant observation Describe where, when, and how you conducted the observation or ethnography .
Existing data Explain how you selected case study materials for your analysis.
In order to gain better insight into possibilities for future improvement of the fitness shop’s product range, semi-structured interviews were conducted with 8 returning customers.
Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.
Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.
Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.
Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods here.
Next, you should indicate how you processed and analysed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.
In quantitative research , your analysis will be based on numbers. In your methods section, you can include:
In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).
Specific methods might include:
Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.
Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.
In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .
Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.
The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .
Your methodology can be strengthened by referencing existing research in your field. This can help you to:
Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.
Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.
Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your objectives.
Methods are the specific tools and procedures you use to collect and analyse data (e.g. interviews, experiments , surveys , statistical tests ).
In a dissertation or scientific paper, the methodology chapter or methods section comes after the introduction and before the results , discussion and conclusion .
Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.
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.
If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.
McCombes, S. (2022, October 10). What Is a Research Methodology? | Steps & Tips. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/thesis-dissertation/methodology/
Other students also liked, how to write a dissertation proposal | a step-by-step guide, what is a literature review | guide, template, & examples, what is a theoretical framework | a step-by-step guide.
Tio Gabunia (B.Arch, M.Arch)
Tio Gabunia is an academic writer and architect based in Tbilisi. He has studied architecture, design, and urban planning at the Georgian Technical University and the University of Lisbon. He has worked in these fields in Georgia, Portugal, and France. Most of Tio’s writings concern philosophy. Other writings include architecture, sociology, urban planning, and economics.
Learn about our Editorial Process
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]
Research methodologies can roughly be categorized into three group: quantitative, qualitative, and mixed-methods.
Below are research methodologies that fit into each category.
1. case study.
Conducts an in-depth examination of a specific case, individual, or event to understand a phenomenon.
Instead of examining a whole population for numerical trend data, case study researchers seek in-depth explanations of one event.
The benefit of case study research is its ability to elucidate overlooked details of interesting cases of a phenomenon (Busetto, Wick & Gumbinger, 2020). It offers deep insights for empathetic, reflective, and thoughtful understandings of that phenomenon.
However, case study findings aren’t transferrable to new contexts or for population-wide predictions. Instead, they inform practitioner understandings for nuanced, deep approaches to future instances (Liamputtong, 2020).
Grounded theory involves generating hypotheses and theories through the collection and interpretation of data (Faggiolani, n.d.). Its distinguishing features is that it doesn’t test a hypothesis generated prior to analysis, but rather generates a hypothesis or ‘theory’ that emerges from the data.
It also involves the application of inductive reasoning and is often contrasted with the hypothetico-deductive model of scientific research. This research methodology was developed by Barney Glaser and Anselm Strauss in the 1960s (Glaser & Strauss, 2009).
The basic difference between traditional scientific approaches to research and grounded theory is that the latter begins with a question, then collects data, and the theoretical framework is said to emerge later from this data.
By contrast, scientists usually begin with an existing theoretical framework , develop hypotheses, and only then start collecting data to verify or falsify the hypotheses.
In ethnographic research , the researcher immerses themselves within the group they are studying, often for long periods of time.
This type of research aims to understand the shared beliefs, practices, and values of a particular community by immersing the researcher within the cultural group.
Although ethnographic research cannot predict or identify trends in an entire population, it can create detailed explanations of cultural practices and comparisons between social and cultural groups.
When a person conducts an ethnographic study of themselves or their own culture, it can be considered autoethnography .
Its strength lies in producing comprehensive accounts of groups of people and their interactions.
Common methods researchers use during an ethnographic study include participant observation , thick description, unstructured interviews, and field notes vignettes. These methods can provide detailed and contextualized descriptions of their subjects.
Example Study
Liquidated: An Ethnography of Wall Street by Karen Ho involves an anthropologist who embeds herself with Wall Street firms to study the culture of Wall Street bankers and how this culture affects the broader economy and world.
Phenomenology to understand and describe individuals’ lived experiences concerning a specific phenomenon.
As a research methodology typically used in the social sciences , phenomenology involves the study of social reality as a product of intersubjectivity (the intersection of people’s cognitive perspectives) (Zahavi & Overgaard, n.d.).
This philosophical approach was first developed by Edmund Husserl.
Narrative research explores personal stories and experiences to understand their meanings and interpretations.
It is also known as narrative inquiry and narrative analysis(Riessman, 1993).
This approach to research uses qualitative material like journals, field notes, letters, interviews, texts, photos, etc., as its data.
It is aimed at understanding the way people create meaning through narratives (Clandinin & Connelly, 2004).
A discourse analysis examines the structure, patterns, and functions of language in context to understand how the text produces social constructs.
This methodology is common in critical theory , poststructuralism , and postmodernism. Its aim is to understand how language constructs discourses (roughly interpreted as “ways of thinking and constructing knowledge”).
As a qualitative methodology , its focus is on developing themes through close textual analysis rather than using numerical methods. Common methods for extracting data include semiotics and linguistic analysis.
Action research involves researchers working collaboratively with stakeholders to address problems, develop interventions, and evaluate effectiveness.
Action research is a methodology and philosophy of research that is common in the social sciences.
The term was first coined in 1944 by Kurt Lewin, a German-American psychologist who also introduced applied research and group communication (Altrichter & Gstettner, 1993).
Lewin originally defined action research as involving two primary processes: taking action and doing research (Lewin, 1946).
Action research involves planning, action, and information-seeking about the result of the action.
Since Lewin’s original formulation, many different theoretical approaches to action research have been developed. These include action science, participatory action research, cooperative inquiry, and living educational theory among others.
Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing (Ellison & Drew, 2019) is a study conducted by a school teacher who used video games to help teach his students English. It involved action research, where he interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience, and iterated on his teaching style based on their feedback (disclaimer: I am the second author of this study).
See More: Examples of Qualitative Research
8. experimental design.
As the name suggests, this type of research is based on testing hypotheses in experimental settings by manipulating variables and observing their effects on other variables.
The main benefit lies in its ability to manipulate specific variables to determine their effect on outcomes which is a great method for those looking for causational links in their research.
This is common, for example, in high-school science labs, where students are asked to introduce a variable into a setting in order to examine its effect.
Non-experimental design observes and measures associations between variables without manipulating them.
It can take, for example, the form of a ‘fly on the wall’ observation of a phenomenon, allowing researchers to examine authentic settings and changes that occur naturally in the environment.
Cross-sectional design involves analyzing variables pertaining to a specific time period and at that exact moment.
This approach allows for an extensive examination and comparison of distinct and independent subjects, thereby offering advantages over qualitative methodologies such as case studies or surveys.
While cross-sectional design can be extremely useful in taking a ‘snapshot in time’, as a standalone method, it is not useful for examining changes in subjects after an intervention. The next methodology addresses this issue.
The prime example of this type of study is a census. A population census is mailed out to every house in the country, and each household must complete the census on the same evening. This allows the government to gather a snapshot of the nation’s demographics, beliefs, religion, and so on.
Longitudinal research gathers data from the same subjects over an extended period to analyze changes and development.
In contrast to cross-sectional tactics, longitudinal designs examine variables more than once, over a pre-determined time span, allowing for multiple data points to be taken at different times.
A cross-sectional design is also useful for examining cohort effects , by comparing differences or changes in multiple different generations’ beliefs over time.
With multiple data points collected over extended periods ,it’s possible to examine continuous changes within things like population dynamics or consumer behavior. This makes detailed analysis of change possible.
Quasi-experimental design involves manipulating variables for analysis, but uses pre-existing groups of subjects rather than random groups.
Because the groups of research participants already exist, they cannot be randomly assigned to a cohort as with a true experimental design study. This makes inferring a causal relationship more difficult, but is nonetheless often more feasible in real-life settings.
Quasi-experimental designs are generally considered inferior to true experimental designs.
Correlational research examines the relationships between two or more variables, determining the strength and direction of their association.
Similar to quasi-experimental methods, this type of research focuses on relationship differences between variables.
This approach provides a fast and easy way to make initial hypotheses based on either positive or negative correlation trends that can be observed within dataset.
Methods used for data analysis may include statistic correlations such as Pearson’s or Spearman’s.
14. sequential explanatory design (quan→qual).
This methodology involves conducting quantitative analysis first, then supplementing it with a qualitative study.
It begins by collecting quantitative data that is then analyzed to determine any significant patterns or trends.
Secondly, qualitative methods are employed. Their intent is to help interpret and expand the quantitative results.
This offers greater depth into understanding both large and smaller aspects of research questions being addressed.
The rationale behind this approach is to ensure that your data collection generates richer context for gaining insight into the particular issue across different levels, integrating in one study, qualitative exploration as well as statistical procedures.
This methodology goes in the other direction, starting with qualitative analysis and ending with quantitative analysis.
It starts with qualitative research that delves deeps into complex areas and gathers rich information through interviewing or observing participants.
After this stage of exploration comes to an end, quantitative techniques are used to analyze the collected data through inferential statistics.
The idea is that a qualitative study can arm the researchers with a strong hypothesis testing framework, which they can then apply to a larger sample size using qualitative methods.
When I first took research classes, I had a lot of trouble distinguishing between methodologies and methods.
The key is to remember that the methodology sets the direction, while the methods are the specific tools to be used. A good analogy is transport: first you need to choose a mode (public transport, private transport, motorized transit, non-motorized transit), then you can choose a tool (bus, car, bike, on foot).
While research methodologies can be split into three types, each type has many different nuanced methodologies that can be chosen, before you then choose the methods – or tools – to use in the study. Each has its own strengths and weaknesses, so choose wisely!
Altrichter, H., & Gstettner, P. (1993). Action Research: A closed chapter in the history of German social science? Educational Action Research , 1 (3), 329–360. https://doi.org/10.1080/0965079930010302
Audi, R. (1999). The Cambridge dictionary of philosophy . Cambridge ; New York : Cambridge University Press. http://archive.org/details/cambridgediction00audi
Clandinin, D. J., & Connelly, F. M. (2004). Narrative Inquiry: Experience and Story in Qualitative Research . John Wiley & Sons.
Creswell, J. W. (2008). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall.
Faggiolani, C. (n.d.). Perceived Identity: Applying Grounded Theory in Libraries . https://doi.org/10.4403/jlis.it-4592
Gauch, H. G. (2002). Scientific Method in Practice . Cambridge University Press.
Glaser, B. G., & Strauss, A. L. (2009). The Discovery of Grounded Theory: Strategies for Qualitative Research . Transaction Publishers.
Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New Age International.
Kuada, J. (2012). Research Methodology: A Project Guide for University Students . Samfundslitteratur.
Lewin, K. (1946). Action research and minority problems. Journal of Social Issues , 2, 4 , 34–46. https://doi.org/10.1111/j.1540-4560.1946.tb02295.x
Mills, J., Bonner, A., & Francis, K. (2006). The Development of Constructivist Grounded Theory. International Journal of Qualitative Methods , 5 (1), 25–35. https://doi.org/10.1177/160940690600500103
Mingers, J., & Willcocks, L. (2017). An integrative semiotic methodology for IS research. Information and Organization , 27 (1), 17–36. https://doi.org/10.1016/j.infoandorg.2016.12.001
OECD. (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development . Organisation for Economic Co-operation and Development. https://www.oecd-ilibrary.org/science-and-technology/frascati-manual-2015_9789264239012-en
Peirce, C. S. (1992). The Essential Peirce, Volume 1: Selected Philosophical Writings (1867–1893) . Indiana University Press.
Reese, W. L. (1980). Dictionary of Philosophy and Religion: Eastern and Western Thought . Humanities Press.
Riessman, C. K. (1993). Narrative analysis . Sage Publications, Inc.
Saussure, F. de, & Riedlinger, A. (1959). Course in General Linguistics . Philosophical Library.
Thomas, C. G. (2021). Research Methodology and Scientific Writing . Springer Nature.
Zahavi, D., & Overgaard, S. (n.d.). Phenomenological Sociology—The Subjectivity of Everyday Life .
Your email address will not be published. Required fields are marked *
The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.
Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.
You must explain how you obtained and analyzed your results for the following reasons:
Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.
I. Groups of Research Methods
There are two main groups of research methods in the social sciences:
II. Content
The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.
The remainder of your methodology section should describe the following:
In addition, an effectively written methodology section should:
NOTE: Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.
ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.
YET ANOTHER NOTE: If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.
III. Problems to Avoid
Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.
Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.
Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.
Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].
It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.
Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.
Statistical Designs and Tests? Do Not Fear Them!
Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.
To locate data and statistics, GO HERE .
Knowing the Relationship Between Theories and Methods
There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.
Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.
Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.
Methods and the Methodology
Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].
The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.
Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.
Home » Research Methods – Types, Examples and Guide
Table of Contents
Definition:
Research Methods refer to the techniques, procedures, and processes used by researchers to collect , analyze, and interpret data in order to answer research questions or test hypotheses. The methods used in research can vary depending on the research questions, the type of data that is being collected, and the research design.
Types of Research Methods are as follows:
Qualitative research methods are used to collect and analyze non-numerical data. This type of research is useful when the objective is to explore the meaning of phenomena, understand the experiences of individuals, or gain insights into complex social processes. Qualitative research methods include interviews, focus groups, ethnography, and content analysis.
Quantitative research methods are used to collect and analyze numerical data. This type of research is useful when the objective is to test a hypothesis, determine cause-and-effect relationships, and measure the prevalence of certain phenomena. Quantitative research methods include surveys, experiments, and secondary data analysis.
Mixed Method Research refers to the combination of both qualitative and quantitative research methods in a single study. This approach aims to overcome the limitations of each individual method and to provide a more comprehensive understanding of the research topic. This approach allows researchers to gather both quantitative data, which is often used to test hypotheses and make generalizations about a population, and qualitative data, which provides a more in-depth understanding of the experiences and perspectives of individuals.
The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods
Research Method | Quantitative | Qualitative | Mixed Methods |
---|---|---|---|
To measure and quantify variables | To understand the meaning and complexity of phenomena | To integrate both quantitative and qualitative approaches | |
Typically focused on testing hypotheses and determining cause and effect relationships | Typically exploratory and focused on understanding the subjective experiences and perspectives of participants | Can be either, depending on the research design | |
Usually involves standardized measures or surveys administered to large samples | Often involves in-depth interviews, observations, or analysis of texts or other forms of data | Usually involves a combination of quantitative and qualitative methods | |
Typically involves statistical analysis to identify patterns and relationships in the data | Typically involves thematic analysis or other qualitative methods to identify themes and patterns in the data | Usually involves both quantitative and qualitative analysis | |
Can provide precise, objective data that can be generalized to a larger population | Can provide rich, detailed data that can help understand complex phenomena in depth | Can combine the strengths of both quantitative and qualitative approaches | |
May not capture the full complexity of phenomena, and may be limited by the quality of the measures used | May be subjective and may not be generalizable to larger populations | Can be time-consuming and resource-intensive, and may require specialized skills | |
Typically focused on testing hypotheses and determining cause-and-effect relationships | Surveys, experiments, correlational studies | Interviews, focus groups, ethnography | Sequential explanatory design, convergent parallel design, explanatory sequential design |
Examples of Research Methods are as follows:
Qualitative Research Example:
A researcher wants to study the experience of cancer patients during their treatment. They conduct in-depth interviews with patients to gather data on their emotional state, coping mechanisms, and support systems.
Quantitative Research Example:
A company wants to determine the effectiveness of a new advertisement campaign. They survey a large group of people, asking them to rate their awareness of the product and their likelihood of purchasing it.
Mixed Research Example:
A university wants to evaluate the effectiveness of a new teaching method in improving student performance. They collect both quantitative data (such as test scores) and qualitative data (such as feedback from students and teachers) to get a complete picture of the impact of the new method.
Research methods are used in various fields to investigate, analyze, and answer research questions. Here are some examples of how research methods are applied in different fields:
Research methods serve several purposes, including:
Research methods are used when you need to gather information or data to answer a question or to gain insights into a particular phenomenon.
Here are some situations when research methods may be appropriate:
Research methods provide several advantages, including:
Researcher, Academic Writer, Web developer
Research methodology involves a systematic and well-structured approach to conducting scholarly or scientific inquiries. Knowing the significance of research methodology and its different components is crucial as it serves as the basis for any study.
Typically, your research topic will start as a broad idea you want to investigate more thoroughly. Once you’ve identified a research problem and created research questions , you must choose the appropriate methodology and frameworks to address those questions effectively.
Research methodology is the process or the way you intend to execute your study. The methodology section of a research paper outlines how you plan to conduct your study. It covers various steps such as collecting data, statistical analysis, observing participants, and other procedures involved in the research process
The methods section should give a description of the process that will convert your idea into a study. Additionally, the outcomes of your process must provide valid and reliable results resonant with the aims and objectives of your research. This thumb rule holds complete validity, no matter whether your paper has inclinations for qualitative or quantitative usage.
Studying research methods used in related studies can provide helpful insights and direction for your own research. Now easily discover papers related to your topic on SciSpace and utilize our AI research assistant, Copilot , to quickly review the methodologies applied in different papers.
While deciding on your approach towards your research, the reason or factors you weighed in choosing a particular problem and formulating a research topic need to be validated and explained. A research methodology helps you do exactly that. Moreover, a good research methodology lets you build your argument to validate your research work performed through various data collection methods, analytical methods, and other essential points.
Just imagine it as a strategy documented to provide an overview of what you intend to do.
While undertaking any research writing or performing the research itself, you may get drifted in not something of much importance. In such a case, a research methodology helps you to get back to your outlined work methodology.
A research methodology helps in keeping you accountable for your work. Additionally, it can help you evaluate whether your work is in sync with your original aims and objectives or not. Besides, a good research methodology enables you to navigate your research process smoothly and swiftly while providing effective planning to achieve your desired results.
Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:
Explain what research methods you’re going to use. Whether you intend to proceed with quantitative or qualitative, or a composite of both approaches, you need to state that explicitly. The option among the three depends on your research’s aim, objectives, and scope.
Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome.
The mechanism encompasses the research methods or instruments you will use to develop your research methodology. It usually refers to your data collection methods. You can use interviews, surveys, physical questionnaires, etc., of the many available mechanisms as research methodology instruments. The data collection method is determined by the type of research and whether the data is quantitative data(includes numerical data) or qualitative data (perception, morale, etc.) Moreover, you need to put logical reasoning behind choosing a particular instrument.
The results will be available once you have finished experimenting. However, you should also explain how you plan to use the data to interpret the findings. This section also aids in understanding the problem from within, breaking it down into pieces, and viewing the research problem from various perspectives.
Anything that you feel must be explained to spread more awareness among readers and focus groups must be included and described in detail. You should not just specify your research methodology on the assumption that a reader is aware of the topic.
All the relevant information that explains and simplifies your research paper must be included in the methodology section. If you are conducting your research in a non-traditional manner, give a logical justification and list its benefits.
Include information about the sample and sample space in the methodology section. The term "sample" refers to a smaller set of data that a researcher selects or chooses from a larger group of people or focus groups using a predetermined selection method. Let your readers know how you are going to distinguish between relevant and non-relevant samples. How you figured out those exact numbers to back your research methodology, i.e. the sample spacing of instruments, must be discussed thoroughly.
For example, if you are going to conduct a survey or interview, then by what procedure will you select the interviewees (or sample size in case of surveys), and how exactly will the interview or survey be conducted.
This part, which is frequently assumed to be unnecessary, is actually very important. The challenges and limitations that your chosen strategy inherently possesses must be specified while you are conducting different types of research.
You must have observed that all research papers, dissertations, or theses carry a chapter entirely dedicated to research methodology. This section helps maintain your credibility as a better interpreter of results rather than a manipulator.
A good research methodology always explains the procedure, data collection methods and techniques, aim, and scope of the research. In a research study, it leads to a well-organized, rationality-based approach, while the paper lacking it is often observed as messy or disorganized.
You should pay special attention to validating your chosen way towards the research methodology. This becomes extremely important in case you select an unconventional or a distinct method of execution.
Curating and developing a strong, effective research methodology can assist you in addressing a variety of situations, such as:
As a researcher, you must choose which tools or data collection methods that fit best in terms of the relevance of your research. This decision has to be wise.
There exists many research equipments or tools that you can use to carry out your research process. These are classified as:
An interview aimed to get your desired research outcomes can be undertaken in many different ways. For example, you can design your interview as structured, semi-structured, or unstructured. What sets them apart is the degree of formality in the questions. On the other hand, in a group interview, your aim should be to collect more opinions and group perceptions from the focus groups on a certain topic rather than looking out for some formal answers.
In surveys, you are in better control if you specifically draft the questions you seek the response for. For example, you may choose to include free-style questions that can be answered descriptively, or you may provide a multiple-choice type response for questions. Besides, you can also opt to choose both ways, deciding what suits your research process and purpose better.
Similar to the group interviews, here, you can select a group of individuals and assign them a topic to discuss or freely express their opinions over that. You can simultaneously note down the answers and later draft them appropriately, deciding on the relevance of every response.
If your research domain is humanities or sociology, observations are the best-proven method to draw your research methodology. Of course, you can always include studying the spontaneous response of the participants towards a situation or conducting the same but in a more structured manner. A structured observation means putting the participants in a situation at a previously decided time and then studying their responses.
Of all the tools described above, it is you who should wisely choose the instruments and decide what’s the best fit for your research. You must not restrict yourself from multiple methods or a combination of a few instruments if appropriate in drafting a good research methodology.
A research methodology exists in various forms. Depending upon their approach, whether centered around words, numbers, or both, methodologies are distinguished as qualitative, quantitative, or an amalgamation of both.
When a research methodology primarily focuses on words and textual data, then it is generally referred to as qualitative research methodology. This type is usually preferred among researchers when the aim and scope of the research are mainly theoretical and explanatory.
The instruments used are observations, interviews, and sample groups. You can use this methodology if you are trying to study human behavior or response in some situations. Generally, qualitative research methodology is widely used in sociology, psychology, and other related domains.
If your research is majorly centered on data, figures, and stats, then analyzing these numerical data is often referred to as quantitative research methodology. You can use quantitative research methodology if your research requires you to validate or justify the obtained results.
In quantitative methods, surveys, tests, experiments, and evaluations of current databases can be advantageously used as instruments If your research involves testing some hypothesis, then use this methodology.
As the name suggests, the amalgam methodology uses both quantitative and qualitative approaches. This methodology is used when a part of the research requires you to verify the facts and figures, whereas the other part demands you to discover the theoretical and explanatory nature of the research question.
The instruments for the amalgam methodology require you to conduct interviews and surveys, including tests and experiments. The outcome of this methodology can be insightful and valuable as it provides precise test results in line with theoretical explanations and reasoning.
The amalgam method, makes your work both factual and rational at the same time.
If you have kept your sincerity and awareness intact with the aims and scope of research well enough, you must have got an idea of which research methodology suits your work best.
Before deciding which research methodology answers your research question, you must invest significant time in reading and doing your homework for that. Taking references that yield relevant results should be your first approach to establishing a research methodology.
Moreover, you should never refrain from exploring other options. Before setting your work in stone, you must try all the available options as it explains why the choice of research methodology that you finally make is more appropriate than the other available options.
You should always go for a quantitative research methodology if your research requires gathering large amounts of data, figures, and statistics. This research methodology will provide you with results if your research paper involves the validation of some hypothesis.
Whereas, if you are looking for more explanations, reasons, opinions, and public perceptions around a theory, you must use qualitative research methodology.The choice of an appropriate research methodology ultimately depends on what you want to achieve through your research.
1. how to write a research methodology.
You can always provide a separate section for research methodology where you should specify details about the methods and instruments used during the research, discussions on result analysis, including insights into the background information, and conveying the research limitations.
There generally exists four types of research methodology i.e.
The set of techniques or procedures followed to discover and analyze the information gathered to validate or justify a research outcome is generally called Research Methodology.
Your research methodology directly reflects the validity of your research outcomes and how well-informed your research work is. Moreover, it can help future researchers cite or refer to your research if they plan to use a similar research methodology.
Reference management. Clean and simple.
Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.
When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.
If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.
Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:
A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.
You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.
In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.
The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.
Think of it like writing a plan or an outline for you what you intend to do.
When carrying out research, it can be easy to go off-track or depart from your standard methodology.
Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.
With all that said, how do you write out your standard approach to a research methodology?
As a general plan, your methodology should include the following information:
In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.
A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.
You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.
Having a sound methodology in place can also help you with the following:
A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.
The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.
There are many different research instruments you can use in collecting data for your research.
Generally, they can be grouped as follows:
These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.
It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.
There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.
Data type | What is it? | Methodology |
---|---|---|
Quantitative | This methodology focuses more on measuring and testing numerical data. What is the aim of quantitative research? | Surveys, tests, existing databases. |
Qualitative | Qualitative research is a process of collecting and analyzing both words and textual data. | Observations, interviews, focus groups. |
Mixed-method | A mixed-method approach combines both of the above approaches. | Where you can use a mixed method of research, this can produce some incredibly interesting results. This is due to testing in a way that provides data that is both proven to be exact while also being exploratory at the same time. |
➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!
If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.
It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.
Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.
If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.
If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.
It helps to always bring things back to the question: what do I want to achieve with my research?
Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:
➡️ How to do a content analysis
➡️ How to do a thematic analysis
➡️ How to do a rhetorical analysis
Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.
Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.
Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.
Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.
The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.
Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.
Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project. The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data. The following boxes below contain a list of quantitative, qualitative, and mixed research methods.
Constructing Questionnaires
When constructing your questions for a survey or questionnaire, there are things you can do to ensure that your questions are accurate and easy to understand (Dawson, 2019):
Quantitative Research Measures
When you are considering a quantitative approach to your research, you need to identify why types of measures you will use in your study. This will determine what type of numbers you will be using to collect your data. There are four levels of measurement:
Focus Groups
This is when a select group of people gather to talk about a particular topic. They can also be called discussion groups or group interviews (Dawson, 2019). They are usually lead by a moderator to help guide the discussion and ask certain questions. It is critical that a moderator allows everyone in the group to get a chance to speak so that no one dominates the discussion. The data that are gathered from focus groups tend to be thoughts, opinions, and perspectives about an issue.
Advantages of Focus Groups
Disadvantages of Focus Groups
Observation
There are two ways to conduct research observations:
Open-Ended Questionnaires
These types of questionnaires are the opposite of "multiple choice" questionnaires because the answer boxes are left open for the participant to complete. This means that participants can write short or extended answers to the questions. Upon gathering the responses, researchers will often "quantify" the data by organizing the responses into different categories. This can be time consuming because the researcher needs to read all responses carefully.
Semi-structured Interviews
This is the most common type of interview where researchers aim to get specific information so they can compare it to other interview data. This requires asking the same questions for each interview, but keeping their responses flexible. This means including follow-up questions if a subject answers a certain way. Interview schedules are commonly used to aid the interviewers, which list topics or questions that will be discussed at each interview (Dawson, 2019).
Theoretical Analysis
Often used for nonhuman research, theoretical analysis is a qualitative approach where the researcher applies a theoretical framework to analyze something about their topic. A theoretical framework gives the researcher a specific "lens" to view the topic and think about it critically. it also serves as context to guide the entire study. This is a popular research method for analyzing works of literature, films, and other forms of media. You can implement more than one theoretical framework with this method, as many theories complement one another.
Common theoretical frameworks for qualitative research are (Grant and Osanloo, 2014):
Unstructured Interviews
These are in-depth interviews where the researcher tries to understand an interviewee's perspective on a situation or issue. They are sometimes called life history interviews. It is important not to bombard the interviewee with too many questions so they can freely disclose their thoughts (Dawson, 2019).
Other mixed method approaches that incorporate quantitative and qualitative research methods depend heavily on the research topic. It is strongly recommended that you collaborate with your academic advisor before finalizing a mixed method approach.
How do you determine which research method would be best for your proposal? This heavily depends on your research objective. According to Dawson (2019), there are several questions to ask yourself when determining the best research method for your project:
Natural Experiments: Missed Opportunities for Causal Inference in Psychology Michael P. Grosz, Adam Ayaita, Ruben C. Arslan, et al.
Knowledge about causal effects is essential for building useful theories and designing effective interventions. The preferred design for learning about causal effects is randomized experiments (i.e., studies in which the researchers randomly assign units to treatment and control conditions). However, randomized experiments are often unethical or unfeasible. On the other hand, observational studies are usually feasible but lack the random assignment that renders randomized experiments causally informative. Natural experiments can sometimes offer unique opportunities for dealing with this dilemma, allowing causal inference on the basis of events that are not controlled by researchers but that nevertheless establish random or as-if random assignment to treatment and control conditions. Yet psychological researchers have rarely exploited natural experiments. To remedy this shortage, we describe three main types of studies exploiting natural experiments (standard natural experiments, instrumental-variable designs, and regression-discontinuity designs) and provide examples from psychology and economics to illustrate how natural experiments can be harnessed. Natural experiments are challenging to find, provide information about only specific causal effects, and involve assumptions that are difficult to validate empirically. Nevertheless, we argue that natural experiments provide valuable causal-inference opportunities that have not yet been sufficiently exploited by psychologists.
Tempered Expectations: A Tutorial for Calculating and Interpreting Prediction Intervals in the Context of Replications Jeffrey R. Spence, David J. Stanley
Over the last decade, replication research in the psychological sciences has become more visible. One way that replication research can be conducted is to compare the results of the replication study with the original study to look for consistency, that is to say, to evaluate whether the original study is “replicable.” Unfortunately, many popular and readily accessible methods for ascertaining replicability, such as comparing significance levels across studies or eyeballing confidence intervals, are generally ill suited to the task of comparing results across studies. To address this issue, we present the prediction interval as a statistic that is effective for determining whether a replication study is inconsistent with the original study. We review the statistical rationale for prediction intervals, demonstrate hand calculations, and provide a walkthrough using an R package for obtaining prediction intervals for means, d values, and correlations. To aid the effective adoption of prediction intervals, we provide guidance on the correct interpretation of results when using prediction intervals in replication research.
Interacting With Curves: How to Validly Test and Probe Interactions in the Real (Nonlinear) World Uri Simonsohn
Hypotheses involving interactions in which one variable modifies the association between another two are very common. They are typically tested relying on models that assume effects are linear, for example, with a regression like y = a + b x + c z + d x × z. In the real world, however, few effects are linear, invalidating inferences about interactions. For instance, in realistic situations, the false-positive rate can be 100% for detecting an interaction, and a probed interaction can reliably produce estimated effects of the wrong sign. In this article, I propose a revised toolbox for studying interactions in a curvilinear-robust manner, giving correct answers “even” when effects are not linear. It is applicable to most study designs and produces results that are analogous to those of current—often invalid—practices. The presentation combines statistical intuition, demonstrations with published results, and simulations.
Performing Small-Telescopes Analysis by Resampling: Empirically Constructing Confidence Intervals and Estimating Statistical Power for Measures of Effect Size Samantha Costigan, John Ruscio, Jarret T. Crawford
When new data are collected to check the findings of an original study, it can be challenging to evaluate replication results. The small-telescopes method is designed to assess not only whether the effect observed in the replication study is statistically significant but also whether this effect is large enough to have been detected in the original study. Unless both criteria are met, the replication either fails to support the original findings or the results are mixed. When implemented in the conventional manner, this small-telescopes method can be impractical or impossible to conduct, and doing so often requires parametric assumptions that may not be satisfied. We present an empirical approach that can be used for a variety of study designs and data-analytic techniques. The empirical approach to the small-telescopes method is intended to extend its reach as a tool for addressing the replication crisis by evaluating findings in psychological science and beyond. In the present tutorial, we demonstrate this approach using a Shiny app and R code and included an analysis of most studies (95%) replicated as part of the Open Science Collaboration’s Reproducibility Project in Psychology. In addition to its versatility, simulations demonstrate the accuracy and precision of the empirical approach to implementing small-telescopes analysis.
Diagnosing the Misuse of the Bayes Factor in Applied Research Jorge N. Tendeiro, Henk A. L. Kiers, Rink Hoekstra, Tsz Keung Wong, Richard D. Morey
Hypothesis testing is often used for inference in the social sciences. In particular, null hypothesis significance testing (NHST) and its p value have been ubiquitous in published research for decades. Much more recently, null hypothesis Bayesian testing (NHBT) and its Bayes factor have also started to become more commonplace in applied research. Following preliminary work by Wong and colleagues, we investigated how, and to what extent, researchers misapply the Bayes factor in applied psychological research by means of a literature study. Based on a final sample of 167 articles, our results indicate that, not unlike NHST and the [Formula: see text] value, the use of NHBT and the Bayes factor also shows signs of misconceptions. We consider the root causes of the identified problems and provide suggestions to improve the current state of affairs. This article is aimed to assist researchers in drawing the best inferences possible while using NHBT and the Bayes factor in applied research.
Careless Responding: Why Many Findings Are Spurious or Spuriously Inflated Morgan D. Stosic, Brett A. Murphy, Fred Duong, Amber A. Fultz, Summer E. Harvey, Frank Bernieri
Contrary to long-standing conventional wisdom, failing to exclude data from carelessly responding participants on questionnaires or behavioral tasks will frequently result in false-positive or spuriously inflated findings. Despite prior publications demonstrating this disturbing statistical confound, it continues to be widely underappreciated by most psychologists, including highly experienced journal editors. In this article, we aim to comprehensively explain and demonstrate the severity and widespread prevalence of careless responding’s (CR) inflationary effects in psychological research. We first describe when and why one can expect to observe the inflationary effect of unremoved CR data in a manner accessible to early graduate or advanced undergraduate students. To this end, we provide an online simulator tool and instructional videos for use in classrooms. We then illustrate realistic magnitudes of the severity of unremoved CR data by presenting novel reanalyses of data sets from three high-profile articles: We found that many of their published effects would have been meaningfully, sometimes dramatically, inflated if they had not rigorously screened out CR data. To demonstrate the frequency with which researchers fail to adequately screen for CR, we then conduct a systematic review of CR screening procedures in studies using paid online samples (e.g., MTurk) published across two prominent psychological-science journals. These findings suggest that most researchers either did not conduct any kind of CR screening or conducted only bare minimal screening. To help researchers avoid publishing spuriously inflated findings, we summarize best practices to help mitigate the threats of CR data.
A Tutorial on Analyzing Ecological Momentary Assessment Data in Psychological Research With Bayesian (Generalized) Mixed-Effects Models Jonas Dora, Connor J. McCabe, Caspar J. van Lissa, Katie Witkiewitz, Kevin M. King
In this tutorial, we introduce the reader to analyzing ecological momentary assessment (EMA) data as applied in psychological sciences with the use of Bayesian (generalized) linear mixed-effects models. We discuss practical advantages of the Bayesian approach over frequentist methods and conceptual differences. We demonstrate how Bayesian statistics can help EMA researchers to (a) incorporate prior knowledge and beliefs in analyses, (b) fit models with a large variety of outcome distributions that reflect likely data-generating processes, (c) quantify the uncertainty of effect-size estimates, and (d) quantify the evidence for or against an informative hypothesis. We present a workflow for Bayesian analyses and provide illustrative examples based on EMA data, which we analyze using (generalized) linear mixed-effects models to test whether daily self-control demands predict three different alcohol outcomes. All examples are reproducible, and data and code are available at https://osf.io/rh2sw/ . Having worked through this tutorial, readers should be able to adopt a Bayesian workflow to their own analysis of EMA data.
The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization Arthur Chatton, Julia M. Rohrer
Recent developments in the causal-inference literature have renewed psychologists’ interest in how to improve causal conclusions based on observational data. A lot of the recent writing has focused on concerns of causal identification (under which conditions is it, in principle, possible to recover causal effects?); in this primer, we turn to causal estimation (how do researchers actually turn the data into an effect estimate?) and modern approaches to it that are commonly used in epidemiology. First, we explain how causal estimands can be defined rigorously with the help of the potential-outcomes framework, and we highlight four crucial assumptions necessary for causal inference to succeed (exchangeability, positivity, consistency, and noninterference). Next, we present three types of approaches to causal estimation and compare their strengths and weaknesses: propensity-score methods (in which the independent variable is modeled as a function of controls), g-computation methods (in which the dependent variable is modeled as a function of both controls and the independent variable), and doubly robust estimators (which combine models for both independent and dependent variables). A companion R Notebook is available at github.com/ArthurChatton/CausalCookbook . We hope that this nontechnical introduction not only helps psychologists and other social scientists expand their causal toolbox but also facilitates communication across disciplinary boundaries when it comes to causal inference, a research goal common to all fields of research.
Simulation-Based Power Analyses for the Smallest Effect Size of Interest: A Confidence-Interval Approach for Minimum-Effect and Equivalence Testing Paul Riesthuis
Effect sizes are often used in psychology because they are crucial when determining the required sample size of a study and when interpreting the implications of a result. Recently, researchers have been encouraged to contextualize their effect sizes and determine what the smallest effect size is that yields theoretical or practical implications, also known as the “smallest effect size of interest” (SESOI). Having a SESOI will allow researchers to have more specific hypotheses, such as whether their findings are truly meaningful (i.e., minimum-effect testing) or whether no meaningful effect exists (i.e., equivalence testing). These types of hypotheses should be reflected in power analyses to accurately determine the required sample size. Through a confidence-interval-focused approach and simulations, I show how to conduct power analyses for minimum-effect and equivalence testing. Moreover, I show that conducting a power analysis for the SESOI might result in inconclusive results. This confidence-interval-focused simulation-based power analysis can be easily adopted to different types of research areas and designs. Last, I provide recommendations on how to conduct such simulation-based power analyses.
Capturing the Social Life of a Person by Integrating Experience-Sampling Methodology and Personal-Social-Network Assessments Marie Stadel, Laura F. Bringmann, Gert Stulp, et al.
The daily social life of a person can be captured with different methodologies. Two methods that are especially promising are personal-social-network (PSN) data collection and experience-sampling methodology (ESM). Whereas PSN data collections ask participants to provide information on their social relationships and broader social environment, ESM studies collect intensive longitudinal data on social interactions in daily life using multiple short surveys per day. In combination, the two methods enable detailed insights into someone’s social life, including information on interactions with specific interaction partners from the personal network. Despite many potential uses of such data integration, there are few studies to date using the two methods in conjunction. This is likely due to their complexity and lack of software that allows capturing the full social life of someone while keeping the burden for participants and researchers sufficiently low. In this article, we report on the development of methodology and software for an ESM/PSN integration within the established ESM tool m-Path. We describe results of a first study using the developed tool that illustrate the feasibility of the proposed method combination and show that participants consider the assessments insightful. We further outline study-design choices and ethical considerations when combining the two methodologies. We hope to encourage applications of the presented methods in research and practice across different fields.
The Incremental Propensity Score Approach for Diversity Science Wen Wei Loh, Dongning Ren
Addressing core questions in diversity science requires quantifying causal effects (e.g., what drives social inequities and how to reduce them). Conventional approaches target the average causal effect (ACE), but ACE-based analyses suffer from limitations that undermine their relevance for diversity science. In this article, we introduce a novel alternative from the causal inference literature: the so-called incremental propensity score (IPS). First, we explain why the IPS is well suited for investigating core queries in diversity science. Unlike the ACE, the IPS does not demand conceptualizing unrealistic counterfactual scenarios in which everyone in the population is uniformly exposed versus unexposed to a causal factor. Instead, the IPS focuses on the effect of hypothetically shifting individuals’ chances of being exposed along a continuum. This allows seeing how the effect may be graded, offering a more realistic and policy-relevant quantification of the causal effect than a single ACE estimate. Moreover, the IPS does not require the positivity assumption, a necessary condition for estimating the ACE but which rarely holds in practice. Next, to broaden accessibility, we provide a step-by-step guide on estimating the IPS using R, a free and popular software. Finally, we illustrate the IPS using two real-world examples. The current article contributes to the methodological advancement in diversity science and offers researchers a more realistic, relevant, and meaningful approach.
Practices in Data-Quality Evaluation: A Large-Scale Review of Online Survey Studies Published in 2022 Jaroslav Gottfried
In this study, I examine data-quality evaluation methods in online surveys and their frequency of use. Drawing from survey-methodology literature, I identified 11 distinct assessment categories and analyzed their prevalence across 3,298 articles published in 2022 from 200 psychology journals in the Web of Science Master Journal List. These English-language articles employed original data from self-administered online questionnaires. Strikingly, 55% of articles opted not to employ any data-quality evaluation, and 24% employed only one method despite the wide repertoire of methods available. The most common data-quality indicators were attention-control items (22%) and nonresponse rates (13%). Strict and unjustified nonresponse-based data-exclusion criteria were often observed. The results highlight a trend of inadequate quality control in online survey research, leaving results vulnerable to biases from automated response bots or respondents’ carelessness and fatigue. More thorough data-quality assurance is currently needed for online surveys.
Implementing Statcheck During Peer Review Is Related to a Steep Decline in Statistical-Reporting Inconsistencies Michèle B. Nuijten, Jelte M. Wicherts
We investigated whether statistical-reporting inconsistencies could be avoided if journals implement the tool statcheck in the peer-review process. In a preregistered pretest–posttest quasi-experiment covering more than 7,000 articles and more than 147,000 extracted statistics, we compared the prevalence of reported p values that were inconsistent with their degrees of freedom and test statistics in two journals that implemented statcheck in their peer-review process ( Psychological Science and Journal of Experimental and Social Psychology ) and two matched control journals ( Journal of Experimental Psychology: General and Journal of Personality and Social Psychology ) before and after statcheck was implemented. Preregistered multilevel logistic regression analyses showed that the decrease in both inconsistencies and decision inconsistencies around p = .05 is considerably steeper in statcheck journals than in control journals, offering preliminary support for the notion that statcheck can be a useful tool for journals to avoid statistical-reporting inconsistencies in published articles. We discuss limitations and implications of these findings.
Listen to related podcast episode.
Feedback on this article? Email [email protected] or login to comment.
APS regularly opens certain online articles for discussion on our website. Effective February 2021, you must be a logged-in APS member to post comments. By posting a comment, you agree to our Community Guidelines and the display of your profile information, including your name and affiliation. Any opinions, findings, conclusions, or recommendations present in article comments are those of the writers and do not necessarily reflect the views of APS or the article’s author. For more information, please see our Community Guidelines .
Please login with your APS account to comment.
Many undergraduate psychology courses fail to ensure students fully understand research design and analysis. An international team of psychological scientists have recommended some systemic steps to remedy that shortcoming.
One of the foremost models that scientists use to measure the effects of cognitive dissonance may have some deficiencies, a new multilab registered replication indicates.
Methodologists have embraced preregistration as a way to prevent questionable research practices and add transparency to scientific studies. But many researchers end up deviating from those preregistered plans, and those deviations aren’t reported systematically, if at all.
Cookie | Duration | Description |
---|---|---|
__cf_bm | 30 minutes | This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. |
Cookie | Duration | Description |
---|---|---|
AWSELBCORS | 5 minutes | This cookie is used by Elastic Load Balancing from Amazon Web Services to effectively balance load on the servers. |
Cookie | Duration | Description |
---|---|---|
at-rand | never | AddThis sets this cookie to track page visits, sources of traffic and share counts. |
CONSENT | 2 years | YouTube sets this cookie via embedded youtube-videos and registers anonymous statistical data. |
uvc | 1 year 27 days | Set by addthis.com to determine the usage of addthis.com service. |
_ga | 2 years | The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors. |
_gat_gtag_UA_3507334_1 | 1 minute | Set by Google to distinguish users. |
_gid | 1 day | Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously. |
Cookie | Duration | Description |
---|---|---|
loc | 1 year 27 days | AddThis sets this geolocation cookie to help understand the location of users who share the information. |
VISITOR_INFO1_LIVE | 5 months 27 days | A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. |
YSC | session | YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. |
yt-remote-connected-devices | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
yt-remote-device-id | never | YouTube sets this cookie to store the video preferences of the user using embedded YouTube video. |
yt.innertube::nextId | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |
yt.innertube::requests | never | This cookie, set by YouTube, registers a unique ID to store data on what videos from YouTube the user has seen. |
Ids item types, copyright holder, usage metrics.
Qualitative vs quantitative vs mixed methods.
By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021
Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!
In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.
Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research
Choosing a research methodology – Nature of the research – Research area norms – Practicalities
Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.
Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.
Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.
Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.
In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.
The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job.
Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.
To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).
The three factors you need to consider are:
Let’s take a look at each of these.
As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .
But, what types of research exist?
Broadly speaking, research can fall into one of three categories:
As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.
Let’s look at an example in action.
If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.
If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .
So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.
The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.
If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.
Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.
A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .
Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.
When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.
But what constraints, you ask?
When you’re evaluating your methodological options, you need to consider the following constraints:
Let’s look at each of these.
The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.
If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.
So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.
The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.
Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon.
As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .
Some of the costs that may arise include:
These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.
Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.
The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.
Some of the questions you should ask yourself are:
Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.
So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.
In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:
If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.
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 ...
Very useful and informative especially for beginners
Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.
I find the article very informative especially for my decitation it has been helpful and an eye opener.
Hi I am Anna ,
I am a PHD candidate in the area of cyber security, maybe we can link up
The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.
I found the post very informative and practical.
I struggle so much with designs of the research for sure!
I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.
Thank you so much this site is such a life saver. How I wish 1-1 coaching is available in our country but sadly it’s not.
Your email address will not be published. Required fields are marked *
Save my name, email, and website in this browser for the next time I comment.
Share this publication.
Christopher Bronk, “AI and Energy: Advanced Tools for Knowledge Discovery” (Houston: Rice University’s Baker Institute for Public Policy, August 22, 2024), https://doi.org/10.25613/TQ6V-C361 .
This article is also featured in Energy Insights , which reflects a sample of ongoing research across the Center for Energy Studies ’ diverse programmatic areas, all addressing the ever-evolving energy challenges across Texas, the U.S., and the globe. Read more from the inaugural edition .
Benjamin Franklin is reputed to have said, “An investment in knowledge pays the best interest.” [1] Right now, a transformation in knowledge is afoot. Information arrives in an unending torrent. It is important to make better sense of this torrent by using computer-human teaming to combine multiple streams of information to see the patterns of discourse and influence on energy issues from the local to global level. In the last year, we have witnessed one of the biggest information and computing breakthroughs of at least a decade — ChatGPT and its Large Language Model (LLM) contemporaries. Despite the hype in the area, there is a need to make sense of ever-deepening pools of information on almost any topic while at the same time using artificial intelligence (AI) to create new information.
To expand the analysis of energy, we must harness rapidly evolving information tools that link quantitative data with news, online media, video streams, and other computer-mediated interactions. A key goal is to better understand how information can be wielded by nation-states and other entities on topics from renewable technologies and data center operations to climate mitigation schemes and international collaborations.
Make no mistake, market data regarding energy products and services remains as important as ever for understanding the field; however, AI technologies may allow us to make better sense of the larger discourse surrounding energy and politics, from local to national to international. Whether tracking statements by world leaders, evaluating new technology investments, or debunking disinformation, utilizing the latest computational tools to better explain our complicated world will become the norm.
As a series of global energy transitions progresses, new methods for understanding markets, regulation, politics, security, social movements, and technology will be critical for coping with information overload. Fortunately, monitoring of energy activity can be greatly aided by new computational technologies that read government proposals, news reporting, and other forms of information to provide unique insights. By coupling human and machine intelligence, in what has been referred to as a centaur model, can offer an innovative capacity for sensemaking and awareness. [2] In the past, we might convene experts to write a study on an emergent topic. Now, we have the capacity to have a first pass summary on almost any issue in minutes.
In the world of energy and geopolitics, it is important to monitor rapidly evolving drafts of law and other policy on a global scale. Tracking only a single piece of proposed regulation can consume considerable analytical capacity. At any time, dozens of different proposals may be passing through legislative bodies. In fact, this applies to issues beyond legislation. A Centaur model can provide situational awareness and be up-to-date on proposed laws and rulemakings, industry positioning, and international sanctions without the need for a deep dive by experts.
Figure 1 — Concept AI Tool for Data Processing
In energy technology, we are continuously asked when an emerging technology may be ready for widespread commercialization and adoption. An exemplar is new forms of nuclear fission energy often referred to as compact nuclear energy. In a typical scenario, industry and academic experts might be brought together to produce a study on the topic. But human interaction with AI can allow a quick assessment of scholarly papers, industry presentations, press releases, public speeches, and other data to have an ongoing assessment of where the compact reactor technology is headed and what players are advancing it. Then, an assessment of technology readiness can proceed much more quickly, facilitating a more rigorous analysis of where the regulatory, social, and/or economic deficiencies may lie.
Regarding geopolitical events, an AI-human hybrid research system could quickly and thoroughly compile the announcements of relevant leaders, translate and summarize news stories, and overlay satellite imagery to assess threats to supply chains and infrastructure. It could also compare news coverage in the United States against news coverage in other countries to assess missed developments, and it could mine for data from alternate voices affecting markets and policy on social platforms, such as Telegram, X, or Instagram. Such a complete picture would allow a deeper look into issues where versions of the same event diverge. In turn, this would allow for a better understanding of the links between messaging and motive, which can be very informative in a geopolitical context.
To be clear, building out AI tools to better understand the global energy system does not translate to the replacement of deep expertise rooted in years of training and experience accumulated in government, industry, and academia. Rather, AI tools, as they are developed, can be done in a way that makes subject matter experts more capable and effective. This will be especially true in times of geopolitical crisis that require rapid response capabilities.
For instance, the activities of political leaders often intersect with potential international conflicts. As one example, the conflict in Ukraine — from the launch of Russia’s invasion to various aspects of the ongoing crisis — is very much a reflection of statements made and actions undertaken by Russian President Vladimir Putin and Ukrainian President Volodymyr Zelenskyy. A rapid assessment of the totality of comments made combined with expert analysis can provide important insights that may be effective aids in managing collateral impacts or even assist in conflict resolution.
As another example, tensions between Venezuela and Guyana have recently emerged as Venezuela’s Nicolás Maduro government has increasingly threatened its neighbor around energy developments in the Essequibo region. Advanced tooling on information resources regarding this international dispute can combine with world-leading expert analysis of everything from social media to commercial satellite imagery in providing valuable assessment of this or any simmering crisis.
As information is compiled using AI tools trained on various issues and topics over time, the increase in data availability for computational analysis will enrich the depth of expert analysis of a variety of issues in energy geopolitics. Moreover, as the pace of information accelerates, it will be possible to more rapidly make sense of global issues to elevate discourse aimed at finding achievable solutions for current issues, from geopolitics to regulation to technological innovation to commercial project development and more. The need for new ways to refine high volumes of data and information across multiple areas of expertise will drive significant innovations in how we process and use those signals. While data is not necessarily the new oil, it will increasingly be commoditized to afford advantages in decision-making that will be critical for success in commercial and diplomatic domains.
< Previous article | Next article >
[1] “About Benjamin Franklin,” Benjamin Franklin College, Yale College, https://benjaminfranklin.yalecollege.yale.edu/about-us/about-benjamin-franklin .
[2] See, for example, Kevin Yamazaki, “Reconciling the AI-Human Conflict with the Centaur Model,” CIOReview , https://artificial-intelligence.cioreview.com/cxoinsight/reconciling-the-aihuman-conflict-with-the-centaur-model-nid-24514-cid-175.html .
This material may be quoted or reproduced without prior permission, provided appropriate credit is given to the author and Rice University’s Baker Institute for Public Policy. The views expressed herein are those of the individual author(s), and do not necessarily represent the views of Rice University’s Baker Institute for Public Policy.
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Methodology
Published on August 13, 2021 by Tegan George . Revised on June 22, 2023.
Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question . Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods.
Mixed methods research is often used in the behavioral, health, and social sciences, especially in multidisciplinary settings and complex situational or societal research.
When to use mixed methods research, mixed methods research designs, advantages of mixed methods research, disadvantages of mixed methods research, other interesting articles, frequently asked questions.
Mixed methods research may be the right choice if your research process suggests that quantitative or qualitative data alone will not sufficiently answer your research question. There are several common reasons for using mixed methods research:
As you formulate your research question , try to directly address how qualitative and quantitative methods will be combined in your study. If your research question can be sufficiently answered via standalone quantitative or qualitative analysis, a mixed methods approach may not be the right fit.
But mixed methods might be a good choice if you want to meaningfully integrate both of these questions in one research study.
Keep in mind that mixed methods research doesn’t just mean collecting both types of data; you need to carefully consider the relationship between the two and how you’ll integrate them into coherent conclusions.
Mixed methods can be very challenging to put into practice, and comes with the same risk of research biases as standalone studies, so it’s a less common choice than standalone qualitative or qualitative research.
Discover proofreading & editing
There are different types of mixed methods research designs . The differences between them relate to the aim of the research, the timing of the data collection , and the importance given to each data type.
As you design your mixed methods study, also keep in mind:
Here are a few of the most common mixed methods designs.
In a convergent parallel design, you collect quantitative and qualitative data at the same time and analyze them separately. After both analyses are complete, compare your results to draw overall conclusions.
In an embedded design, you collect and analyze both types of data at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
This is a good approach to take if you have limited time or resources. You can use an embedded design to strengthen or supplement your conclusions from the primary type of research design.
In an explanatory sequential design, your quantitative data collection and analysis occurs first, followed by qualitative data collection and analysis.
You should use this design if you think your qualitative data will explain and contextualize your quantitative findings.
In an exploratory sequential design, qualitative data collection and analysis occurs first, followed by quantitative data collection and analysis.
You can use this design to first explore initial questions and develop hypotheses . Then you can use the quantitative data to test or confirm your qualitative findings.
Combining the two types of data means you benefit from both the detailed, contextualized insights of qualitative data and the generalizable , externally valid insights of quantitative data. The strengths of one type of data often mitigate the weaknesses of the other.
For example, solely quantitative studies often struggle to incorporate the lived experiences of your participants, so adding qualitative data deepens and enriches your quantitative results.
Solely qualitative studies are often not very generalizable, only reflecting the experiences of your participants, so adding quantitative data can validate your qualitative findings.
Mixed methods are less tied to disciplines and established research paradigms. They offer more flexibility in designing your research, allowing you to combine aspects of different types of studies to distill the most informative results.
Mixed methods research can also combine theory generation and hypothesis testing within a single study, which is unusual for standalone qualitative or quantitative studies.
Mixed methods research is very labor-intensive. Collecting, analyzing, and synthesizing two types of data into one research product takes a lot of time and effort, and often involves interdisciplinary teams of researchers rather than individuals. For this reason, mixed methods research has the potential to cost much more than standalone studies.
If your analysis yields conflicting results, it can be very challenging to know how to interpret them in a mixed methods study. If the quantitative and qualitative results do not agree or you are concerned you may have confounding variables , it can be unclear how to proceed.
Due to the fact that quantitative and qualitative data take two vastly different forms, it can also be difficult to find ways to systematically compare the results, putting your data at risk for bias in the interpretation stage.
Professional editors proofread and edit your paper by focusing on:
See an example
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.
Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.
Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.
These are four of the most common mixed methods designs :
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
George, T. (2023, June 22). Mixed Methods Research | Definition, Guide & Examples. Scribbr. Retrieved August 21, 2024, from https://www.scribbr.com/methodology/mixed-methods-research/
Other students also liked, writing strong research questions | criteria & examples, what is quantitative research | definition, uses & methods, what is qualitative research | methods & examples, get unlimited documents corrected.
✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.
Nature Reviews Molecular Cell Biology ( 2024 ) Cite this article
13 Altmetric
Metrics details
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
This is a preview of subscription content, access via your institution
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
24,99 € / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
176,64 € per year
only 14,72 € per issue
Buy this article
Prices may be subject to local taxes which are calculated during checkout
Quake, S. R. A decade of molecular cell atlases. Trends Genet. 38 , 805–810 (2022).
Article CAS PubMed Google Scholar
Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24 , 695–713 (2023).
Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22 , 627–644 (2021).
Article CAS PubMed PubMed Central Google Scholar
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550 , 451–453 (2017).
Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the Human Cell Atlas on medicine. Nat. Med. 28 , 2486–2496 (2022).
Ferreira, P. G. et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9 , 490 (2018).
Article PubMed PubMed Central Google Scholar
Baechler, E. C. et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5 , 347–353 (2004).
Massoni-Badosa, R. et al. Sampling time-dependent artifacts in single-cell genomics studies. Genome Biol. 21 , 112 (2020).
Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7 , 5435 (2017).
O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20 , 210 (2019).
Liu, Y. et al. Digestion of nucleic acids starts in the stomach. Sci. Rep. 5 , 11936 (2015).
Martinez-Diez, M. C., Serrano, M. A., Monte, M. J. & Marin, J. J. Comparison of the effects of bile acids on cell viability and DNA synthesis by rat hepatocytes in primary culture. Biochim. Biophys. Acta 1500 , 153–160 (2000).
Sorrentino, S. & Libonati, M. Human pancreatic-type and nonpancreatic-type ribonucleases: a direct side-by-side comparison of their catalytic properties. Arch. Biochem. Biophys. 312 , 340–348 (1994).
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21 , 130 (2020).
Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47 , 1584–1797 (2017).
Lahoz-Beneytez, J. et al. Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives. Blood 127 , 3431–3438 (2016).
Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14 , 865–868 (2017).
Quan, Y. et al. Impact of cell dissociation on identification of breast cancer stem cells. Cancer Biomark. 12 , 125–133 (2012).
Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. 2 , 112–120 (2012).
Article CAS Google Scholar
Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35 , 936–939 (2017).
Butto, T. et al. Nuclei on the rise: when nuclei-based methods meet next-generation sequencing. Cells 12 , 1051 (2023).
Caglayan, E., Liu, Y. & Konopka, G. Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets. Neuron 110 , 4043–4056.e5 (2022).
Thrupp, N. et al. Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 32 , 108189 (2020).
Pitchiaya, S. et al. Dynamic recruitment of single RNAs to processing bodies depends on RNA functionality. Mol. Cell 74 , 521–533.e6 (2019).
Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19 , 562–578 (2018).
Article PubMed Google Scholar
Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1 , 263–280 (2022).
Chervov, A. & Zinovyev, A. Computational challenges of cell cycle analysis using single cell transcriptomics. Preprint at https://doi.org/10.48550/arXiv.2208.05229 (2022).
Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37 , 963–967 (2021).
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21 , 12 (2020).
Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2 , lqaa078 (2020).
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47 (2015).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36 , 421–427 (2018).
Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36 , 964–965 (2020).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16 , 1289–1296 (2019).
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37 , 685–691 (2019).
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177 , 1873–1887.e17 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177 , 1888–1902.e21 (2019).
Nguyen, H. C. T., Baik, B., Yoon, S., Park, T. & Nam, D. Benchmarking integration of single-cell differential expression. Nat. Commun. 14 , 1570 (2023).
Junttila, S., Smolander, J. & Elo, L. L. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief. Bioinform. 23 , bbac286 (2022).
Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15 , 255–261 (2018).
Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40 , 245–253 (2022).
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15 , 1053–1058 (2018).
Lakkis, J. et al. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics. Genome Res. 31 , 1753–1766 (2021).
Li, H., McCarthy, D. J., Shim, H. & Wei, S. Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics. BMC Bioinform. 23 , 460 (2022).
Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16 , 715–721 (2019).
Bahrami, M. et al. Deep feature extraction of single-cell transcriptomes by generative adversarial network. Bioinformatics 37 , 1345–1351 (2021).
Tyler, S. R., Guccione, E. & Schadt, E. E. Erasure of biologically meaningful signal by unsupervised scRNAseq batch-correction methods. Preprint at bioRxiv https://doi.org/10.1101/2021.11.15.468733 (2023).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19 , 41–50 (2022).
Zhang, Z. et al. Signal recovery in single cell batch integration. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539614 (2023).
Dann, E. et al. Precise identification of cell states altered in disease using healthy single-cell references. Nat. Genet. 55 , 1998–2008 (2023).
Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database 2020 , baaa073 (2020).
Regev, A. et al. The Human Cell Atlas. eLife 6 , e27041 (2017).
Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376 , eabl4290 (2022).
Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26 , 792–802 (2020).
Zhang, A. W. et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods 16 , 1007–1015 (2019).
Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16 , 983–986 (2019).
Ianevski, A., Giri, A. K. & Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat. Commun. 13 , 1246 (2022).
Franchini, M., Pellecchia, S., Viscido, G. & Gambardella, G. Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genom. Bioinform. 5 , lqad024 (2023).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20 , 163–172 (2019).
Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15 , 359–362 (2018).
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T. & Holstege, F. C. P. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res. 47 , e95 (2019).
Lyu, P., Zhai, Y., Li, T. & Qian, J. CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server. Bioinformatics 39 , btad521 (2023).
Boufea, K., Seth, S. & Batada, N. N. scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-seq data with batch effect. iScience 23 , 100914 (2020).
Lin, Y. et al. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol. Syst. Biol. 16 , e9389 (2020).
Yin, Q. et al. scGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38 , 2996–3003 (2022).
Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20 , 264 (2019).
Li, C. et al. SciBet as a portable and fast single cell type identifier. Nat. Commun. 11 , 1818 (2020).
Hou, W. & Ji, Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat. Methods https://doi.org/10.1038/s41592-024-02235-4 (2024).
Lotfollahi, M., Yuhan, H., Theis, F. J. & Satija, R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 187 , 2343–2358 (2024).
Michielsen, L. et al. Single-cell reference mapping to construct and extend cell-type hierarchies. NAR Genom. Bioinform. 5 , lqad070 (2023).
Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40 , 121–130 (2022).
Osumi-Sutherland, D. et al. Cell type ontologies of the Human Cell Atlas. Nat. Cell Biol. 23 , 1129–1135 (2021).
Flanagin, A., Frey, T. & Christiansen, S. L. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326 , 621–627 (2021).
Brbić, M. et al. Annotation of spatially resolved single-cell data with STELLAR. Nat. Methods 19 , 1411–1418 (2022).
Wen, L. & Tang, F. Single-cell sequencing in stem cell biology. Genome Biol. 17 , 71 (2016).
Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell Mol. Immunol. 16 , 242–249 (2019).
Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618 , 598–606 (2023).
Zhang, Y. et al. Single-cell RNA sequencing in cancer research. J. Exp. Clin. Cancer Res. 40 , 81 (2021).
Machado, L. et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell 28 , 1125–1135.e7 (2021).
Uniken Venema, W. T. C. et al. Gut mucosa dissociation protocols influence cell type proportions and single-cell gene expression levels. Sci. Rep. 12 , 9897 (2022).
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14 , 935–936 (2017).
Jones, R. C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376 , eabl4896 (2022).
Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562 , 367–372 (2018).
Article Google Scholar
Kumar, T. et al. A spatially resolved single-cell genomic atlas of the adult human breast. Nature 620 , 181–191 (2023).
Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530 , 223–227 (2016).
Rossi, L., Challen, G. A., Sirin, O., Lin, K. K. & Goodell, M. A. Hematopoietic stem cell characterization and isolation. Methods Mol. Biol. 750 , 47–59 (2011).
Ikuta, K. & Weissman, I. L. Evidence that hematopoietic stem cells express mouse c-kit but do not depend on steel factor for their generation. Proc. Natl Acad. Sci. USA 89 , 1502–1506 (1992).
Liu, D. D. et al. Purification and characterization of human neural stem and progenitor cells. Cell 186 , 1179–1194.e15 (2023).
Chan, C. K. F. et al. Identification of the human skeletal stem cell. Cell 175 , 43–56.e21 (2018).
Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65 , 631–643.e4 (2017).
Al’Khafaji, A. M. et al. High-throughput RNA isoform sequencing using programmed cDNA concatenation. Nat. Biotechnol. 42 , 582–586 (2023).
Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40 , 1780–1793 (2022).
Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15 , 379–386 (2018).
Jindal, A., Gupta, P., Jayadeva & Sengupta, D. Discovery of rare cells from voluminous single cell expression data. Nat. Commun. 9 , 4719 (2018).
Fa, B. et al. GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles. Nat. Commun. 12 , 4197 (2021).
Dong, R. & Yuan, G. C. GiniClust3: a fast and memory-efficient tool for rare cell type identification. BMC Bioinform. 21 , 158 (2020).
Wegmann, R. et al. CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data. Genome Biol. 20 , 142 (2019).
Song, D., Li, K., Hemminger, Z., Wollman, R. & Li, J. J. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling. Bioinformatics 37 , i358–i366 (2021).
Neufeld, A., Gao, L. L., Popp, J., Battle, A. & Witten, D. Inference after latent variable estimation for single-cell RNA sequencing data. Biostatistics 25 , 270–287 (2023).
Song, D., Li, K., Ge, X. & Li, J. J. ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double bioRxiv (2023).
Persad, S. et al. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat. Biotechnol. 41 , 1746–1757 (2023).
Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56 , 431–441 (2024).
Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46 , 2496–2506 (2016).
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37 , 547–554 (2019).
La Manno, G. et al. RNA velocity of single cells. Nature 560 , 494–498 (2018).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38 , 1408–1414 (2020).
Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21 , 39 (2020).
Hendriks, G. J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10 , 3138 (2019).
Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571 , 419–423 (2019).
Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173 , 1535–1548.e16 (2018).
Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548 , 456–460 (2017).
Sharma, R. et al. The TRACE-seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells. Nat. Commun. 12 , 472 (2021).
Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185 , 1905–1923.e25 (2022).
Spencer Chapman, M. et al. Lineage tracing of human development through somatic mutations. Nature 595 , 85–90 (2021).
Muyas, F. et al. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat. Biotechnol. 42 , 758–767 (2024).
Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176 , 1325–1339.e22 (2019).
Gabbutt, C. et al. Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat. Biotechnol. 40 , 720–730 (2022).
DuPage, M. & Bluestone, J. A. Harnessing the plasticity of CD4 + T cells to treat immune-mediated disease. Nat. Rev. Immunol. 16 , 149–163 (2016).
Huyghe, A., Trajkova, A. & Lavial, F. Cellular plasticity in reprogramming, rejuvenation and tumorigenesis: a pioneer TF perspective. Trends Cell Biol. 34 , 255–267 (2024).
Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37 , 451–460 (2019).
Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K. & Tsia, K. K. Generalized and scalable trajectory inference in single-cell omics data with VIA. Nat. Commun. 12 , 5528 (2021).
Pandey, K. & Zafar, H. Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET. Nucleic Acids Res. 50 , e86 (2022).
Lönnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol. 2 , eaal2192 (2017).
Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19 , 271–281 (2017).
Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19 , 159–170 (2022).
Weiler, P., Lange, M., Klein, M., Pe’er, D. & Theis, F. CellRank 2: unified fate mapping in multiview single-cell data. Nat. Methods 21 , 1196–1205 (2024).
Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176 , 928–943.e22 (2019).
Tong, A., Huang, J., Wolf, G., van Dijk, D. & Krishnaswamy, S. TrajectoryNet: a dynamic optimal transport network for modeling cellular dynamics. Proc. Mach. Learn. Res. 119 , 9526–9536 (2020).
PubMed PubMed Central Google Scholar
Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614 , 742–751 (2023).
Weissman, I. L. Stem cells: units of development, units of regeneration, and units in evolution. Cell 100 , 157–168 (2000).
Senra, D., Guisoni, N. & Diambra, L. ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data. MethodsX 9 , 101778 (2022).
Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173 , 338–354.e15 (2018).
Müller, F. J. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455 , 401–405 (2008).
Zhang, F. et al. FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight. Brief. Bioinform. 23 , bbac293 (2022).
Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367 , 405–411 (2020).
Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19 , 266–277 (2016).
Kannan, S., Farid, M., Lin, B. L., Miyamoto, M. & Kwon, C. Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level. PLoS Comput. Biol. 17 , e1009305 (2021).
Jin, S., MacLean, A. L., Peng, T. & Nie, Q. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data. Bioinformatics 34 , 2077–2086 (2018).
Guo, M., Bao, E. L., Wagner, M., Whitsett, J. A. & Xu, Y. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45 , e54 (2017).
PubMed Google Scholar
Teschendorff, A. E. & Enver, T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8 , 15599 (2017).
Teschendorff, A. E., Maity, A. K., Hu, X., Weiyan, C. & Lechner, M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-seq data. Bioinformatics 37 , 1528–1534 (2021).
Ni, X. et al. Accurate estimation of single-cell differentiation potency based on network topology and gene ontology information. IEEE/ACM Trans. Comput. Biol. Bioinform. 19 , 3255–3262 (2022).
Kang, M. et al. Mapping single-cell developmental potential in health and disease with interpretable deep learning. Preprint at bioRxiv https://doi.org/10.1101/2024.03.19.585637 (2024).
Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40 , 308–318 (2022).
Lohoff, T. et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40 , 74–85 (2022).
Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature 619 , 572–584 (2023).
Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187 , 2485–2501.e26 (2024).
Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci. Alliance 6 , e202201701 (2023).
He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40 , 1794–1806 (2022).
Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568 , 235–239 (2019).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361 , eaat5691 (2018).
Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14 , 8353 (2023).
Liang, S. et al. Single-cell manifold-preserving feature selection for detecting rare cell populations. Nat. Comput. Sci. 1 , 374–384 (2021).
Missarova, A. et al. geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. Genome Biol. 22 , 333 (2021).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39 , 313–319 (2021).
Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377 , eabp9444 (2022).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185 , 1777–1792.e21 (2022).
Nagendran, M. et al. 1457 Visium HD enables spatially resolved, single-cell scale resolution mapping of FFPE human breast cancer tissue. J. Immunother. Cancer 11 , A1620 (2023).
Google Scholar
Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv . 7 , eabg4755 (2021).
Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373 , 111–117 (2021).
Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625 , 101–109 (2024).
Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489 , 391–399 (2012).
Jung, N. & Kim, T.-K. Spatial transcriptomics in neuroscience. Exp. Mol. Med. 55 , 2105–2115 (2023).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19 , 534–546 (2022).
Magoulopoulou, A. et al. Padlock probe-based targeted in situ sequencing: overview of methods and applications. Annu. Rev. Genom. Hum. Genet. 24 , 133–150 (2023).
Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14 , 68 (2022).
Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3 , 505–517 (2022).
Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37 , 773–782 (2019).
Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19 , 662–670 (2022).
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40 , 661–671 (2022).
Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40 , 517–526 (2022).
Vahid, M. R. et al. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat. Biotechnol. 41 , 1543–1548 (2023).
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18 , 1352–1362 (2021).
Wei, R. et al. Spatial charting of single-cell transcriptomes in tissues. Nat. Biotechnol. 40 , 1190–1199 (2022).
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39 , 1375–1384 (2021).
Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40 , 476–479 (2022).
Hu, J. et al. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst. 14 , 404–417.e4 (2023).
Zhang, D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol . https://doi.org/10.1038/s41587-023-02019-9 (2024).
Miller, B. F., Huang, F., Atta, L., Sahoo, A. & Fan, J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat. Commun. 13 , 2339 (2022).
Garcia-Alonso, L. et al. Single-cell roadmap of human gonadal development. Nature 607 , 540–547 (2022).
Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184 , 810–826.e23 (2021).
Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175 , 1156–1167.e15 (2018).
Bahar Halpern, K. et al. Lgr5+ telocytes are a signaling source at the intestinal villus tip. Nat. Commun. 11 , 1936 (2020).
Valdeolivas, A. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis. Oncol. 8 , 10 (2024).
Sibai, M. et al. The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology. Preprint at bioRxiv https://doi.org/10.1101/2022.06.18.496114 (2023).
Heiser, C. N. et al. Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors. Cell 186 , 5620–5637.e16 (2023).
Ren, Y. et al. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nat. Commun. 14 , 1028 (2023).
Arora, R. et al. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. Nat. Commun. 14 , 5029 (2023).
Meylan, M. et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity 55 , 527–541.e5 (2022).
Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02193-4 (2024).
Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Res. 48 , e107 (2020).
Sun, E. D., Ma, R., Navarro Negredo, P., Brunet, A. & Zou, J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat. Methods 21 , 444–454 (2024).
Clifton, K. et al. STalign: alignment of spatial transcriptomics data using diffeomorphic metric mapping. Nat. Commun. 14 , 8123 (2023).
Jones, A., Townes, F. W., Li, D. & Engelhardt, B. E. Alignment of spatial genomics data using deep Gaussian processes. Nat. Methods 20 , 1379–1387 (2023).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624 , 343–354 (2023).
Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25 , 1463–1465 (2009).
Varrone, M., Tavernari, D., Santamaria-Martínez, A., Walsh, L. A. & Ciriello, G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat. Genet. 56 , 74–84 (2024).
Rajachandran, S. et al. Dissecting the spermatogonial stem cell niche using spatial transcriptomics. Cell Rep. 42 , 112737 (2023).
Walsh, L. A. & Quail, D. F. Decoding the tumor microenvironment with spatial technologies. Nat. Immunol. 24 , 1982–1993 (2023).
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3 , 100498 (2023).
Choi, J. et al. QuadST: a powerful and robust approach for identifying cell-cell interaction-changed genes on spatially resolved transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.12.04.570019 (2023).
Pentimalli, T. M. et al. High-resolution molecular atlas of a lung tumor in 3D. Preprint at bioRxiv https://doi.org/10.1101/2023.05.10.539644 (2023).
Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182 , 1341–1359.e19 (2020).
Qiu, X. et al. Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2022.12.07.519417 (2022).
Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6 , 1435–1448 (2022).
Farah, E. N. et al. Spatially organized cellular communities form the developing human heart. Nature 627 , 854–864 (2024).
Bhate, S. S., Barlow, G. L., Schürch, C. M. & Nolan, G. P. Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors. Cell Syst. 13 , 109–130.e6 (2022).
Kim, J. et al. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat. Methods 19 , 1653–1661 (2022).
Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14 , 1155 (2023).
Ren, H., Walker, B. L., Cang, Z. & Nie, Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat. Commun. 13 , 4076 (2022).
Li, J., Chen, S., Pan, X., Yuan, Y. & Shen, H.-B. Cell clustering for spatial transcriptomics data with graph neural networks. Nat. Comput. Sci. 2 , 399–408 (2022).
Pham, D. et al. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat. Commun. 14 , 7739 (2023).
Cang, Z. et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20 , 218–228 (2023).
Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12 , 6012 (2021).
Birk, S. et al. Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning. Preprint at bioRxiv https://doi.org/10.1101/2024.02.21.581428 (2024).
Turesson, G. The genotypical response of the plant species to the habitat. Hereditas 3 , 211–350 (1922).
Ortiz, R. Göte Turesson’s research legacy to Hereditas: from the ecotype concept in plants to the analysis of landraces’ diversity in crops. Hereditas 157 , 44 (2020).
Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184 , 5482–5496.e28 (2021).
Steen, C. B. et al. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell 39 , 1422–1437.e10 (2021).
Luca, B. A. et al. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors. Cancer Res. 80 , abstr. 3443 (2020).
Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53 , 1334–1347 (2021).
Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40 , 1467–1477 (2022).
Bill, R. et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science 381 , 515–524 (2023).
Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184 , 4734–4752.e20 (2021).
Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182 , 497–514.e22 (2020).
He, S. et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02173-8 (2024).
Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57 , 1284–1298.e5 (2022).
Gu, Y., Liu, J., Li, C. & Welch, J. D. Mapping cell fate transition in space and time. Preprint at bioRxiv https://doi.org/10.1101/2024.02.12.579941 (2024).
Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601 , 85–91 (2022).
Xue, Y. et al. Single-cell mitochondrial variant enrichment resolved clonal tracking and spatial architecture in human embryonic hematopoiesis. Preprint at bioRxiv https://doi.org/10.1101/2023.09.18.558215 (2023).
Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25 , 285–294 (2022).
Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608 , 360–367 (2022).
Lomakin, A. et al. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature 611 , 594–602 (2022).
Househam, J. et al. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 611 , 744–753 (2022).
Lim, J. et al. Transitioning single-cell genomics into the clinic. Nat. Rev. Genet. 24 , 573–584 (2023).
Van de Sande, B. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 22 , 496–520 (2023).
Lozano, A. X. et al. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat. Med. 28 , 353–362 (2022).
Kwon, M. et al. Determinants of response and intrinsic resistance to PD-1 blockade in microsatellite instability-high gastric cancer. Cancer Discov. 11 , 2168–2185 (2021).
Zhou, Y. et al. Single-cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer. Cancer Cell 38 , 818–828.e5 (2020).
Abe, Y. et al. A single-cell atlas of non-haematopoietic cells in human lymph nodes and lymphoma reveals a landscape of stromal remodelling. Nat. Cell Biol. 24 , 565–578 (2022).
Ajani, J. A. et al. YAP1 mediates gastric adenocarcinoma peritoneal metastases that are attenuated by YAP1 inhibition. Gut 70 , 55–66 (2021).
Beneyto-Calabuig, S. et al. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. Cell Stem Cell 30 , 706–721.e8 (2023).
Miyamoto, D. T., Ting, D. T., Toner, M., Maheswaran, S. & Haber, D. A. Single-cell analysis of circulating tumor cells as a window into tumor heterogeneity. Cold Spring Harb. Symp. Quant. Biol. 81 , 269–274 (2016).
Loh, K. M. et al. Mapping the pairwise choices leading from pluripotency to human bone, heart, and other mesoderm cell types. Cell 166 , 451–467 (2016).
Karimi, E. et al. Single-cell spatial immune landscapes of primary and metastatic brain tumours. Nature 614 , 555–563 (2023).
Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621 , 868–876 (2023).
Lin, J. R. et al. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat. Cancer 4 , 1036–1052 (2023).
Sorin, M. et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 614 , 548–554 (2023).
Lin, J. R. et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 186 , 363–381.e19 (2023).
Digre, A. & Lindskog, C. The human protein atlas-Integrated omics for single cell mapping of the human proteome. Protein Sci. 32 , e4562 (2023).
Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024).
Carpenter, A. E. & Singh, S. Bringing computation to biology by bridging the last mile. Nat. Cell Biol. 26 , 5–7 (2024).
Hu, Y. et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat. Methods 21 , 267–278 (2024).
Heimberg, G. et al. Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages. Preprint at bioRxiv https://doi.org/10.1101/2023.07.18.549537 (2023).
Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://doi.org/10.48550/arXiv.2108.07258 (2022).
Yang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4 , 852–866 (2022).
Bian, H. et al. scMulan: a multitask generative pre-trained language model for single-cell analysis. Preprint at bioRxiv https://doi.org/10.1101/2024.01.25.577152 (2024).
Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024).
Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618 , 616–624 (2023).
Shen, H. et al. Generative pretraining from large-scale transcriptomes for single-cell deciphering. iScience 26 , 106536 (2023).
Yang, X. et al. GeneCompass: deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model. Preprint at bioRxiv https://doi.org/10.1101/2023.09.26.559542 (2023).
Rosen, Y. et al. Universal cell embeddings: a foundation model for cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.11.28.568918 (2023).
Zhang, R., Luo, Y., Ma, J., Zhang, M. & Wang, S. scPretrain: multi-task self-supervised learning for cell-type classification. Bioinformatics 38 , 1607–1614 (2022).
Alsabbagh, A. R. et al. Foundation models meet imbalanced single-cell data when learning cell type annotations. Preprint at bioRxiv https://doi.org/10.1101/2023.10.24.563625 (2023).
Boiarsky, R., Singh, N., Buendia, A., Getz, G. & Sontag, D. A deep dive into single-cell RNA sequencing foundation models. Preprint at bioRxiv https://doi.org/10.1101/2023.10.19.563100 (2023).
Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Assessing the limits of zero-shot foundation models in single-cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.561085 (2023).
Khan, S. A. et al. Reusability report: learning the transcriptional grammar in single-cell RNA-sequencing data using transformers. Nat. Mach. Intell. 5 , 1437–1446 (2023).
Schaar, A. C. et al. Nicheformer: a foundation model for single-cell and spatial omics. Preprint at bioRxiv https://doi.org/10.1101/2024.04.15.589472 (2024).
Roohani, Y., Huang, K. & Leskovec, J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. 42 , 927–935 (2023).
Zhang, Y., Tiňo, P., Leonardis, A. & Tang, K. A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5 , 726–742 (2021).
Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A. & Zou, J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 186 , 1772–1791 (2023).
Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17 , e9620 (2021).
Zimmerman, K. D., Espeland, M. A. & Langefeld, C. D. A practical solution to pseudoreplication bias in single-cell studies. Nat. Commun. 12 , 738 (2021).
Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11 , 6077 (2020).
He, L. et al. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun. Biol. 4 , 629 (2021).
Pullin, J. M. & McCarthy, D. J. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol. 25 , 56 (2024).
Wang, X. et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 28 , 2534–2536 (2012).
Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42 , 293–304 (2024).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566 , 496–502 (2019).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19 , 477 (2018).
Ellwanger, D. C., Scheibinger, M., Dumont, R. A., Barr-Gillespie, P. G. & Heller, S. Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails. Cell Rep. 23 , 2901–2914.e13 (2018).
Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://doi.org/10.1101/079509 (2016).
Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22 , 145 (2021).
Wan, X. et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat. Commun. 14 , 7848 (2023).
Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12 , 453–457 (2015).
Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10 , 380 (2019).
Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6 , eaba2619 (2020).
Garmire, L. X. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat. Methods 21 , 391–400 (2024).
Li, H. et al. DeconPeaker, a deconvolution model to identify cell types based on chromatin accessibility in ATAC-seq data of mixture samples. Front. Genet. 11 , 392 (2020).
Hutson, M. Hunting for the best bioscience software tool? Check this database. Nature https://doi.org/10.1038/d41586-023-00053-w (2023).
Decamps, C. et al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinform. 21 , 16 (2020).
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B. & Livak, K. J. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat. Rev. Clin. Oncol. 18 , 244–256 (2021).
Download references
The authors are grateful to the members of the Newman and Wang laboratories for the valuable discussions and feedback. The original figures were created with Biorender.com. This work was supported by the National Science Foundation (J.P.D., Graduate Research Fellowship DGE-1656518), the National Cancer Institute (L.W., R01CA266280 and U24CA274274; A.M.N., R01CA255450), the Cancer Prevention and Research Institute of Texas (L.W., RP200385), the Break Through Cancer Foundation (L.W.), the University Cancer Foundation via the Institutional Research Grant Program (L.W.), the Melanoma Research Alliance (A.M.N., grant number 926521), and the Virginia and D. K. Ludwig Fund for Cancer Research (A.M.N.). L.W. is an Andrew Sabin Family Foundation Fellow. A.M.N. is a Chan Zuckerberg Biohub – San Francisco Investigator.
These authors contributed equally: Gunsagar S. Gulati, Jeremy Philip D’Silva.
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Gunsagar S. Gulati
Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
Jeremy Philip D’Silva & Aaron M. Newman
Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Yunhe Liu & Linghua Wang
The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
Linghua Wang
Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
Aaron M. Newman
Stanford Cancer Institute, Stanford University, Stanford, CA, USA
Chan Zuckerberg Biohub – San Francisco, San Francisco, CA, USA
You can also search for this author in PubMed Google Scholar
All authors discussed the content of the article, contributed to writing or editing, and reviewed the manuscript before submission. L.W. and A.M.N. jointly supervised the work.
Correspondence to Aaron M. Newman .
Competing interests.
A.M.N. holds patents related to digital cytometry and cancer biomarkers and has ownership interests in CiberMed, Inc., LiquidCell Dx, Inc. and CytoTrace Biosciences, Inc. The other authors declare no competing interests.
Peer review information.
Nature Reviews Molecular Cell Biology thanks Tallulah Andrews and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Gene Ontology: https://geneontology.org/
Human Cell Atlas: https://www.humancellatlas.org/
Human Protein Atlas: http://www.proteinatlas.org
Human Tumor Atlas Network: https://humantumoratlas.org/
Extraneous RNA molecules arising from lysed cells during sample processing that can contaminate gene expression measurements.
Comprehensive references of cell types and states, typically generated using single-cell omics technologies.
A technique for grouping elements of a dataset (for example, cells) by a similarity measure.
Quantitative phenotypes (for example, the abundance of a particular cell state or type within a tissue) that are statistically associated with a health outcome (known as ‘prognostic’ biomarkers) or the likelihood of responding to a given treatment (‘predictive’ biomarker).
A computational technique typically applied to bulk RNA admixtures to infer the proportions and characteristics of specific cell types within a complex tissue sample using gene expression signatures.
A dataset containing information on the expression levels of numerous genes across multiple samples, which in single-cell RNA-seq data are single cells.
Statistical methods to correct data by accounting for the effect of one or more linear relationships between variables.
A representation of high-dimensional data (for example, expression matrix with thousands of genes) that reduces the number of variables, while preserving important structures and relationships in the data for simplified analysis and visualization.
A local multicellular microenvironment, with an exact spatial resolution defined based on the assay and the studied tissue, for example, cells within a radius of 50 µm, or the 200 nearest neighbours of a cell.
A problem solved by determining a mapping between two distributions that minimizes a cost function; in the context of single-cell time series data, the distributions can be cell populations at different time points, and the solution finds a mapping that relates cells at a later time point to their inferred antecedents at one or more earlier time points.
Recurrent sets of multicellular neighbourhoods characterized by a co-occurring set of phenotypic states (for example, transcriptional programmes) in one or more cell types.
A cellular phenotype characterized by small or nuanced differences in gene expression compared to other states of the same cell type.
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Reprints and permissions
Cite this article.
Gulati, G.S., D’Silva, J.P., Liu, Y. et al. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol (2024). https://doi.org/10.1038/s41580-024-00768-2
Download citation
Accepted : 16 July 2024
Published : 21 August 2024
DOI : https://doi.org/10.1038/s41580-024-00768-2
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.
IMAGES
COMMENTS
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
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:
As we mentioned, research methodology refers to the collection of practical decisions regarding what data you'll collect, from who, how you'll collect it and how you'll analyse it. Research design, on the other hand, is more about the overall strategy you'll adopt in your study. For example, whether you'll use an experimental design ...
Research methodology is a crucial framework that guides the entire research process. It involves choosing between various qualitative and quantitative approaches, each tailored to specific research questions and objectives. Your chosen methodology shapes how data is gathered, analysed, and interpreted, ultimately influencing the reliability and ...
1. Qualitative research methodology. Qualitative research methodology is aimed at understanding concepts, thoughts, or experiences. This approach is descriptive and is often utilized to gather in-depth insights into people's attitudes, behaviors, or cultures. Qualitative research methodology involves methods like interviews, focus groups, and ...
1. Focus on your objectives and research questions. The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions. 2.
Research Methodology Example. Detailed Walkthrough + Free Methodology Chapter Template. If you're working on a dissertation or thesis and are looking for an example of a research methodology chapter, you've come to the right place. In this video, we walk you through a research methodology from a dissertation that earned full distinction ...
Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.
A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.
Revised on 10 October 2022. Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research.
15 Research Methodology Examples. Research methodologies can roughly be categorized into three group: quantitative, qualitative, and mixed-methods. Qualitative Research: This methodology is based on obtaining deep, contextualized, non-numerical data. It can occur, for example, through open-ended questioning of research particiapnts in order to ...
Example of a methodology in a research paper. The following example of a methodology in a research paper provides insight into the structure and content to consider when writing your own: This research article discusses the psychological and emotional impact of a mental health support program for employees. The program provided prolonged and ...
I. Groups of Research Methods. There are two main groups of research methods in the social sciences: The empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences.This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured.
Here are some examples of how research methods are applied in different fields: Psychology: Research methods are widely used in psychology to study human behavior, emotions, and mental processes. For example, researchers may use experiments, surveys, and observational studies to understand how people behave in different situations, how they ...
Do yourself a favour and start with the end in mind. Section 1 - Introduction. As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims. As we've discussed many times on the blog ...
Definition, Types, and Examples. Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of ...
Provide the rationality behind your chosen approach. Based on logic and reason, let your readers know why you have chosen said research methodologies. Additionally, you have to build strong arguments supporting why your chosen research method is the best way to achieve the desired outcome. 3. Explain your mechanism.
A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more. You can think of your research methodology as being a formula. One part will be how you plan on putting your research into ...
Research methods are different from research methodologies because they are the ways in which you will collect the data for your research project. The best method for your project largely depends on your topic, the type of data you will need, and the people or items from which you will be collecting data. ... One example is collecting zip codes ...
Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research. Qualitative research is the opposite of quantitative research, which involves collecting and ...
Qualitative research methods. Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data: Conduct interviews to learn about subjective experiences; Host focus groups to gather feedback and personal accounts
A sample of articles on natural experiments, calculating and interpreting prediction intervals, the misuse of the bayes factor, analyzing ecological momentary assessment data, and much more. ... We hope to encourage applications of the presented methods in research and practice across different fields. The Incremental Propensity Score Approach ...
In Malawi in 1999-2002 we conducted research studies using participatory methods to generate population estimates; estimates of the proportion of people in a population with certain characteristics (e.g. the very food insecure); and estimates of the proportion of people in a population that should be targeted by an intervention. We claim that the statistics generated in these studies are at ...
1. Understanding the options. Before we jump into the question of how to choose a research methodology, it's useful to take a step back to understand the three overarching types of research - qualitative, quantitative and mixed methods -based research. Each of these options takes a different methodological approach.
Other interesting articles. If you want to know more about statistics, methodology, or research bias, make sure to check out some of our other articles with explanations and examples. Statistics. Normal distribution. Skewness. Kurtosis. Degrees of freedom. Variance. Null hypothesis.
A Q methodology study was conducted to identify types of ACP perspectives in Koreans with HF. The Q sample (31 statements representing ACP perspectives) was constructed through an extensive literature review and in-depth qualitative interview. The P sample (individuals with HF) completed each grid with a statement on the Q sorting table.
This article is also featured in Energy Insights, which reflects a sample of ongoing research across the Center for Energy Studies ' diverse programmatic areas, all addressing the ever-evolving energy challenges across Texas, the U.S., and the globe. Read more from the inaugural edition.. A New World. Benjamin Franklin is reputed to have said, "An investment in knowledge pays the best ...
Mixed methods research combines elements of quantitative research and qualitative research in order to answer your research question. Mixed methods can help you gain a more complete picture than a standalone quantitative or qualitative study, as it integrates benefits of both methods. Mixed methods research is often used in the behavioral ...
The most widely used deconvolution methods (for example, CIBERSORT/x 168,285,290) use reference profiles from specific cell types, which can be derived from single-cell data or purified bulk data ...