methodologies in research examples

What Is Research Methodology? Types, Process, Examples In Research Design

Research methodology is the backbone of any successful study, providing a structured approach to collecting and analysing data. It encompasses a broad spectrum of methods, each with specific processes and applications, tailored to answer distinct research questions.

This article will explore various types of research methodologies, delve into their processes, and illustrate with examples how they are applied in real-world research.

Understanding these methodologies is essential for any researcher aiming to conduct thorough and impactful studies.

Types Of Research Methodology

Research methodology contains various strategies and approaches to conduct scientific research, each tailored to specific types of questions and data.

Think of research methodology as the master plan for your study. It guides you on why and how to gather and analyse data, ensuring your approach aligns perfectly with your research question.

This methodology includes deciding between qualitative research, which explores topics in depth through interviews or focus groups, or quantitative research, which quantifies data through surveys and statistical analysis.

There is even an option to mix both, and approach called the mixed method.

If you’re analysing the lived experiences of individuals in a specific setting, qualitative methodologies allow you to capture the nuances of human emotions and behaviours through detailed narratives.

Quantitative methodologies would enable you to measure and compare these experiences in a more structured, numerical format.

Choosing a robust methodology not only provides the rationale for the methods you choose but also highlights the research limitations and ethical considerations, keeping your study transparent and grounded.

It’s a thoughtful composition that gives research its direction and purpose, much like how an architect’s plan is essential before the actual construction begins.

Qualitative Research Methodology

Qualitative research dives deep into the social context of a topic. It collects words and textual data rather than numerical data.

Within the family, qualitative research methodologies can be broken down into several approaches: 

Ethnography: Deeply rooted in the traditions of anthropology, you immerse yourself in the community or social setting you’re studying when conducting an ethnography study.

Case Study Research:  Here, you explore the complexity of a single case in detail. This could be an institution, a group, or an individual. You might look into interviews, documents, and reports, to build a comprehensive picture of the subject.

Grounded Theory:  Here, you try to generate theories from the data itself rather than testing existing hypotheses. You might start with a research question but allow your theories to develop as you gather more data.

Narrative Research:  You explore the stories people tell about their lives and personal experiences in their own words. Through techniques like in-depth interviews or life story collections, you analyse the narrative to understand the individual’s experiences.

Discourse Analysis: You analyse written or spoken words to understand the social norms and power structures that underlie the language used. This method can reveal a lot about the social context and the dynamics of power in communication. 

These methods help to uncover patterns in how people think and interact. For example, in exploring consumer attitudes toward a new product, you would likely conduct focus groups or participant observations to gather qualitative data.

This method helps you understand the motivations and feelings behind consumer choices.

Quantitative Research Methodology

Quantitative research relies on numerical data to find patterns and test hypotheses. This methodology uses statistical analysis to quantify data and uncover relationships between variables.

There are several approaches in quantitative research:

Experimental Research:  This is the gold standard when you aim to determine causality. By manipulating one variable and controlling others, you observe changes in the dependent variables.

Survey Research: A popular approach, because of its efficiency in collecting data from a large sample of participants. By using standardised questions, you can gather data that are easy to analyse statistically. 

Correlational Research: This approach tries to identify relationships between two or more variables without establishing a causal link. The strength and direction of these relationships are quantified, albeit without confirming one variable causes another.

Longitudinal Studies: You track variables over time, providing a dynamic view of how situations evolve. This approach requires commitment and can be resource-intensive, but the depth of data they provide is unparalleled.

Cross-sectional Studies: Offers a snapshot of a population at a single point in time. They are quicker and cheaper than longitudinal studies. 

Mixed Research Methodology

Mixed methods research combines both approaches to benefit from the depth of qualitative data and the breadth of quantitative analysis.

You might start with qualitative interviews to develop hypotheses about health behaviours in a community. Then, you could conduct a large-scale survey to test these hypotheses quantitatively.

This approach is particularly useful when you want to explore a new area where previous data may not exist, giving you a comprehensive insight into both the empirical and social dimensions of a research problem.

Factors To Consider When Deciding On Research Methodology

When you dive into a research project, choosing the right methodology is akin to selecting the best tools for building a house.

It shapes how you approach the research question, gather data, and interpret the results. Here are a couple of crucial factors to keep in mind.

Research Question Compatibility

The type of research question you pose can heavily influence the methodology you choose. Qualitative methodologies are superb for exploratory research where you aim to understand concepts, perceptions, and experiences.

If you’re exploring how patients feel about a new healthcare policy, interviews and focus groups would be instrumental.

Quantitative methods are your go-to for questions that require measurable and statistical data, like assessing the prevalence of a medical condition across different regions.

Data Requirements

Consider what data is necessary to address your research question effectively. Qualitative data can provide depth and detail through:

• images, and

This makes qualitative method ideal for understanding complex social interactions or historical contexts. 

Quantitative data, however, offers the breadth and is often numerical, allowing for a broad analysis of patterns and correlations.

If your study aims to investigate both the breadth and depth, a mixed methods approach might be necessary, enabling you to draw on the strengths of both qualitative and quantitative data.

Resources and Constraints

While deciding on research methodology, you must evaluate the resources available, including:

• funding, and

Quantitative research often requires larger samples and hence, might be more costly and time-consuming.

Qualitative research, while generally less resource-intensive, demands substantial time for data collection and analysis, especially if you conduct lengthy interviews or detailed content analysis.

If resources are limited, adapting your methodology to fit these constraints without compromising the integrity of your research is crucial.

Skill Set and Expertise

Your familiarity and comfort level with various research methodologies will significantly affect your choice.

Conducting sophisticated statistical analyses requires a different skill set than carrying out in-depth qualitative interviews.

If your background is in social science, you might find qualitative methods more within your wheelhouse; whereas, a postgraduate student in epidemiology might be more adept at quantitative methods.

It’s also worth considering the availability of workshops, courses, or collaborators who could complement your skills.

Ethical and Practical Considerations

Different methodologies raise different ethical concerns.

In qualitative research, maintaining anonymity and dealing with sensitive information can be challenging, especially when using direct quotes or detailed descriptions from participants.

Quantitative research might involve considerations around participant consent for large surveys or experiments.

Practically, you need to think about the sampling design to ensure it is representative of the population studied. Non-probability sampling might be quicker and cheaper but can introduce bias, limiting the generalisability of your findings.

By meticulously considering these factors, you tailor your research design to not just answer the research questions effectively but also to reflect the realities of your operational environment.

This thoughtful approach helps ensure that your research is not only robust but also practical and ethical, standing up to both academic scrutiny and real-world application.

What Is Research Methodology? Answered

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 validity of your research findings.

Understanding these methodologies ensures that researchers can effectively write research proposal, address their study’s aims and contribute valuable insights to their field.

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

We are here to help you navigate Academia as painlessly as possible. We are supported by our readers and by visiting you are helping us earn a small amount through ads and affiliate revenue - Thank you!

2024 © Academia Insider

Still have questions? Leave a comment

Add Comment

Checklist: Dissertation Proposal

Enter your email id to get the downloadable right in your inbox!

Examples: Edited Papers

Need editing and proofreading services, research methodology guide: writing tips, types, & examples.

• Tags: Academic Research , Research

No dissertation or research paper is complete without the research methodology section. Since this is the chapter where you explain how you carried out your research, this is where all the meat is! Here’s where you clearly lay out the steps you have taken to test your hypothesis or research problem.

Through this blog, we’ll unravel the complexities and meaning of research methodology in academic writing , from its fundamental principles and ethics to the diverse types of research methodology in use today. Alongside offering research methodology examples, we aim to guide you on how to write research methodology, ensuring your research endeavors are both impactful and impeccably grounded!

Ensure your research methodology is foolproof. Learn more

Let’s first take a closer look at a simple research methodology definition:

Defining what is research methodology

Research methodology is the set of procedures and techniques used to collect, analyze, and interpret data to understand and solve a research problem. Methodology in research not only includes the design and methods but also the basic principles that guide the choice of specific methods.

Grasping the concept of methodology in research is essential for students and scholars, as it demonstrates the thorough and structured method used to explore a hypothesis or research question. Understanding the definition of methodology in research aids in identifying the methods used to collect data. Be it through any type of research method approach, ensuring adherence to the proper research paper format is crucial.

Now let’s explore some research methodology types:

Types of research methodology

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 observation. The strength of this methodology lies in its ability to provide contextual richness.

2. Quantitative research methodology

Quantitative research methodology, on the other hand, is focused on quantifying the problem by generating numerical data or data that can be transformed into usable statistics. It uses measurable data to formulate facts and uncover patterns in research. Quantitative research methodology typically involves surveys, experiments, or statistical analysis. This methodology is appreciated for its ability to produce objective results that are generalizable to a larger population.

3. Mixed-Methods research methodology

Mixed-methods research combines both qualitative and quantitative research methodologies to provide a more comprehensive understanding of the research problem. This approach leverages the strengths of both methodologies to provide a deeper insight into the research question of a research paper .

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.

Research methodology examples and application

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.

Importance of methodology in research papers

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.

How to structure the research methods chapter

Wondering how to write the research methodology section? Follow these steps to create a strong methods chapter:

Step 1: Explain your research methodology

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. 

Step 2: Explain the methods you used to test your hypothesis 

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. 

Step 3: Explain how you analyzed the results

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. 

Step 4: Defend your choice of methodology 

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.  

Step 5: Discuss the obstacles you encountered and how you overcame them

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. 

Tips to write an effective methodology chapter

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: 

• Essential Research Tips for Essay Writing
• How to Write a Lab Report: Examples from Academic Editors
• The Essential Types of Editing Every Writer Needs to Know
• Editing and Proofreading Academic Papers: A Short Guide
• The Top 10 Editing and Proofreading Services of 2023

Found this article helpful?

One comment on “ Research Methodology Guide: Writing Tips, Types, & Examples ”

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 vs. You’re: When to Use Your and You’re

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 of service
• Privacy policy
• Partner with us
• Annual report
• Website content
• Marketing material
• Job Applicant
• Cover letter
• Resource Center
• Case studies

• How it works

"Christmas Offer"

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

The Ultimate Guide To Research Methodology

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

What Is Research Methodology?

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.

Facilitating Rigorous Inquiry

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.

Ensuring Reproducibility And Reliability

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.

Guiding Decision-Making Processes

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.

Contributing To Academic Excellence

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.

Enhancing Problem-Solving Abilities

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.

Understanding Research Methodology

In the pursuit of knowledge and discovery, understanding the fundamentals of research methodology is paramount. 

Basics Of Research

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.

Types of Research

Research takes on various forms, each tailored to the nature of the inquiry. Broadly classified, research can be categorized into two main types:

• Quantitative Research: This type involves the collection and analysis of numerical data to identify patterns, relationships, and statistical significance. It is particularly useful for testing hypotheses and making predictions.
• Qualitative Research: Qualitative research focuses on understanding the depth and details of a phenomenon through non-numerical data. It often involves methods such as interviews, focus groups, and content analysis, providing rich insights into complex issues.

Components Of Research Methodology

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

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:

• Exploratory Research: Aimed at gaining insights and familiarity with the topic, often used in the early stages of research.
• Descriptive Research: Involves portraying an accurate profile of a situation or phenomenon, answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.
• Explanatory Research: Seeks to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how.’

Data Collection Methods

Choosing the right data collection methods is crucial for obtaining reliable and relevant information. Common methods include:

• Surveys and Questionnaires: Employed to gather information from a large number of respondents through standardized questions.
• Interviews: In-depth conversations with participants, offering qualitative insights.
• Observation: Systematic watching and recording of behaviour, events, or processes in their natural setting.

Data Analysis Techniques

Once data is collected, analysis becomes imperative to derive meaningful conclusions. Different methodologies exist for quantitative and qualitative data:

• Quantitative Data Analysis: Involves statistical techniques such as descriptive statistics, inferential statistics, and regression analysis to interpret numerical data.
• Qualitative Data Analysis: Methods like content analysis, thematic analysis, and grounded theory are employed to extract patterns, themes, and meanings from non-numerical data.

The research paper we write have:

• Precision and Clarity
• Zero Plagiarism
• High-level Encryption
• Authentic Sources

Choosing a Research Method

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 Methods

Quantitative research involves the collection and analysis of numerical data, providing a structured and objective approach to understanding and explaining phenomena.

Experimental Research

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:

• Controlled Environment: Experiments are conducted in a controlled setting to minimize external influences.
• Random Assignment: Participants are randomly assigned to different experimental conditions.
• Quantitative Data: Data collected is numerical, allowing for statistical analysis.

Applications: Commonly used in scientific studies and psychology to test hypotheses and identify causal relationships.

Survey Research

Survey research gathers information from a sample of individuals through standardized questionnaires or interviews. It aims to collect data on opinions, attitudes, and behaviours.

• Structured Instruments: Surveys use structured instruments, such as questionnaires, to collect data.
• Large Sample Size: Surveys often target a large and diverse group of participants.
• Quantitative Data Analysis: Responses are quantified for statistical analysis.

Applications: Widely employed in social sciences, marketing, and public opinion research to understand trends and preferences.

Descriptive Research

Descriptive research seeks to portray an accurate profile of a situation or phenomenon. It focuses on answering the ‘what,’ ‘who,’ ‘where,’ and ‘when’ questions.

• Observation and Data Collection: This involves observing and documenting without manipulating variables.
• Objective Description: Aim to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: T his can include both types of data, depending on the research focus.

Applications: Useful in situations where researchers want to understand and describe a phenomenon without altering it, common in social sciences and education.

Qualitative Research Methods

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.

• Rich Data Collection: Uses various data sources, such as interviews, observations, and documents.
• Contextual Understanding: Aims to understand the context and unique characteristics of the case.
• Holistic Approach: Examines the case in its entirety.

Applications: Common in social sciences, psychology, and business to investigate complex and specific instances.

Ethnography

Ethnography involves immersing the researcher in the culture or community being studied to gain a deep understanding of their behaviours, beliefs, and practices.

• Participant Observation: Researchers actively participate in the community or setting.
• Holistic Perspective: Focuses on the interconnectedness of cultural elements.
• Qualitative Data: In-depth narratives and descriptions are central to ethnographic studies.

Applications: Widely used in anthropology, sociology, and cultural studies to explore and document cultural practices.

Grounded Theory

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.

• Constant Comparison: Data is continually compared and analyzed during the research process.
• Inductive Reasoning: Theories emerge from the data rather than being imposed on it.
• Iterative Process: The research design evolves as the study progresses.

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, Descriptive, And Explanatory Designs

Exploratory design.

Exploratory research design is employed when a researcher aims to explore a relatively unknown subject or gain insights into a complex phenomenon.

• Flexibility: Allows for flexibility in data collection and analysis.
• Open-Ended Questions: Uses open-ended questions to gather a broad range of information.
• Preliminary Nature: Often used in the initial stages of research to formulate hypotheses.

Applications: Valuable in the early stages of investigation, especially when the researcher seeks a deeper understanding of a subject before formalizing research questions.

Descriptive Design

Descriptive research design focuses on portraying an accurate profile of a situation, group, or phenomenon.

• Structured Data Collection: Involves systematic and structured data collection methods.
• Objective Presentation: Aims to provide an unbiased and factual account of the subject.
• Quantitative or Qualitative Data: Can incorporate both types of data, depending on the research objectives.

Applications: Widely used in social sciences, marketing, and educational research to provide detailed and objective descriptions.

Explanatory Design

Explanatory research design aims to identify the causes and effects of a phenomenon, explaining the ‘why’ and ‘how’ behind observed relationships.

• Causal Relationships: Seeks to establish causal relationships between variables.
• Controlled Variables : Often involves controlling certain variables to isolate causal factors.
• Quantitative Analysis: Primarily relies on quantitative data analysis techniques.

Applications: Commonly employed in scientific studies and social sciences to delve into the underlying reasons behind observed patterns.

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

Cross-sectional designs collect data from participants at a single point in time.

• Snapshot View: Provides a snapshot of a population at a specific moment.
• Efficiency: More efficient in terms of time and resources.
• Limited Temporal Insights: Offers limited insights into changes over time.

Applications: Suitable for studying characteristics or behaviours that are stable or not expected to change rapidly.

Longitudinal Design

Longitudinal designs involve the collection of data from the same participants over an extended period.

• Temporal Sequence: Allows for the examination of changes over time.
• Causality Assessment: Facilitates the assessment of cause-and-effect relationships.
• Resource-Intensive: Requires more time and resources compared to cross-sectional designs.

Applications: Ideal for studying developmental processes, trends, or the impact of interventions over time.

Experimental Vs Non-experimental Designs

Experimental design.

Experimental designs involve manipulating variables under controlled conditions to observe the effect on another variable.

• Causality Inference: Enables the inference of cause-and-effect relationships.
• Quantitative Data: Primarily involves the collection and analysis of numerical data.

Applications: Commonly used in scientific studies, psychology, and medical research to establish causal relationships.

Non-Experimental Design

Non-experimental designs observe and describe phenomena without manipulating variables.

• Natural Settings: Data is often collected in natural settings without intervention.
• Descriptive or Correlational: Focuses on describing relationships or correlations between variables.
• Quantitative or Qualitative Data: This can involve either type of data, depending on the research approach.

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. 

Designing Effective Surveys

Objective Design:

• Clearly define the research objectives to guide the survey design.
• Craft questions that align with the study’s goals and avoid ambiguity.

Structured Format:

• Use a structured format with standardized questions for consistency.
• Include a mix of closed-ended and open-ended questions for detailed insights.

Pilot Testing:

• Conduct pilot tests to identify and rectify potential issues with survey design.
• Ensure clarity, relevance, and appropriateness of questions.

Sampling Strategy:

• Develop a robust sampling strategy to ensure a representative participant group.
• Consider random sampling or stratified sampling based on the research goals.

Conducting Interviews

Establishing Rapport:

• Build rapport with participants to create a comfortable and open environment.
• Clearly communicate the purpose of the interview and the value of participants’ input.

Open-Ended Questions:

• Frame open-ended questions to encourage detailed responses.
• Allow participants to express their thoughts and perspectives freely.

Active Listening:

• Practice active listening to understand areas and gather rich data.
• Avoid interrupting and maintain a non-judgmental stance during the interview.

Ethical Considerations:

• Obtain informed consent and assure participants of confidentiality.
• Be transparent about the study’s purpose and potential implications.

Observation

1. participant observation.

Immersive Participation:

• Actively immerse yourself in the setting or group being observed.
• Develop a deep understanding of behaviours, interactions, and context.

Field Notes:

• Maintain detailed and reflective field notes during observations.
• Document observed patterns, unexpected events, and participant reactions.

Ethical Awareness:

• Be conscious of ethical considerations, ensuring respect for participants.
• Balance the role of observer and participant to minimize bias.

2. Non-participant Observation

Objective Observation:

• Maintain a more detached and objective stance during non-participant observation.
• Focus on recording behaviours, events, and patterns without direct involvement.

Data Reliability:

• Enhance the reliability of data by reducing observer bias.
• Develop clear observation protocols and guidelines.

Contextual Understanding:

• Strive for a thorough understanding of the observed context.
• Consider combining non-participant observation with other methods for triangulation.

Archival Research

1. using existing data.

Identifying Relevant Archives:

• Locate and access archives relevant to the research topic.
• Collaborate with institutions or repositories holding valuable data.

Data Verification:

• Verify the accuracy and reliability of archived data.
• Cross-reference with other sources to ensure data integrity.

Ethical Use:

• Adhere to ethical guidelines when using existing data.
• Respect copyright and intellectual property rights.

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

• Address the possibility of incomplete or inaccurate archival records.
• Acknowledge limitations and uncertainties in the data.

Temporal Bias:

• Recognize potential temporal biases in archived data.
• Consider the historical context and changes that may impact interpretation.

Access Limitations:

• Address potential limitations in accessing certain archives.
• Seek alternative sources or collaborate with institutions to overcome barriers.

Common Challenges in Research Methodology

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 Issues

Sampling bias:.

• The presence of sampling bias can lead to an unrepresentative sample, affecting the generalizability of findings.
• Employ random sampling methods and ensure the inclusion of diverse participants to reduce bias.

Sample Size Determination:

• Determining an appropriate sample size is a delicate balance. Too small a sample may lack statistical power, while an excessively large sample may strain resources.
• Conduct a power analysis to determine the optimal sample size based on the research objectives and expected effect size.

Data Quality And Validity

Measurement error:.

• Inaccuracies in measurement tools or data collection methods can introduce measurement errors, impacting the validity of results.
• Pilot test instruments, calibrate equipment, and use standardized measures to enhance the reliability of data.

Construct Validity:

• Ensuring that the chosen measures accurately capture the intended constructs is a persistent challenge.
• Use established measurement instruments and employ multiple measures to assess the same construct for triangulation.

Time And Resource Constraints

Timeline pressures:.

• Limited timeframes can compromise the depth and thoroughness of the research process.
• Develop a realistic timeline, prioritize tasks, and communicate expectations with stakeholders to manage time constraints effectively.

Resource Availability:

• Inadequate resources, whether financial or human, can impede the execution of research activities.
• Seek external funding, collaborate with other researchers, and explore alternative methods that require fewer resources.

Managing Bias in Research

Selection bias:.

• Selecting participants in a way that systematically skews the sample can introduce selection bias.
• Employ randomization techniques, use stratified sampling, and transparently report participant recruitment methods.

Confirmation Bias:

• Researchers may unintentionally favour information that confirms their preconceived beliefs or hypotheses.
• Adopt a systematic and open-minded approach, use blinded study designs, and engage in peer review to mitigate confirmation bias.

Tips On How To Write A Research Methodology

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:

Tip 1. Clear Research Objectives

Well-defined research objectives guide the entire research process. Clearly articulate the purpose of your study, outlining specific research questions or hypotheses.

Tip 2. Comprehensive Literature Review

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.

Tip 3. Detailed Research Plan

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.

Tip 4. Ethical Considerations

Ethical practices are fundamental to maintaining the integrity of research. Address ethical considerations early, obtain necessary approvals, and ensure participant rights are safeguarded.

Tip 5. Stay Updated On Methodologies

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.

Tip 6. Adaptability In Methods

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.

Tip 7. Iterative Approach

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.

Frequently Asked Questions

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.

What are the methodologies in research?

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.

How to write research methodology?

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.

How to write the methodology section of a research paper?

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.

What is mixed research methodology?

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.

You May Also Like

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?

USEFUL LINKS

Learning resources.

COMPANY DETAILS

• How It Works

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

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:

• The type of research you conducted
• How you collected and analysed your data
• Any tools or materials you used in the research
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

Instantly correct all language mistakes in your text

Be assured that you'll submit flawless writing. Upload your document to correct all your mistakes.

Table of contents

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 only proofreading tool specialized in correcting academic writing

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.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

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?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative 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.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

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.

Qualitative methods

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.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyse?
• How did you select them?

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

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:

• How you prepared the data before analysing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorising and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

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 .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviours, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalised beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalisable.

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.

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. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

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.

Cite this Scribbr article

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/

Is this article helpful?

Shona McCombes

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.

15 Research Methodology Examples

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.

• 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 understand human behavior. It’s all about describing and analyzing subjective phenomena such as emotions or experiences.
• Quantitative Research: This methodology is rationally-based and relies heavily on numerical analysis of empirical data . With quantitative research, you aim for objectivity by creating hypotheses and testing them through experiments or surveys, which allow for statistical analyses.
• Mixed-Methods Research: Mixed-methods research combines both previous types into one project. We have more flexibility when designing our research study with mixed methods since we can use multiple approaches depending on our needs at each time. Using mixed methods can help us validate our results and offer greater predictability than just either type of methodology alone could provide.

Below are research methodologies that fit into each category.

Qualitative Research Methodologies

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).

2. Grounded Theory

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.

3. Ethnography

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.

4. Phenomenology

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.

5. Narrative Research

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).

6. Discourse Analysis

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.

7. Action Research

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

Quantitative Research Methodologies

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.

9. Non-Experimental Design

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.

10. Cross-Sectional Design

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.

11. Longitudinal Design

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.

12. Quasi-Experimental Design

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.

13. Correlational Research

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.

Mixed-Methods Research Methodologies

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.

15. Sequential Exploratory Design (QUAL→QUAN)

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 .

• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 6 Types of Societies (With 21 Examples)
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 25 Public Health Policy Examples
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link 15 Cultural Differences Examples
• Tio Gabunia (B.Arch, M.Arch) #molongui-disabled-link Social Interaction Types & Examples (Sociology)

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Green Flags in a Relationship
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Signs you're Burnt Out, Not Lazy
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Toxic Things Parents Say to their Children
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Red Flags Early in a Relationship

Leave a Comment Cancel Reply

Your email address will not be published. Required fields are marked *

• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

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.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

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.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-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. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

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:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

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.

Writing Tip

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 .

Another Writing Tip

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.

Yet Another Writing Tip

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.

• << Previous: Scholarly vs. Popular Publications
• Next: Qualitative Methods >>
• Last Updated: Aug 21, 2024 8:54 AM
• URL: https://libguides.usc.edu/writingguide

• Privacy Policy

Home » Research Methods – Types, Examples and Guide

Research Methods – Types, Examples and Guide

Table of Contents

Research Methods

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

Types of Research Methods are as follows:

Qualitative research Method

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 Method

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

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.

Key Differences Between Research Methods

The following Table shows the key differences between Quantitative, Qualitative and Mixed Research Methods

Examples of Research Methods

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.

Applications of Research Methods

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:

• 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 respond to different stimuli, and how their brains process information.
• Sociology : Sociologists use research methods to study social phenomena, such as social inequality, social change, and social relationships. Researchers may use surveys, interviews, and observational studies to collect data on social attitudes, beliefs, and behaviors.
• Medicine : Research methods are essential in medical research to study diseases, test new treatments, and evaluate their effectiveness. Researchers may use clinical trials, case studies, and laboratory experiments to collect data on the efficacy and safety of different medical treatments.
• Education : Research methods are used in education to understand how students learn, how teachers teach, and how educational policies affect student outcomes. Researchers may use surveys, experiments, and observational studies to collect data on student performance, teacher effectiveness, and educational programs.
• Business : Research methods are used in business to understand consumer behavior, market trends, and business strategies. Researchers may use surveys, focus groups, and observational studies to collect data on consumer preferences, market trends, and industry competition.
• Environmental science : Research methods are used in environmental science to study the natural world and its ecosystems. Researchers may use field studies, laboratory experiments, and observational studies to collect data on environmental factors, such as air and water quality, and the impact of human activities on the environment.
• Political science : Research methods are used in political science to study political systems, institutions, and behavior. Researchers may use surveys, experiments, and observational studies to collect data on political attitudes, voting behavior, and the impact of policies on society.

Purpose of Research Methods

Research methods serve several purposes, including:

• Identify research problems: Research methods are used to identify research problems or questions that need to be addressed through empirical investigation.
• Develop hypotheses: Research methods help researchers develop hypotheses, which are tentative explanations for the observed phenomenon or relationship.
• Collect data: Research methods enable researchers to collect data in a systematic and objective way, which is necessary to test hypotheses and draw meaningful conclusions.
• Analyze data: Research methods provide tools and techniques for analyzing data, such as statistical analysis, content analysis, and discourse analysis.
• Test hypotheses: Research methods allow researchers to test hypotheses by examining the relationships between variables in a systematic and controlled manner.
• Draw conclusions : Research methods facilitate the drawing of conclusions based on empirical evidence and help researchers make generalizations about a population based on their sample data.
• Enhance understanding: Research methods contribute to the development of knowledge and enhance our understanding of various phenomena and relationships, which can inform policy, practice, and theory.

When to Use Research Methods

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:

• To investigate a problem : Research methods can be used to investigate a problem or a research question in a particular field. This can help in identifying the root cause of the problem and developing solutions.
• To gather data: Research methods can be used to collect data on a particular subject. This can be done through surveys, interviews, observations, experiments, and more.
• To evaluate programs : Research methods can be used to evaluate the effectiveness of a program, intervention, or policy. This can help in determining whether the program is meeting its goals and objectives.
• To explore new areas : Research methods can be used to explore new areas of inquiry or to test new hypotheses. This can help in advancing knowledge in a particular field.
• To make informed decisions : Research methods can be used to gather information and data to support informed decision-making. This can be useful in various fields such as healthcare, business, and education.

Advantages of Research Methods

Research methods provide several advantages, including:

• Objectivity : Research methods enable researchers to gather data in a systematic and objective manner, minimizing personal biases and subjectivity. This leads to more reliable and valid results.
• Replicability : A key advantage of research methods is that they allow for replication of studies by other researchers. This helps to confirm the validity of the findings and ensures that the results are not specific to the particular research team.
• Generalizability : Research methods enable researchers to gather data from a representative sample of the population, allowing for generalizability of the findings to a larger population. This increases the external validity of the research.
• Precision : Research methods enable researchers to gather data using standardized procedures, ensuring that the data is accurate and precise. This allows researchers to make accurate predictions and draw meaningful conclusions.
• Efficiency : Research methods enable researchers to gather data efficiently, saving time and resources. This is especially important when studying large populations or complex phenomena.
• Innovation : Research methods enable researchers to develop new techniques and tools for data collection and analysis, leading to innovation and advancement in the field.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Institutional Review Board – Application Sample...

Ethnographic Research -Types, Methods and Guide

Research Process – Steps, Examples and Tips

Conceptual Framework – Types, Methodology and...

Table of Contents – Types, Formats, Examples

Questionnaire – Definition, Types, and Examples

• Resources Home 🏠
• Try SciSpace Copilot
• Search research papers
• Add Copilot Extension
• Try AI Detector
• Try Paraphraser
• Try Citation Generator
• April Papers
• June Papers
• July Papers

Here's What You Need to Understand About Research Methodology

Table of Contents

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.

What is the definition of a research methodology?

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.

The need for a good research methodology

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.

What is the basic structure of a research methodology?

Usually, you must ensure to include the following stated aspects while deciding over the basic structure of your research methodology:

1. Your research procedure

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.

2. 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

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.

4. Significance of outcomes

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.

5. Reader’s advice

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.

6. Explain your sample space

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.

7. Challenges and limitations

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.

The importance of a good research methodology

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:

• When someone tries to duplicate or expand upon your research after few years.
• If a contradiction or conflict of facts occurs at a later time. This gives you the security you need to deal with these contradictions while still being able to defend your approach.
• Gaining a tactical approach in getting your research completed in time. Just ensure you are using the right approach while drafting your research methodology, and it can help you achieve your desired outcomes. Additionally, it provides a better explanation and understanding of the research question itself.
• Documenting the results so that the final outcome of the research stays as you intended it to be while starting.

Instruments you could use while writing a good research methodology

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:

a. Interviews (One-on-One or a Group)

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.

c. Sample Groups

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.

d. Observations

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.

Types of 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.

1. Qualitative research methodology

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.

2. Quantitative research methodology

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.

3. Amalgam 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.

Final words: How to decide which is the best research methodology?

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.

Frequently Asked Questions (FAQs) about Research Methodology

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.

2. What are the types of research methodology?

There generally exists four types of research methodology i.e.

• Observation
• Experimental
• Derivational

3. What is the true meaning of research methodology?

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.

4. Where lies the importance of 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.

You might also like

Consensus GPT vs. SciSpace GPT: Choose the Best GPT for Research

Literature Review and Theoretical Framework: Understanding the Differences

Using AI for research: A beginner’s guide

Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

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:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

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:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

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:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

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.

➡️ 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.

Research Methodologies

• What are research designs?
• What are research methodologies?

What are research methods?

Quantitative research methods, qualitative research methods, mixed method approach, selecting the best research method.

• Additional Sources

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.

• Closed-ended questionnaires/survey: These types of questionnaires or surveys are like "multiple choice" tests, where participants must select from a list of premade answers.  According to the content of the question, they must select the one that they agree with the most.  This approach is the simplest form of quantitative research because the data is easy to combine and quantify.
• Structured interviews: These are a common research method in market research because the data can be quantified.  They are strictly designed for little "wiggle room" in the interview process so that the data will not be skewed.  You can conduct structured interviews in-person, online, or over the phone (Dawson, 2019).

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):

• Keep the questions brief and simple.
• Eliminate any potential bias from your questions.  Make sure that they do not word things in a way that favor one perspective over another.
• If your topic is very sensitive, you may want to ask indirect questions rather than direct ones.  This prevents participants from being intimidated and becoming unwilling to share their true responses.
• If you are using a closed-ended question, try to offer every possible answer that a participant could give to that question.
• Do not ask questions that assume something of the participant.  The question "How often do you exercise?" assumes that the participant exercises (when they may not), so you would want to include a question that asks if they exercise at all before asking them how often.
• Try and keep the questionnaire as short as possible.  The longer a questionnaire takes, the more likely the participant will not complete it or get too tired to put truthful answers.
• Promise confidentiality to your participants at the beginning of the questionnaire.

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:

• Nominal: These are numbers where the order of the numbers do not matter.  They aim to identify separate information.  One example is collecting zip codes from research participants.  The order of the numbers does not matter, but the series of numbers in each zip code indicate different information (Adamson and Prion, 2013).
• Ordinal: Also known as rankings because the order of these numbers matter.  This is when items are given a specific rank according to specific criteria.  A common example of ordinal measurements include ranking-based questionnaires, where participants are asked to rank items from least favorite to most favorite.  Another common example is a pain scale, where a patient is asked to rank their pain on a scale from 1 to 10 (Adamson and Prion, 2013).
• Interval: This is when the data are ordered and the distance between the numbers matters to the researcher (Adamson and Prion, 2013).  The distance between each number is the same.  An example of interval data is test grades.
• Ratio: This is when the data are ordered and have a consistent distance between numbers, but has a "zero point."  This means that there could be a measurement of zero of whatever you are measuring in your study (Adamson and Prion, 2013).  An example of ratio data is measuring the height of something because the "zero point" remains constant in all measurements.  The height of something could also be zero.

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

• Only requires one meeting to get different types of responses.
• Less researcher bias due to participants being able to speak openly.
• Helps participants overcome insecurities or fears about a topic.
• The researcher can also consider the impact of participant interaction.

Disadvantages of Focus Groups

• Participants may feel uncomfortable to speak in front of an audience, especially if the topic is sensitive or controversial.
• Since participation is voluntary, not every participant may contribute equally to the discussion.
• Participants may impact what others say or think.
• A researcher may feel intimidated by running a focus group on their own.
• A researcher may need extra funds/resources to provide a safe space to host the focus group.
• Because the data is collective, it may be difficult to determine a participant's individual thoughts about the research topic.

Observation

There are two ways to conduct research observations:

• Direct Observation: The researcher observes a participant in an environment.  The researcher often takes notes or uses technology to gather data, such as a voice recorder or video camera.  The researcher does not interact or interfere with the participants.  This approach is often used in psychology and health studies (Dawson, 2019).
• Participant Observation:  The researcher interacts directly with the participants to get a better understanding of the research topic.  This is a common research method when trying to understand another culture or community.  It is important to decide if you will conduct a covert (participants do not know they are part of the research) or overt (participants know the researcher is observing them) observation because it can be unethical in some situations (Dawson, 2019).

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):

• Behavioral theory
• Change theory
• Cognitive theory
• Content analysis
• Cross-sectional analysis
• Developmental theory
• Feminist theory
• Gender theory
• Marxist theory
• Queer theory
• Systems theory
• Transformational theory

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).

• Open-ended and closed-ended questionnaires: This approach means implementing elements of both questionnaire types into your data collection.  Participants may answer some questions with premade answers and write their own answers to other questions.  The advantage to this method is that you benefit from both types of data collection to get a broader understanding of you participants.  However, you must think carefully about how you will analyze this data to arrive at a conclusion.

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:

• Are you good with numbers and mathematics?
• Would you be interested in conducting interviews with human subjects?
• Would you enjoy creating a questionnaire for participants to complete?
• Do you prefer written communication or face-to-face interaction?
• What skills or experiences do you have that might help you with your research?  Do you have any experiences from past research projects that can help with this one?
• How much time do you have to complete the research?  Some methods take longer to collect data than others.
• What is your budget?  Do you have adequate funding to conduct the research in the method you  want?
• How much data do you need?  Some research topics need only a small amount of data while others may need significantly larger amounts.
• What is the purpose of your research? This can provide a good indicator as to what research method will be most appropriate.
• << Previous: What are research methodologies?
• Next: Additional Sources >>
• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

New Content From Advances in Methods and Practices in Psychological Science

• Advances in Methods and Practices in Psychological Science
• Meta-Analysis
• Methodology
• Statistical Analysis

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.

Scientists Propose Upgrades to Research-Methods Education for Psychology Students 

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.

Multilab Replication Challenges Long-held Theories on Cognitive Dissonance

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.

When Things Don’t Go According to Plan

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.

Privacy Overview

How to generate statistics and influence policy using participatory methods in research : reflections on work in Malawi, 1999-2002

Ids item types, copyright holder, usage metrics.

How To Choose Your Research Methodology

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.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

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.

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.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

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.

2. How to choose a research methodology

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:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

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:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

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.

Need a helping hand?

Factor #2: The disciplinary norms

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.

Factor #3: Practicalities

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:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

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.

Constraint #2: Time

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. 

Constraint #3: Money

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:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

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.

Constraint #4: Equipment & software

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.

Constraint #5: Your knowledge and skillset

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:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

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.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

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.

Psst... there’s more!

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

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.

Submit a Comment Cancel reply

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.

• Print Friendly

• Economics & Finance
• Foreign Policy
• Domestic Policy
• Health & Science
• All Publications

AI and Energy: Advanced Tools for Knowledge Discovery

Table of Contents

Christopher Bronk

Share this publication.

• Print 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 .

A New World

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. 

New Methods for Understanding a World Remade 

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.

Energy Policy and Technology

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.

Energy and Geopolitics

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. 

The Path Forward

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.

Related Research

Fueling Transportation Is Becoming More Complex

Reimagining Sustainability: A Systems Approach for a Resilient Future

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Mixed Methods Research | Definition, Guide & Examples

Mixed Methods Research | Definition, Guide & Examples

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.

• To what extent does the frequency of traffic accidents ( quantitative ) reflect cyclist perceptions of road safety ( qualitative ) in Amsterdam?
• How do student perceptions of their school environment ( qualitative ) relate to differences in test scores ( quantitative ) ?
• How do interviews about job satisfaction at Company X ( qualitative ) help explain year-over-year sales performance and other KPIs ( quantitative ) ?
• How can voter and non-voter beliefs about democracy ( qualitative ) help explain election turnout patterns ( quantitative ) in Town X?
• How do average hospital salary measurements over time (quantitative) help to explain nurse testimonials about job satisfaction (qualitative) ?

Table of contents

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:

• Generalizability : Qualitative research usually has a smaller sample size , and thus is not generalizable. In mixed methods research, this comparative weakness is mitigated by the comparative strength of “large N,” externally valid quantitative research.
• Contextualization: Mixing methods allows you to put findings in context and add richer detail to your conclusions. Using qualitative data to illustrate quantitative findings can help “put meat on the bones” of your analysis.
• Credibility: Using different methods to collect data on the same subject can make your results more credible. If the qualitative and quantitative data converge, this strengthens the validity of your conclusions. This process is called triangulation .

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.

Here's why students love Scribbr's proofreading services

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:

• Your research approach ( inductive vs deductive )
• Your research questions
• What kind of data is already available for you to use
• What kind of data you’re able to collect yourself.

Here are a few of the most common mixed methods designs.

Convergent parallel

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.

• On the qualitative side, you analyze cyclist complaints via the city’s database and on social media to find out which areas are perceived as dangerous and why.
• On the quantitative side, you analyze accident reports in the city’s database to find out how frequently accidents occur in different areas of the city.

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.

Explanatory sequential

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.

Exploratory sequential

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.

“Best of both worlds” analysis

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.

Method flexibility

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.

Differing or conflicting results

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.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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.

• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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 :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Cite this Scribbr article

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/

Is this article helpful?

Tegan George

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.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 21 August 2024

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

• Gunsagar S. Gulati   ORCID: orcid.org/0000-0003-2798-6220 1   na1 ,
• Jeremy Philip D’Silva   ORCID: orcid.org/0009-0005-0053-7726 2   na1 ,
• Yunhe Liu   ORCID: orcid.org/0000-0002-2120-952X 3 ,
• Linghua Wang 3 , 4 &
• Aaron M. Newman   ORCID: orcid.org/0000-0002-1857-8172 2 , 5 , 6 , 7  

Nature Reviews Molecular Cell Biology ( 2024 ) Cite this article

13 Altmetric

Metrics details

• Gene expression analysis

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 options

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

• Purchase on SpringerLink
• Instant access to full article PDF

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope

Spatial charting of single-cell transcriptomes in tissues

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

Acknowledgements

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.

Author information

These authors contributed equally: Gunsagar S. Gulati, Jeremy Philip D’Silva.

Authors and Affiliations

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

Contributions

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.

Corresponding author

Correspondence to Aaron M. Newman .

Ethics declarations

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

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.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

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.

Rights and permissions

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

About this article

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

Share this article

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

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.