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Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

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

Structure of Research Methodology

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

I. Introduction

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

II. Research Design

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

III. Data Collection Methods

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

IV. Data Analysis Methods

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

V. Ethical Considerations

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

VI. Limitations

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

VII. Conclusion

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

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

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

Qualitative Research Methodology

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

Mixed-Methods Research Methodology

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

Case Study Research Methodology

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

Action Research Methodology

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

Experimental Research Methodology

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

Survey Research Methodology

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

Grounded Theory Research Methodology

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

Research Methodology Example

An Example of Research Methodology could be the following:

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

Introduction:

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

Research Design:

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

Participants:

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

Intervention :

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

Data Collection:

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

Data Analysis:

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

Ethical Considerations:

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

Data Management:

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

Limitations:

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

Conclusion:

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

How to Write Research Methodology

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

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

When to Write Research Methodology

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

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

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

Applications of Research Methodology

Here are some of the applications of research methodology:

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

Purpose of Research Methodology

Research methodology serves several important purposes, including:

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

Advantages of Research Methodology

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

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

Research Methodology Vs Research Methods

About the author.

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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Organizing Your Social Sciences Research Paper

• 6. The Methodology
• Purpose of Guide
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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.

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How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

Need a helping hand?

How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover. 

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

Methodological Choice #2 – Research Type

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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• 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 ).

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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.

Prevent plagiarism, run a free check.

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.

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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.

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• How it works

Research Methodology

The research methodology is a part of your research paper that describes your research process in detail. It would help if you always tried to make the section of the research methodology enjoyable.

As you describe the procedure that has already been completed, you need to write it in the past tense.

Your research methodology should explain:

What was the purpose of your research?

What type of research method is used?

What were the data collecting methods?

How did you analyze the data?

What kind of resources has been used in your research?

Why did you choose these methods?

How to Write a Research Methodology?

Start writing your research methodology with the research problem giving a clear picture of your study’s purpose. It’ll help your readers focus on the research objectives and understand the remaining procedure of your research.

You should explain:

What type of research have you conducted?

The types of research can be categorized from the following perspectives;

Application of the study

Aim of the research

Mode of inquiry

Research approach

While talking about the research methods, you should highlight the key points, such as:

• The objective of choosing a specific research method.
• Is the purpose of the study fulfilled?
• The criteria of validity and reliability
• Did you meet the ethical considerations?

What kind of data gathering methods you’ve used in your research?

There are three types of data collecting methods such as:

Qualitative Method

Qualitative research is based on quality, and it looks in-depth at non-numerical data. It enables us to understand the comprehensive details of the problem. The researcher prepares open-ended questions to gather as much information as possible.

Quantitative Method

The quantitative research is associated with the aspects of measurement, quantity, and extent. It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population.

Mixed Methods

When you combine quantitative and qualitative methods of research, the resulting approach becomes mixed methods of research.

Example: In quantitative correlation research , you aim to identify the cause-and-effect relationship between two or more variables. It would help if you also focused on explaining the difference between correlation and causation.

Example: In a qualitative research case study , your research’s focus is to find answers to how and why questions. You need to collect data collection from multiple sources over time. You need to analyse real-world problems in-depth, then you can use the method of the case study.

Describe the Research Methods

After explaining the research method you have used, you should describe the data collection methods you used. Mention the procedure and materials you used in your research.

Qualitative Methods

Interview/Focus Group Discussion

Describe the details and criteria of the interviews and. You should include the following points:

The type of questionnaire you have used in your interview.

The procedure for selecting participants.

The size of your sample (number of participation)

The duration and location of interviews.

Observation

Describe the procedure of your observation and include the following points:

Who were the participants of your observation?

How did you get access to that specific group?

How did you record the data? (written form, audio or video recording)

Archival Data

Here you have to describe the existing data you’ve’ used. You should explain:

What type of resources have you used? (texts, images, audio, videos)

• How did you get access to them?

To seek in-depth information about the stress level among men and women, semi-structured interviews were conducted with ten men and ten women of company X. The participants were aged between 20-40. The interviews were held in the canteen to create a stress-free environment that lasted 15 minutes each. The responses were written and filmed.

Quantitative Methods

Describe the entire procedure of your survey. Include the following points:

What type of survey have you conducted? (Questionnaire/interview/ rating scale/ Online Survey)

Who were the participants of your survey? How did you select them?

What was the sample size ?

What type of questions you’ve used in your survey? (open-ended/closed-ended)

How many questions have you used?

What was the response rate of the participants?

Experiments

Explain the detailed procedure you have followed in your experiment. Try to provide as much information you can provide. Include the following points:

The type of your experimental design .

Sampling method you’ve used to select subjects.

Tools and techniques used in the experiment.

The way you identified a cause-and-effect relationship between the variables.

Describe the existing data you’ve used in your research. Include the following points:

• What type of resources have you used? (journals, newspapers, books, online content)
• Who is the author of the source?
• Who published it? When?

The survey included ten multiple-choice questions and ten open-ended questions. The survey’s objective is to determine the stress level of working women who have to deal with household responsibilities. From 17-20 Jan 2018, between 11:00 to 13:00, the survey questionnaire was distributed among the women at the working counters. The participants were given 10 minutes to fill the questionnaire. Out of 500 participants, 450 responded, and 350 were included in the analysis.

Describe Methods of Data Analysis

In this section, you should briefly describe the methods you’ve used to analyse the data you’ve collected.

The qualitative method includes analysing language, images, audio, videos, or any textual data (textual analysis). The following types of methods are used in textual analysis .

Discourse analysis : Discourse analysis is an essential aspect of studying a language and its uses in day-to-day life.

Content analysis : It is a method of studying and retrieving meaningful information from documents

Thematic analysis: It’s a method of identifying patterns of themes in the collected information, such as face-to-face interviews, texts, and transcripts.

Example: After collecting the data, it was checked thoroughly to find the missing information. The interviews were transcribed, and textual analysis was conducted. The repetitions of the text, types of colours displayed, the tone of the speakers was measured.

Quantitative data analysis is used for analysing numerical data. Include the following points:

The methods of preparing data before analysing it.

Which statistical test you have used? (one-ended test, two-ended test)

The type of software you’ve used.

After collecting the data, it was checked thoroughly to find out the missing information. The coding system was used to interpret the data.

Provide Background and Justification

Many research methods are available, from standard to an averaged approach based on the requirements and abilities. In the research methodology section, it’s essential to mention the reasons behind selecting a specific research method.

You should also explain why you did not choose any other standard approach to your topic when it fits your requirements. Talk about your research objectives and highlight the points that could affect your research procedure if you select another research method.

You can discuss the limitations of other research methods compared to your research requirements and the method you’ve used.

Ethnographic research requires a lot of time, and one has to struggle a lot to gain access to the community. A researcher has to spend time with the target group in their natural environment. Sometimes, it’s difficult for a researcher to introduce himself as a researcher/participant with the community.

The online survey does not provide reliable responses. The only benefit of conducting an online survey would be its quick response rate and cost-effectiveness.

Points to Remember while Writing Methodology

While writing your methodology, you need to keep in mind that you don’t need to make it complicated with unnecessary details.

The aim of your writing a research methodology is not merely discussing the methods and techniques you’ve used.

You have to provide a detailed account of the procedure you’ve followed, the obstacles you faced, and the way you overcome them.

Your research question and objectives of the research are the base of your research. You should discuss the objectives and explain how this specific method helped you answer your research question. You can use goals and outcomes as evidence to support your discussion.

If you’ve used any standard method in your research, you don’t need to provide many details about it as it would be common in your field. However, if you’ve used any specific approach rarely used in your field, you should explain it in detail. Your explanation and information can help other researchers in their research.

Your methodology should be well-structured and easy to understand, with all the necessary information, evidence to support your argument.

After gathering the data, it’s essential to credit the sources you have used in your research. Mention the resources you’ve used, the way you got access to those resources. Use any suitable referencing style to cite sources such as APA, MLA, and Chicago, etc.

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• Research Process

Choosing the Right Research Methodology: A Guide for Researchers

• 3 minute read
• 34.6K views

Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories. 

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis. 

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable. 

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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• Published: 07 September 2020

A tutorial on methodological studies: the what, when, how and why

• Lawrence Mbuagbaw   ORCID: orcid.org/0000-0001-5855-5461 1 , 2 , 3 ,
• Daeria O. Lawson 1 ,
• Livia Puljak 4 ,
• David B. Allison 5 &
• Lehana Thabane 1 , 2 , 6 , 7 , 8  

BMC Medical Research Methodology volume  20 , Article number:  226 ( 2020 ) Cite this article

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Methodological studies – studies that evaluate the design, analysis or reporting of other research-related reports – play an important role in health research. They help to highlight issues in the conduct of research with the aim of improving health research methodology, and ultimately reducing research waste.

We provide an overview of some of the key aspects of methodological studies such as what they are, and when, how and why they are done. We adopt a “frequently asked questions” format to facilitate reading this paper and provide multiple examples to help guide researchers interested in conducting methodological studies. Some of the topics addressed include: is it necessary to publish a study protocol? How to select relevant research reports and databases for a methodological study? What approaches to data extraction and statistical analysis should be considered when conducting a methodological study? What are potential threats to validity and is there a way to appraise the quality of methodological studies?

Appropriate reflection and application of basic principles of epidemiology and biostatistics are required in the design and analysis of methodological studies. This paper provides an introduction for further discussion about the conduct of methodological studies.

Peer Review reports

The field of meta-research (or research-on-research) has proliferated in recent years in response to issues with research quality and conduct [ 1 , 2 , 3 ]. As the name suggests, this field targets issues with research design, conduct, analysis and reporting. Various types of research reports are often examined as the unit of analysis in these studies (e.g. abstracts, full manuscripts, trial registry entries). Like many other novel fields of research, meta-research has seen a proliferation of use before the development of reporting guidance. For example, this was the case with randomized trials for which risk of bias tools and reporting guidelines were only developed much later – after many trials had been published and noted to have limitations [ 4 , 5 ]; and for systematic reviews as well [ 6 , 7 , 8 ]. However, in the absence of formal guidance, studies that report on research differ substantially in how they are named, conducted and reported [ 9 , 10 ]. This creates challenges in identifying, summarizing and comparing them. In this tutorial paper, we will use the term methodological study to refer to any study that reports on the design, conduct, analysis or reporting of primary or secondary research-related reports (such as trial registry entries and conference abstracts).

In the past 10 years, there has been an increase in the use of terms related to methodological studies (based on records retrieved with a keyword search [in the title and abstract] for “methodological review” and “meta-epidemiological study” in PubMed up to December 2019), suggesting that these studies may be appearing more frequently in the literature. See Fig.  1 .

Trends in the number studies that mention “methodological review” or “meta-

epidemiological study” in PubMed.

The methods used in many methodological studies have been borrowed from systematic and scoping reviews. This practice has influenced the direction of the field, with many methodological studies including searches of electronic databases, screening of records, duplicate data extraction and assessments of risk of bias in the included studies. However, the research questions posed in methodological studies do not always require the approaches listed above, and guidance is needed on when and how to apply these methods to a methodological study. Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research.

The objectives of this paper are to provide some insights on how to conduct methodological studies so that there is greater consistency between the research questions posed, and the design, analysis and reporting of findings. We provide multiple examples to illustrate concepts and a proposed framework for categorizing methodological studies in quantitative research.

What is a methodological study?

Any study that describes or analyzes methods (design, conduct, analysis or reporting) in published (or unpublished) literature is a methodological study. Consequently, the scope of methodological studies is quite extensive and includes, but is not limited to, topics as diverse as: research question formulation [ 11 ]; adherence to reporting guidelines [ 12 , 13 , 14 ] and consistency in reporting [ 15 ]; approaches to study analysis [ 16 ]; investigating the credibility of analyses [ 17 ]; and studies that synthesize these methodological studies [ 18 ]. While the nomenclature of methodological studies is not uniform, the intents and purposes of these studies remain fairly consistent – to describe or analyze methods in primary or secondary studies. As such, methodological studies may also be classified as a subtype of observational studies.

Parallel to this are experimental studies that compare different methods. Even though they play an important role in informing optimal research methods, experimental methodological studies are beyond the scope of this paper. Examples of such studies include the randomized trials by Buscemi et al., comparing single data extraction to double data extraction [ 19 ], and Carrasco-Labra et al., comparing approaches to presenting findings in Grading of Recommendations, Assessment, Development and Evaluations (GRADE) summary of findings tables [ 20 ]. In these studies, the unit of analysis is the person or groups of individuals applying the methods. We also direct readers to the Studies Within a Trial (SWAT) and Studies Within a Review (SWAR) programme operated through the Hub for Trials Methodology Research, for further reading as a potential useful resource for these types of experimental studies [ 21 ]. Lastly, this paper is not meant to inform the conduct of research using computational simulation and mathematical modeling for which some guidance already exists [ 22 ], or studies on the development of methods using consensus-based approaches.

When should we conduct a methodological study?

Methodological studies occupy a unique niche in health research that allows them to inform methodological advances. Methodological studies should also be conducted as pre-cursors to reporting guideline development, as they provide an opportunity to understand current practices, and help to identify the need for guidance and gaps in methodological or reporting quality. For example, the development of the popular Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guidelines were preceded by methodological studies identifying poor reporting practices [ 23 , 24 ]. In these instances, after the reporting guidelines are published, methodological studies can also be used to monitor uptake of the guidelines.

These studies can also be conducted to inform the state of the art for design, analysis and reporting practices across different types of health research fields, with the aim of improving research practices, and preventing or reducing research waste. For example, Samaan et al. conducted a scoping review of adherence to different reporting guidelines in health care literature [ 18 ]. Methodological studies can also be used to determine the factors associated with reporting practices. For example, Abbade et al. investigated journal characteristics associated with the use of the Participants, Intervention, Comparison, Outcome, Timeframe (PICOT) format in framing research questions in trials of venous ulcer disease [ 11 ].

How often are methodological studies conducted?

There is no clear answer to this question. Based on a search of PubMed, the use of related terms (“methodological review” and “meta-epidemiological study”) – and therefore, the number of methodological studies – is on the rise. However, many other terms are used to describe methodological studies. There are also many studies that explore design, conduct, analysis or reporting of research reports, but that do not use any specific terms to describe or label their study design in terms of “methodology”. This diversity in nomenclature makes a census of methodological studies elusive. Appropriate terminology and key words for methodological studies are needed to facilitate improved accessibility for end-users.

Why do we conduct methodological studies?

Methodological studies provide information on the design, conduct, analysis or reporting of primary and secondary research and can be used to appraise quality, quantity, completeness, accuracy and consistency of health research. These issues can be explored in specific fields, journals, databases, geographical regions and time periods. For example, Areia et al. explored the quality of reporting of endoscopic diagnostic studies in gastroenterology [ 25 ]; Knol et al. investigated the reporting of p -values in baseline tables in randomized trial published in high impact journals [ 26 ]; Chen et al. describe adherence to the Consolidated Standards of Reporting Trials (CONSORT) statement in Chinese Journals [ 27 ]; and Hopewell et al. describe the effect of editors’ implementation of CONSORT guidelines on reporting of abstracts over time [ 28 ]. Methodological studies provide useful information to researchers, clinicians, editors, publishers and users of health literature. As a result, these studies have been at the cornerstone of important methodological developments in the past two decades and have informed the development of many health research guidelines including the highly cited CONSORT statement [ 5 ].

Where can we find methodological studies?

Methodological studies can be found in most common biomedical bibliographic databases (e.g. Embase, MEDLINE, PubMed, Web of Science). However, the biggest caveat is that methodological studies are hard to identify in the literature due to the wide variety of names used and the lack of comprehensive databases dedicated to them. A handful can be found in the Cochrane Library as “Cochrane Methodology Reviews”, but these studies only cover methodological issues related to systematic reviews. Previous attempts to catalogue all empirical studies of methods used in reviews were abandoned 10 years ago [ 29 ]. In other databases, a variety of search terms may be applied with different levels of sensitivity and specificity.

Some frequently asked questions about methodological studies

In this section, we have outlined responses to questions that might help inform the conduct of methodological studies.

Q: How should I select research reports for my methodological study?

A: Selection of research reports for a methodological study depends on the research question and eligibility criteria. Once a clear research question is set and the nature of literature one desires to review is known, one can then begin the selection process. Selection may begin with a broad search, especially if the eligibility criteria are not apparent. For example, a methodological study of Cochrane Reviews of HIV would not require a complex search as all eligible studies can easily be retrieved from the Cochrane Library after checking a few boxes [ 30 ]. On the other hand, a methodological study of subgroup analyses in trials of gastrointestinal oncology would require a search to find such trials, and further screening to identify trials that conducted a subgroup analysis [ 31 ].

The strategies used for identifying participants in observational studies can apply here. One may use a systematic search to identify all eligible studies. If the number of eligible studies is unmanageable, a random sample of articles can be expected to provide comparable results if it is sufficiently large [ 32 ]. For example, Wilson et al. used a random sample of trials from the Cochrane Stroke Group’s Trial Register to investigate completeness of reporting [ 33 ]. It is possible that a simple random sample would lead to underrepresentation of units (i.e. research reports) that are smaller in number. This is relevant if the investigators wish to compare multiple groups but have too few units in one group. In this case a stratified sample would help to create equal groups. For example, in a methodological study comparing Cochrane and non-Cochrane reviews, Kahale et al. drew random samples from both groups [ 34 ]. Alternatively, systematic or purposeful sampling strategies can be used and we encourage researchers to justify their selected approaches based on the study objective.

Q: How many databases should I search?

A: The number of databases one should search would depend on the approach to sampling, which can include targeting the entire “population” of interest or a sample of that population. If you are interested in including the entire target population for your research question, or drawing a random or systematic sample from it, then a comprehensive and exhaustive search for relevant articles is required. In this case, we recommend using systematic approaches for searching electronic databases (i.e. at least 2 databases with a replicable and time stamped search strategy). The results of your search will constitute a sampling frame from which eligible studies can be drawn.

Alternatively, if your approach to sampling is purposeful, then we recommend targeting the database(s) or data sources (e.g. journals, registries) that include the information you need. For example, if you are conducting a methodological study of high impact journals in plastic surgery and they are all indexed in PubMed, you likely do not need to search any other databases. You may also have a comprehensive list of all journals of interest and can approach your search using the journal names in your database search (or by accessing the journal archives directly from the journal’s website). Even though one could also search journals’ web pages directly, using a database such as PubMed has multiple advantages, such as the use of filters, so the search can be narrowed down to a certain period, or study types of interest. Furthermore, individual journals’ web sites may have different search functionalities, which do not necessarily yield a consistent output.

Q: Should I publish a protocol for my methodological study?

A: A protocol is a description of intended research methods. Currently, only protocols for clinical trials require registration [ 35 ]. Protocols for systematic reviews are encouraged but no formal recommendation exists. The scientific community welcomes the publication of protocols because they help protect against selective outcome reporting, the use of post hoc methodologies to embellish results, and to help avoid duplication of efforts [ 36 ]. While the latter two risks exist in methodological research, the negative consequences may be substantially less than for clinical outcomes. In a sample of 31 methodological studies, 7 (22.6%) referenced a published protocol [ 9 ]. In the Cochrane Library, there are 15 protocols for methodological reviews (21 July 2020). This suggests that publishing protocols for methodological studies is not uncommon.

Authors can consider publishing their study protocol in a scholarly journal as a manuscript. Advantages of such publication include obtaining peer-review feedback about the planned study, and easy retrieval by searching databases such as PubMed. The disadvantages in trying to publish protocols includes delays associated with manuscript handling and peer review, as well as costs, as few journals publish study protocols, and those journals mostly charge article-processing fees [ 37 ]. Authors who would like to make their protocol publicly available without publishing it in scholarly journals, could deposit their study protocols in publicly available repositories, such as the Open Science Framework ( https://osf.io/ ).

Q: How to appraise the quality of a methodological study?

A: To date, there is no published tool for appraising the risk of bias in a methodological study, but in principle, a methodological study could be considered as a type of observational study. Therefore, during conduct or appraisal, care should be taken to avoid the biases common in observational studies [ 38 ]. These biases include selection bias, comparability of groups, and ascertainment of exposure or outcome. In other words, to generate a representative sample, a comprehensive reproducible search may be necessary to build a sampling frame. Additionally, random sampling may be necessary to ensure that all the included research reports have the same probability of being selected, and the screening and selection processes should be transparent and reproducible. To ensure that the groups compared are similar in all characteristics, matching, random sampling or stratified sampling can be used. Statistical adjustments for between-group differences can also be applied at the analysis stage. Finally, duplicate data extraction can reduce errors in assessment of exposures or outcomes.

Q: Should I justify a sample size?

A: In all instances where one is not using the target population (i.e. the group to which inferences from the research report are directed) [ 39 ], a sample size justification is good practice. The sample size justification may take the form of a description of what is expected to be achieved with the number of articles selected, or a formal sample size estimation that outlines the number of articles required to answer the research question with a certain precision and power. Sample size justifications in methodological studies are reasonable in the following instances:

Comparing two groups

Determining a proportion, mean or another quantifier

Determining factors associated with an outcome using regression-based analyses

For example, El Dib et al. computed a sample size requirement for a methodological study of diagnostic strategies in randomized trials, based on a confidence interval approach [ 40 ].

Q: What should I call my study?

A: Other terms which have been used to describe/label methodological studies include “ methodological review ”, “methodological survey” , “meta-epidemiological study” , “systematic review” , “systematic survey”, “meta-research”, “research-on-research” and many others. We recommend that the study nomenclature be clear, unambiguous, informative and allow for appropriate indexing. Methodological study nomenclature that should be avoided includes “ systematic review” – as this will likely be confused with a systematic review of a clinical question. “ Systematic survey” may also lead to confusion about whether the survey was systematic (i.e. using a preplanned methodology) or a survey using “ systematic” sampling (i.e. a sampling approach using specific intervals to determine who is selected) [ 32 ]. Any of the above meanings of the words “ systematic” may be true for methodological studies and could be potentially misleading. “ Meta-epidemiological study” is ideal for indexing, but not very informative as it describes an entire field. The term “ review ” may point towards an appraisal or “review” of the design, conduct, analysis or reporting (or methodological components) of the targeted research reports, yet it has also been used to describe narrative reviews [ 41 , 42 ]. The term “ survey ” is also in line with the approaches used in many methodological studies [ 9 ], and would be indicative of the sampling procedures of this study design. However, in the absence of guidelines on nomenclature, the term “ methodological study ” is broad enough to capture most of the scenarios of such studies.

Q: Should I account for clustering in my methodological study?

A: Data from methodological studies are often clustered. For example, articles coming from a specific source may have different reporting standards (e.g. the Cochrane Library). Articles within the same journal may be similar due to editorial practices and policies, reporting requirements and endorsement of guidelines. There is emerging evidence that these are real concerns that should be accounted for in analyses [ 43 ]. Some cluster variables are described in the section: “ What variables are relevant to methodological studies?”

A variety of modelling approaches can be used to account for correlated data, including the use of marginal, fixed or mixed effects regression models with appropriate computation of standard errors [ 44 ]. For example, Kosa et al. used generalized estimation equations to account for correlation of articles within journals [ 15 ]. Not accounting for clustering could lead to incorrect p -values, unduly narrow confidence intervals, and biased estimates [ 45 ].

Q: Should I extract data in duplicate?

A: Yes. Duplicate data extraction takes more time but results in less errors [ 19 ]. Data extraction errors in turn affect the effect estimate [ 46 ], and therefore should be mitigated. Duplicate data extraction should be considered in the absence of other approaches to minimize extraction errors. However, much like systematic reviews, this area will likely see rapid new advances with machine learning and natural language processing technologies to support researchers with screening and data extraction [ 47 , 48 ]. However, experience plays an important role in the quality of extracted data and inexperienced extractors should be paired with experienced extractors [ 46 , 49 ].

Q: Should I assess the risk of bias of research reports included in my methodological study?

A : Risk of bias is most useful in determining the certainty that can be placed in the effect measure from a study. In methodological studies, risk of bias may not serve the purpose of determining the trustworthiness of results, as effect measures are often not the primary goal of methodological studies. Determining risk of bias in methodological studies is likely a practice borrowed from systematic review methodology, but whose intrinsic value is not obvious in methodological studies. When it is part of the research question, investigators often focus on one aspect of risk of bias. For example, Speich investigated how blinding was reported in surgical trials [ 50 ], and Abraha et al., investigated the application of intention-to-treat analyses in systematic reviews and trials [ 51 ].

Q: What variables are relevant to methodological studies?

A: There is empirical evidence that certain variables may inform the findings in a methodological study. We outline some of these and provide a brief overview below:

Country: Countries and regions differ in their research cultures, and the resources available to conduct research. Therefore, it is reasonable to believe that there may be differences in methodological features across countries. Methodological studies have reported loco-regional differences in reporting quality [ 52 , 53 ]. This may also be related to challenges non-English speakers face in publishing papers in English.

Authors’ expertise: The inclusion of authors with expertise in research methodology, biostatistics, and scientific writing is likely to influence the end-product. Oltean et al. found that among randomized trials in orthopaedic surgery, the use of analyses that accounted for clustering was more likely when specialists (e.g. statistician, epidemiologist or clinical trials methodologist) were included on the study team [ 54 ]. Fleming et al. found that including methodologists in the review team was associated with appropriate use of reporting guidelines [ 55 ].

Source of funding and conflicts of interest: Some studies have found that funded studies report better [ 56 , 57 ], while others do not [ 53 , 58 ]. The presence of funding would indicate the availability of resources deployed to ensure optimal design, conduct, analysis and reporting. However, the source of funding may introduce conflicts of interest and warrant assessment. For example, Kaiser et al. investigated the effect of industry funding on obesity or nutrition randomized trials and found that reporting quality was similar [ 59 ]. Thomas et al. looked at reporting quality of long-term weight loss trials and found that industry funded studies were better [ 60 ]. Kan et al. examined the association between industry funding and “positive trials” (trials reporting a significant intervention effect) and found that industry funding was highly predictive of a positive trial [ 61 ]. This finding is similar to that of a recent Cochrane Methodology Review by Hansen et al. [ 62 ]

Journal characteristics: Certain journals’ characteristics may influence the study design, analysis or reporting. Characteristics such as journal endorsement of guidelines [ 63 , 64 ], and Journal Impact Factor (JIF) have been shown to be associated with reporting [ 63 , 65 , 66 , 67 ].

Study size (sample size/number of sites): Some studies have shown that reporting is better in larger studies [ 53 , 56 , 58 ].

Year of publication: It is reasonable to assume that design, conduct, analysis and reporting of research will change over time. Many studies have demonstrated improvements in reporting over time or after the publication of reporting guidelines [ 68 , 69 ].

Type of intervention: In a methodological study of reporting quality of weight loss intervention studies, Thabane et al. found that trials of pharmacologic interventions were reported better than trials of non-pharmacologic interventions [ 70 ].

Interactions between variables: Complex interactions between the previously listed variables are possible. High income countries with more resources may be more likely to conduct larger studies and incorporate a variety of experts. Authors in certain countries may prefer certain journals, and journal endorsement of guidelines and editorial policies may change over time.

Q: Should I focus only on high impact journals?

A: Investigators may choose to investigate only high impact journals because they are more likely to influence practice and policy, or because they assume that methodological standards would be higher. However, the JIF may severely limit the scope of articles included and may skew the sample towards articles with positive findings. The generalizability and applicability of findings from a handful of journals must be examined carefully, especially since the JIF varies over time. Even among journals that are all “high impact”, variations exist in methodological standards.

Q: Can I conduct a methodological study of qualitative research?

A: Yes. Even though a lot of methodological research has been conducted in the quantitative research field, methodological studies of qualitative studies are feasible. Certain databases that catalogue qualitative research including the Cumulative Index to Nursing & Allied Health Literature (CINAHL) have defined subject headings that are specific to methodological research (e.g. “research methodology”). Alternatively, one could also conduct a qualitative methodological review; that is, use qualitative approaches to synthesize methodological issues in qualitative studies.

Q: What reporting guidelines should I use for my methodological study?

A: There is no guideline that covers the entire scope of methodological studies. One adaptation of the PRISMA guidelines has been published, which works well for studies that aim to use the entire target population of research reports [ 71 ]. However, it is not widely used (40 citations in 2 years as of 09 December 2019), and methodological studies that are designed as cross-sectional or before-after studies require a more fit-for purpose guideline. A more encompassing reporting guideline for a broad range of methodological studies is currently under development [ 72 ]. However, in the absence of formal guidance, the requirements for scientific reporting should be respected, and authors of methodological studies should focus on transparency and reproducibility.

Q: What are the potential threats to validity and how can I avoid them?

A: Methodological studies may be compromised by a lack of internal or external validity. The main threats to internal validity in methodological studies are selection and confounding bias. Investigators must ensure that the methods used to select articles does not make them differ systematically from the set of articles to which they would like to make inferences. For example, attempting to make extrapolations to all journals after analyzing high-impact journals would be misleading.

Many factors (confounders) may distort the association between the exposure and outcome if the included research reports differ with respect to these factors [ 73 ]. For example, when examining the association between source of funding and completeness of reporting, it may be necessary to account for journals that endorse the guidelines. Confounding bias can be addressed by restriction, matching and statistical adjustment [ 73 ]. Restriction appears to be the method of choice for many investigators who choose to include only high impact journals or articles in a specific field. For example, Knol et al. examined the reporting of p -values in baseline tables of high impact journals [ 26 ]. Matching is also sometimes used. In the methodological study of non-randomized interventional studies of elective ventral hernia repair, Parker et al. matched prospective studies with retrospective studies and compared reporting standards [ 74 ]. Some other methodological studies use statistical adjustments. For example, Zhang et al. used regression techniques to determine the factors associated with missing participant data in trials [ 16 ].

With regard to external validity, researchers interested in conducting methodological studies must consider how generalizable or applicable their findings are. This should tie in closely with the research question and should be explicit. For example. Findings from methodological studies on trials published in high impact cardiology journals cannot be assumed to be applicable to trials in other fields. However, investigators must ensure that their sample truly represents the target sample either by a) conducting a comprehensive and exhaustive search, or b) using an appropriate and justified, randomly selected sample of research reports.

Even applicability to high impact journals may vary based on the investigators’ definition, and over time. For example, for high impact journals in the field of general medicine, Bouwmeester et al. included the Annals of Internal Medicine (AIM), BMJ, the Journal of the American Medical Association (JAMA), Lancet, the New England Journal of Medicine (NEJM), and PLoS Medicine ( n  = 6) [ 75 ]. In contrast, the high impact journals selected in the methodological study by Schiller et al. were BMJ, JAMA, Lancet, and NEJM ( n  = 4) [ 76 ]. Another methodological study by Kosa et al. included AIM, BMJ, JAMA, Lancet and NEJM ( n  = 5). In the methodological study by Thabut et al., journals with a JIF greater than 5 were considered to be high impact. Riado Minguez et al. used first quartile journals in the Journal Citation Reports (JCR) for a specific year to determine “high impact” [ 77 ]. Ultimately, the definition of high impact will be based on the number of journals the investigators are willing to include, the year of impact and the JIF cut-off [ 78 ]. We acknowledge that the term “generalizability” may apply differently for methodological studies, especially when in many instances it is possible to include the entire target population in the sample studied.

Finally, methodological studies are not exempt from information bias which may stem from discrepancies in the included research reports [ 79 ], errors in data extraction, or inappropriate interpretation of the information extracted. Likewise, publication bias may also be a concern in methodological studies, but such concepts have not yet been explored.

A proposed framework

In order to inform discussions about methodological studies, the development of guidance for what should be reported, we have outlined some key features of methodological studies that can be used to classify them. For each of the categories outlined below, we provide an example. In our experience, the choice of approach to completing a methodological study can be informed by asking the following four questions:

What is the aim?

Methodological studies that investigate bias

A methodological study may be focused on exploring sources of bias in primary or secondary studies (meta-bias), or how bias is analyzed. We have taken care to distinguish bias (i.e. systematic deviations from the truth irrespective of the source) from reporting quality or completeness (i.e. not adhering to a specific reporting guideline or norm). An example of where this distinction would be important is in the case of a randomized trial with no blinding. This study (depending on the nature of the intervention) would be at risk of performance bias. However, if the authors report that their study was not blinded, they would have reported adequately. In fact, some methodological studies attempt to capture both “quality of conduct” and “quality of reporting”, such as Richie et al., who reported on the risk of bias in randomized trials of pharmacy practice interventions [ 80 ]. Babic et al. investigated how risk of bias was used to inform sensitivity analyses in Cochrane reviews [ 81 ]. Further, biases related to choice of outcomes can also be explored. For example, Tan et al investigated differences in treatment effect size based on the outcome reported [ 82 ].

Methodological studies that investigate quality (or completeness) of reporting

Methodological studies may report quality of reporting against a reporting checklist (i.e. adherence to guidelines) or against expected norms. For example, Croituro et al. report on the quality of reporting in systematic reviews published in dermatology journals based on their adherence to the PRISMA statement [ 83 ], and Khan et al. described the quality of reporting of harms in randomized controlled trials published in high impact cardiovascular journals based on the CONSORT extension for harms [ 84 ]. Other methodological studies investigate reporting of certain features of interest that may not be part of formally published checklists or guidelines. For example, Mbuagbaw et al. described how often the implications for research are elaborated using the Evidence, Participants, Intervention, Comparison, Outcome, Timeframe (EPICOT) format [ 30 ].

Methodological studies that investigate the consistency of reporting

Sometimes investigators may be interested in how consistent reports of the same research are, as it is expected that there should be consistency between: conference abstracts and published manuscripts; manuscript abstracts and manuscript main text; and trial registration and published manuscript. For example, Rosmarakis et al. investigated consistency between conference abstracts and full text manuscripts [ 85 ].

Methodological studies that investigate factors associated with reporting

In addition to identifying issues with reporting in primary and secondary studies, authors of methodological studies may be interested in determining the factors that are associated with certain reporting practices. Many methodological studies incorporate this, albeit as a secondary outcome. For example, Farrokhyar et al. investigated the factors associated with reporting quality in randomized trials of coronary artery bypass grafting surgery [ 53 ].

Methodological studies that investigate methods

Methodological studies may also be used to describe methods or compare methods, and the factors associated with methods. Muller et al. described the methods used for systematic reviews and meta-analyses of observational studies [ 86 ].

Methodological studies that summarize other methodological studies

Some methodological studies synthesize results from other methodological studies. For example, Li et al. conducted a scoping review of methodological reviews that investigated consistency between full text and abstracts in primary biomedical research [ 87 ].

Methodological studies that investigate nomenclature and terminology

Some methodological studies may investigate the use of names and terms in health research. For example, Martinic et al. investigated the definitions of systematic reviews used in overviews of systematic reviews (OSRs), meta-epidemiological studies and epidemiology textbooks [ 88 ].

Other types of methodological studies

In addition to the previously mentioned experimental methodological studies, there may exist other types of methodological studies not captured here.

What is the design?

Methodological studies that are descriptive

Most methodological studies are purely descriptive and report their findings as counts (percent) and means (standard deviation) or medians (interquartile range). For example, Mbuagbaw et al. described the reporting of research recommendations in Cochrane HIV systematic reviews [ 30 ]. Gohari et al. described the quality of reporting of randomized trials in diabetes in Iran [ 12 ].

Methodological studies that are analytical

Some methodological studies are analytical wherein “analytical studies identify and quantify associations, test hypotheses, identify causes and determine whether an association exists between variables, such as between an exposure and a disease.” [ 89 ] In the case of methodological studies all these investigations are possible. For example, Kosa et al. investigated the association between agreement in primary outcome from trial registry to published manuscript and study covariates. They found that larger and more recent studies were more likely to have agreement [ 15 ]. Tricco et al. compared the conclusion statements from Cochrane and non-Cochrane systematic reviews with a meta-analysis of the primary outcome and found that non-Cochrane reviews were more likely to report positive findings. These results are a test of the null hypothesis that the proportions of Cochrane and non-Cochrane reviews that report positive results are equal [ 90 ].

What is the sampling strategy?

Methodological studies that include the target population

Methodological reviews with narrow research questions may be able to include the entire target population. For example, in the methodological study of Cochrane HIV systematic reviews, Mbuagbaw et al. included all of the available studies ( n  = 103) [ 30 ].

Methodological studies that include a sample of the target population

Many methodological studies use random samples of the target population [ 33 , 91 , 92 ]. Alternatively, purposeful sampling may be used, limiting the sample to a subset of research-related reports published within a certain time period, or in journals with a certain ranking or on a topic. Systematic sampling can also be used when random sampling may be challenging to implement.

What is the unit of analysis?

Methodological studies with a research report as the unit of analysis

Many methodological studies use a research report (e.g. full manuscript of study, abstract portion of the study) as the unit of analysis, and inferences can be made at the study-level. However, both published and unpublished research-related reports can be studied. These may include articles, conference abstracts, registry entries etc.

Methodological studies with a design, analysis or reporting item as the unit of analysis

Some methodological studies report on items which may occur more than once per article. For example, Paquette et al. report on subgroup analyses in Cochrane reviews of atrial fibrillation in which 17 systematic reviews planned 56 subgroup analyses [ 93 ].

This framework is outlined in Fig.  2 .

A proposed framework for methodological studies

Conclusions

Methodological studies have examined different aspects of reporting such as quality, completeness, consistency and adherence to reporting guidelines. As such, many of the methodological study examples cited in this tutorial are related to reporting. However, as an evolving field, the scope of research questions that can be addressed by methodological studies is expected to increase.

In this paper we have outlined the scope and purpose of methodological studies, along with examples of instances in which various approaches have been used. In the absence of formal guidance on the design, conduct, analysis and reporting of methodological studies, we have provided some advice to help make methodological studies consistent. This advice is grounded in good contemporary scientific practice. Generally, the research question should tie in with the sampling approach and planned analysis. We have also highlighted the variables that may inform findings from methodological studies. Lastly, we have provided suggestions for ways in which authors can categorize their methodological studies to inform their design and analysis.

Availability of data and materials

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Abbreviations

Consolidated Standards of Reporting Trials

Evidence, Participants, Intervention, Comparison, Outcome, Timeframe

Grading of Recommendations, Assessment, Development and Evaluations

Participants, Intervention, Comparison, Outcome, Timeframe

Preferred Reporting Items of Systematic reviews and Meta-Analyses

Studies Within a Review

Studies Within a Trial

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Lawrence Mbuagbaw, Daeria O. Lawson & Lehana Thabane

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Mbuagbaw, L., Lawson, D.O., Puljak, L. et al. A tutorial on methodological studies: the what, when, how and why. BMC Med Res Methodol 20 , 226 (2020). https://doi.org/10.1186/s12874-020-01107-7

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Article Contents

Role of methodologic guidelines, generalizability of guidelines, resistance to change within the scientific community.

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Methodologic Guidelines for Review Papers

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Douglas L. Weed, Methodologic Guidelines for Review Papers, JNCI: Journal of the National Cancer Institute , Volume 89, Issue 1, 1 January 1997, Pages 6–7, https://doi.org/10.1093/jnci/89.1.6

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Reading a good review paper is one of the most efficient ways of becoming familiar with state-of-the-art research and practice on any topic in cancer biology, epidemiology, prevention, or treatment. Yet, what constitutes a good review? It must be clearly organized, recently written by a knowledgeable (expert) scientist, and describe a topic appropriate to the general readership of the Journal of the National Cancer Institute . A 10-year methodologic discussion, however, suggests that there is more to the quality of reviews than judgments about writing style, author's expertise, and choice of topic ( 1 – 18 ). Review papers are sources of scientific information and should be read (and written) with specific methodologic considerations in mind. The purpose of this editorial is to propose a set of guidelines for reviews submitted to the Journal, a general oncology journal.

Methodologic guidelines for review papers (and the related issue of quality) have been discussed in several journals, including in alphabetical order:

American Journal of Preventive Medicine ( 8 )

Annals of Internal Medicine ( 3 )

Annals of the New York Academy of Science ( 12 )

British Medical Journal ( 9 , 15–17 )

Canadian Family Physician ( 11 )

Canadian Medical Association Journal ( 2 , 4 , 5 )

Journal of the American Medical Association ( 18 )

Journal of Clinical Epidemiology ( 6 , 7 )

Journal of Epidemiology and Community Health ( 10 , 14 )

Otolaryngology—Head and Neck Surgery ( 13 )

These discussions reflect a historical trend on the part of journal editors to improve the quality of reviews ( 8 ). This trend is traceable to Light and Pillemer's ( 1 ) classic book and to a study published in 1987 showing that 50 randomly selected review papers published in four prominent American medical journals ( Annals of Internal Medicine, Archives of Internal Medicine, Journal of the American Medical Association, and New England Journal of Medicine ) did not use scientific methods in the identification, assessment, and synthesis of information ( 3 ). Some journals now require a description of methods used in preparing a review or a structured abstract ( 8 , 11 , 13 ). In general, methodologic guidelines—whether required or suggested—provide an objective basis on which editors and referees can judge submissions of review papers to journals ( 8 ). Guidelines also help readers assess the extent to which the information in reviews is complete and unbiased and if the research and/or practice recommendations made by the author(s) of the review are reasonable ( 8 ).

The following guidelines are recommended for authors submitting reviews to this Journal and may also be useful for editors, referees, and readers in their assessment of the quality of submitted and published reviews.

Statement of Purpose

A review paper should include a clearly stated purpose in terms of questions to be answered or goals to be met. Noting that the purpose is to review a topic is insufficient. Reviews summarize evidence for several possible purposes including, but not limited to, making research recommendations, making causal conclusions, or making public health or medical practice recommendations. Not all purposes are appropriate for a given review, but all reviews should include a clear statement of purpose.

Search Methods and Inclusion/Exclusion Criteria

A review paper should describe the information sources searched. Computerized and manual databases, such as Medline, CANCERLIT, Index Medicus, and Current Contents, are typical examples. Other sources include reprint files and reference lists in books or published papers. In addition, a review paper should describe the inclusion criteria used in selecting the papers cited. Inclusions (and therefore exclusions) can be made on the basis of time period (e.g., papers published after 1990), type of publication (e.g., peer-reviewed, published, in press, abstracts, and proceedings), and by language (e.g., English). Inclusions may also be made on the basis of study design (e.g., observational/epidemiologic studies), topic (e.g., specific exposure-cancer association or class of chemotherapeutic agent), and by population studied (e.g., Hispanics, women, or animal models). Once these inclusion criteria are described, a review article may specify the number of studies identified by the search methods and the proportion selected for review. The reader of any review should have a clear idea of the search techniques used, what evidence was assessed, and what evidence was excluded.

Criteria for Evaluating Validity (or Quality) of Studies

A review paper should describe the criteria used to evaluate the quality of the evidence. There are many examples of such criteria: some applicable to specific design types, such as case-control studies, some applicable to a broader set of study designs (such as the hierarchy of evidence ( 19 ) used by the U.S. Preventive Services Task Force or Physician's Data Query of the National Cancer Institute), and some applicable to types of biologic evidence (such as those used by the International Agency for Research on Cancer). Authors of reviews may wish to state their own criteria, including, but not limited to, sample size, laboratory and/or statistical methods, measurement error, confounding and other forms of bias, and statistical significance or confidence limits.

Methods for Summarizing Evidence

A review paper should describe the methods used for summarizing the evidence from the studies selected for review. These may range from simple narrative techniques to highly structured quantitative techniques, such as meta-analysis.

Criteria for Conclusions and Recommendations

A review paper should describe the methods used to make conclusions. For example, if causal (or preventive) conclusions are a stated purpose of the review of epidemiologic evidence, then inferential methods, such as those published by the Surgeon General's Office or by Austin Bradford Hill or others ( 20 ), should be stated. If public health or medical practice recommendations are a stated purpose of the review, then the methods used to make those recommendations should be clearly stated. In addition, there should be a discussion of the extent to which economic, ethical, and pragmatic considerations were used in arriving at the recommendations.

The primary purpose of these guidelines is to ensure that readers (including editors and referees) are informed about the methods used in preparing the review. Readers can then better assess the quality of the paper and not judge it solely on the basis of writing style, author's expertise, appropriateness of the topic, or other implicit criteria. Recommending that authors disclose their methods, however, is not the same as judging the appropriateness of the methods used. A wide range of methods is used in reviews, some more quantitative while others are more qualitative. Indeed, disclosing which methods are used may stimulate readers and authors to examine methodologic research issues, such as the predictability, reliability, and validity of search methods or methods of summarizing evidence. Ultimately, methodologic research on such issues could lead to an improvement in the overall quality of review papers.

The Journal of the National Cancer Institute publishes reviews from many areas within the broad topic of oncology, including reviews on biology, epidemiology, prevention, and treatment. Nevertheless, the methodologic guidelines described above are applicable to any review submitted to the Journal. The only methodologic requirements are for authors to state the purpose of the review beyond “reviewing the evidence” and to state what methods were used in preparing the review. There is no requirement to use a particular method. Thus, these guide lines are generalizable to any review as long as the author of that review can state the purpose of the paper and can describe the search techniques, the studies included and excluded, the review author's approach to assessing the validity of studies, and the methods used to summarize evidence and make recommendations.

Although these guidelines are generalizable in principle to any review in the field of general oncology, some resistance to this proposal may be encountered. Change is often difficult for members of the scientific community, especially in situations in which there appears to be a challenge to the expertise of scientists and clinicians who, by virtue of that expertise, write reviews. However, no such challenge is intended. Rather, the only challenge found in this paper is for scientists to disclose the methods that they used to prepare reviews. Such disclosure (and the methodologic research it encourages) will eventually result in an improvement in the quality of reviews and thus an improvement in the quality of the Journal.

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Methodology for research I

Rakesh garg.

Department of Onco-anaesthesiology and Palliative Medicine, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India

The conduct of research requires a systematic approach involving diligent planning and its execution as planned. It comprises various essential predefined components such as aims, population, conduct/technique, outcome and statistical considerations. These need to be objective, reliable and in a repeatable format. Hence, the understanding of the basic aspects of methodology is essential for any researcher. This is a narrative review and focuses on various aspects of the methodology for conduct of a clinical research. The relevant keywords were used for literature search from various databases and from bibliographies of the articles.

INTRODUCTION

Research is a process for acquiring new knowledge in systematic approach involving diligent planning and interventions for discovery or interpretation of the new-gained information.[ 1 , 2 ] The outcome reliability and validity of a study would depend on well-designed study with objective, reliable, repeatable methodology with appropriate conduct, data collection and its analysis with logical interpretation. Inappropriate or faulty methodology would make study unacceptable and may even provide clinicians faulty information. Hence, the understanding the basic aspects of methodology is essential.

This is a narrative review based on existing literature search. This review focuses on specific aspects of the methodology for conduct of a research/clinical trial. The relevant keywords for literature search included ‘research’, ‘study design’, ‘study controls’, ‘study population’, ‘inclusion/exclusion criteria’, ‘variables’, ‘sampling’, ‘randomisation’, ‘blinding’, ‘masking’, ‘allocation concealment’, ‘sample size’, ‘bias’, ‘confounders’ alone and in combinations. The search engine included PubMed/MEDLINE, Google Scholar and Cochrane. The bibliographies of the searched articles were specifically searched for missing manuscripts from the search engines and manually from the print journals in the library.

The following text highlights/describes the basic essentials of methodology which needs to be adopted for conducting a good research.

Aims and objectives of study

The aims and objectives of research need to be known thoroughly and should be specified before start of the study based on thorough literature search and inputs from professional experience. Aims and objectives state whether nature of the problem (formulated as research question or research problem) has to be investigated or its solution has to be found by different more appropriate method. The lacunae in existing knowledge would help formulate a research question. These statements have to be objective specific with all required details such as population, intervention, control, outcome variables along with time interventions.[ 3 , 4 , 5 ] This would help formulate a hypothesis which is a scientifically derived statement about a particular problem in the defined population. The hypothesis generation depends on the type of study as well. Researcher observation related to any aspect initiates hypothesis generation. A cross-sectional survey would generate hypothesis. An observational study establishes associations and supports/rejects the hypothesis. An experiment would finally test the hypothesis.[ 5 , 6 , 7 ]

STUDY POPULATION AND PATIENT SELECTION, STUDY AREA, STUDY PERIOD

The flow of study in an experimental design has various sequential steps [ Figure 1 ].[ 1 , 2 , 6 ] Population refers to an aggregate of individuals, things, cases, etc., i.e., observation units that are of interest and remain the focus of investigation. This reference population or target population is the group on which the study outcome would be extrapolated.[ 6 ] Once this target population is identified, researcher needs to assess whether it is possible to study all the individuals for an outcome. Usually, all cannot be included, so a study population is sampled. The important attribute of a sample is that every individual should have equal and non-zero chance of getting included in the study. The sample should be made independently, i.e., selection of one does not influence inclusion or exclusion of other. In clinical practice, the sampling is restricted to a particular place (patients attending to clinics or posted for surgery) or includes multiple centres rather than sampling the universe. Hence, the researcher should be cautious in generalising the outcomes. For example, in a tertiary care hospital, patients are referred and may have more risk factors as compared to primary centres where a patient with lesser severity are managed. Hence, researchers must disclose details of the study area. The study period needs to be disclosed as it would make readers understand the population characteristics. Furthermore, study period would tell about relevance of the study with respect to the present period.

Flow of an experimental study

The size of sample has to be pre-determined, analytically approached and sufficiently large to represent the population.[ 7 , 8 , 9 ] Including a larger sample would lead to wastage of resources, risk that the true treatment effect may be missed due to heterogeneity of large population and would be time-consuming.[ 6 ] If a study is too small, it will not provide the suitable answer to research question. The main determinant of the sample size includes clinical hypothesis, primary endpoint, study design, probability of Type I and II error, power, minimum treatment difference of clinical importance.[ 7 ] Attrition of patients should be attended during the sample size calculation.[ 6 , 9 ]

SELECTION OF STUDY DESIGN

The appropriate study design is essential for the intervention outcome in terms of its best possible and most reliable estimate. The study design selection is based on parameters such as objectives, therapeutic area, treatment comparison, outcome and phase of the trial.[ 6 ] The study design may be broadly classified as:[ 5 , 6 , 7 ]

• Descriptive: Case report, case series, survey
• Analytical: Case-control, cohort, cross-sectional
• Experimental: Randomised controlled trial (RCT), quasi-experiment
• Qualitative.

For studying causality, analytical observational studies would be prudent to avoid posing risk to subjects. For clinical drugs or techniques, experimental study would be more appropriate.[ 6 ] The treatments remain concurrent, i.e. the active and control interventions happen at the same period in RCT. It may parallel group design wherein treatment and control groups are allocated to different individuals. This requires comparing a placebo group or a gold standard intervention (control) with newer agent or technique.[ 6 ] In matched-design RCT, randomisation is between matched pairs. For cross-over study design, two or more treatments are administered sequentially to the same subject and thus each subject acts as its own control. However, researches should be aware of ‘carryover effect’ of the previous intervention and suitable wash period needs to be ensured. In cohort study design, subjects with disease/symptom or free of study variable are followed for a particular period. The cross-sectional study examines the prevalence of the disease, surveys, validating instruments, tools and questionnaires. The qualitative research is a study design wherein health-related issue in the population is explored with regard to its description, exploration and explanation.[ 6 ]

Selection of controls

The control is required because disease may be self-remitting, Hawthorne effect (change in response or behaviours of subjects when included in study), placebo effect (patients feel improvement even with placebo), effect of confounder, co-intervention and regression to the mean phenomenon (for example, white coat hypertension, i.e. patients at recruitment may have higher study parameter but subsequently may get normal).[ 2 , 6 , 7 ] The control could be a placebo, no treatment, different dose or regimen or intervention or the standard/gold treatment. Avoiding a routine care for placebo is not desirable and unethical. For instance, for studying analgesic regimen, it would be unethical not to administer analgesics in a control group. It is advisable to continue standard of care, i.e. providing routine analgesics even in control group. The use of placebo or no treatment may be considered where no current proven intervention exists or placebo is required to evaluate efficacy or safety of an intervention without serious or irreversible harm.

The comparisons to be made in the study among groups also need to be specified.[ 6 , 7 , 9 ] These comparisons may prove superiority, non-inferiority or equivalence among groups. The superiority trials demonstrate superiority either to a placebo in a placebo-controlled trial or to an active control treatment. The non-inferiority trials would prove that the efficacy of an intervention is no worse than that of the active comparative treatment. The equivalence trials demonstrate that the outcome of two or more interventions differs by a clinically unimportant margin and either technique or drug may be clinically acceptable.

STUDY TOOLS

The study tools such as measurements scales, questionnaires and scoring systems need to be specified with an objective definition. These tools should be validated before its use and appropriate use by the research staff is mandatory to avoid any bias. These tools should be simple and easily understandable to everyone involved in the study.

Inclusion/exclusion criteria

In clinical research, specific group of relatively homogeneous patient population needs to be selected.[ 6 ] Inclusion and exclusion criteria define who can be included or excluded from the study sample. The inclusion criteria identify the study population in a consistent, reliable, uniform and objective manner. The exclusion criteria include factors or characteristics that make the recruited population ineligible for the study. These factors may be confounders for the outcome parameter. For example, patients with liver disease would be excluded if coagulation parameters would impact the outcome. The exclusion criteria are inclusive of inclusion criteria.

VARIABLES: PRIMARY AND SECONDARY

Variables are definite characteristics/parameters that are being studied. Clear, precise and objective definition for measurement of these characteristics needs to be defined.[ 2 ] These should be measurable and interpretable, sensitive to the objective of the study and clinically relevant. The most common end-point is related to efficacy, safety and quality of life. The study variables could be primary or secondary.[ 6 ] The primary end-point, usually one, provides the most relevant, reliable and convincing evidence related to the aim and objective. It is the characteristic on the basis of which research question/hypothesis has been formulated. It reflects clinically relevant and important treatment benefits. It determines the sample size. Secondary end-points are the other objectives indirectly related to primary objective with regard to its close association or they may be some associated effects/adverse effects related to intervention. The measurement timing of the variables must be defined a priori . These are usually done at screening, baseline and completion of trial.

The study end-point parameter may be clinical or surrogate in nature. A clinical end-point is related directly to clinical implications with regard to beneficial outcome of the intervention. The surrogate end-point is indirectly related to patient clinical benefit and is usually measures laboratory measurement or physical sign as a substitute for a clinically meaningful end-point. Surrogate end-points are more convenient, easily measurable, repeatable and faster.

SAMPLING TECHNIQUES: RANDOMISATION, BLINDING/MASKING AND ALLOCATION CONCEALMENT

Randomisation.

Randomisation or random allocation is a method to allocate individuals into one of the groups (arms) of a study.[ 1 , 2 ] It is the basic assumption required for statistical analysis of data. The randomisation would maximise statistical power, especially in subgroup analyses, minimise selection bias and minimise allocation bias (or confounding). This leads to distribution of all the characteristics, measured or non-measured, visible or invisible and known or unknown equally into the groups. Randomisation uses various strategies as per the study design and outcome.

Probability sampling/randomisation

• Simple/unrestricted: Each individual of the population has the same chance of being included in the sample. This is used when population is small, homogenous and the sampling frame is available. For example, lottery method, table of random numbers or computer-generated
• Stratified: It is used in non-homogenous population. Population is divided into homogenous groups (strata), and the sample is drawn for each stratum at random. It keeps the ‘characteristics’ of the participants (for example, age, weight or physical status) as similar as possible across the study groups. The allocation to strata can be by equal or proportional allocation
• Systematic: This is used when complete and up-to-date sampling frame is available. The first unit is selected at random and the rest get selected automatically according to some pre-designed pattern
• Cluster: This applies for large geographical area. Population is divided into a finite numbers of distinct and identifiable units (sampling units/element). A group of such elements is a cluster and sampling of these clusters is done. All units of the selected clusters are included in the study
• Multistage: This applies for large nationwide surveys. Sampling is done in stages using random sampling. Here, sub-sampling within the selected clusters is done. If procedure is repeated in more number of stages, then they termed as multistage sampling
• Multiphase: Here, some data are collected from whole of the units of a sample, and other data are collected from a sub-sample of the units constituting the original sample (two-phase sampling). If three or more phases are used, then they termed as multiphase sampling.

Non-probability sampling/randomisation

This technique does not give equal and non-zero chances to all the individuals in the population to be selected in the sample.

• Convenience: Sampling is done as per the convenience of the investigator, i.e., easily available
• Purposive/judgemental/selective/subjective: The sample is selected as per judgement of investigator
• Quota: It is done as per judgement of the interviewer based on some specified characteristics such as sex and physical status.

ALLOCATION CONCEALMENT

Allocation concealment refers to the process ensuring the person who generates the random assignment remains blind to what arm the person will be allotted.[ 8 , 9 , 10 ] It is a strategy to avoid ascertainment or selection bias. For example, based on an outcome, researcher may recruit a specific category as lesser sicker patients to a particular group and vice versa to the other group. This selective recruitment would underestimate (if treatment group is sicker) or overestimate (if control group is sicker) the intervention effect.[ 9 ] The allocation should be concealed from investigator till the initiation of intervention. Hence, randomisation should be performed by an independent person who is not involved in the conduct of the study or its monitoring. The randomisation list is kept secret. The methods of allocation concealment include:[ 9 , 10 ]

• Central randomisation: Some centrally independent authority performs randomisation and informs the investigators via telephone, E-mail or fax
• Pharmacy controlled: Here, pharmacy provides coded drugs for use
• Sequentially numbered containers: Identical containers equal in weight, similar in appearance and tamper-proof are used
• Sequentially numbered, opaque, sealed envelopes: The randomised numbers are concealed in opaque envelope to be opened just before intervention and are the most common and easy to perform method.

BLINDING/MASKING

Blinding ensures the group to which the study subjects are assigned not known or easily ascertained by those who are ‘masked’, i.e., participants, investigators, evaluators or statistician to limit occurrence of bias.[ 1 , 2 ] It confirms that the intervention and standard or placebo treatment appears the same. Blinding is different from allocation concealment. Allocation concealment is done before, whereas blinding is done at and after initiation of treatment. In situations such as study drugs with different formulations or medical versus surgical interventions, blinding may not be feasible.[ 8 ] Sham blocks or needling in subjects may not be ethical. In such situation, the outcome measurement should be made objective to the fullest to avoid bias and whosoever may be masked should be blinded. The research manuscript must mention the details about blinding including who was blinded after assignment to interventions and process or technique used. Blinding could be:[ 8 , 9 ]

• Unblinded: The process cannot conceal randomisation
• Single blind: One of the participants, investigators or evaluators remains masked
• Double-blind: The investigator and participants remained masked
• Triple blind: Not only investigator but also participant maintains a blind data analysis.

BIAS AND CONFOUNDERS

Bias is a systematic deviation of the real, true effect (better or worst outcome) resulting from faulty study design.[ 1 , 2 ] The various steps of study such as randomisation, concealment, blinding, objective measurement and strict protocol adherence would reduce bias.

The various possible and potential biases in a trial can be:[ 7 ]

• Investigator bias: An investigator either consciously or subconsciously favours one group than other
• Evaluator bias: The investigator taking end-point variable measurement intentionally or unintentionally favours one group over other. It is more common with subjective or quality of life end-points
• Performance bias: It occurs when participant knows of exposure to intervention or its response, be it inactive or active
• Selection bias: This occurs due to sampling method such as admission bias (selective factors for admission), non-response bias (refusals to participate and the population who refused may be different from who participated) or sample is not representative of the population
• Ascertainment or information bias: It occurs due to measurement error or misclassification of patient. For example, diagnostic bias (more diagnostic procedures performed in cases as compared with controls), recall bias (error of categorisation, investigator aggressively search for exposure variables in cases)
• Allocation bias: Allocation bias occurs when the measured treatment effect differs from the true treatment effect
• Detection bias: It occurs when observations in one group are not as vigilantly sought as in the other
• Attrition bias/loss-to-follow-up bias: It occurs when patient is lost to follow-up preferentially in a particular group.

Confounding occurs when outcome parameters are affected by effects of other factors not directly relevant to the research question.[ 1 , 7 ] For example, if impact of drug on haemodynamics is studied on hypertensive patients, then diabetes mellitus would be confounder as it also effects the hemodynamic response to autonomic disturbances. Hence, it becomes prudent during the designing stage for a study that all potential confounders should be carefully considered. If the confounders are known, then they can be adjusted statistically but with loss of precision (statistical power). Hence, confounding can be controlled either by preventing it or by adjusting for it in the statistical analysis. The confounding can be controlled by restriction by study design (for example, restricted age range as 2-6 years), matching (use of constraints in the selection of the comparison group so that the study and comparison group have similar distribution with regard to potential confounder), stratification in the analysis without matching (involves restriction of the analysis to narrow ranges of the extraneous variable) and mathematical modelling in the analysis (use of advanced statistical methods of analysis such as multiple linear regression and logistic regression). Strategies during data analysis include stratified analysis using the Mantel-Haenszel method to adjust for confounders, using a matched design approach, data restriction and model fitting using regression techniques.

Basic understanding of the methodology is essential to have reliable, repeatable and clinically acceptable outcome. The study plan including all its components needs to be designed before start of the study, and the study protocol should be strictly adhered during the conduct of study.

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• Inclusion and Exclusion Criteria | Examples & Definition

Inclusion and Exclusion Criteria | Examples & Definition

Published on September 17, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Inclusion and exclusion criteria determine which members of the target population can or can’t participate in a research study. Collectively, they’re known as eligibility criteria , and establishing them is critical when seeking study participants for clinical trials.

This allows researchers to study the needs of a relatively homogeneous group (e.g., people with liver disease) with precision. Examples of common inclusion and exclusion criteria are:

• Demographic characteristics: Age, gender identity, ethnicity
• Study-specific variables: Type and stage of disease, previous treatment history, presence of chronic conditions, ability to attend follow-up study appointments, technological requirements (e.g., internet access)
• Control variables : Fitness level, tobacco use, medications used

Failure to properly define inclusion and exclusion criteria can undermine your confidence that causal relationships exist between treatment and control groups, affecting the internal validity of your study and the generalizability ( external validity ) of your findings.

Table of contents

What are inclusion criteria, what are exclusion criteria, examples of inclusion and exclusion criteria, why are inclusion and exclusion criteria important, other interesting articles, frequently asked questions.

Inclusion criteria comprise the characteristics or attributes that prospective research participants must have in order to be included in the study. Common inclusion criteria can be demographic, clinical, or geographic in nature.

• 18 to 80 years of age
• Diagnosis of chronic heart failure at least 6 months before trial
• On stable doses of heart failure therapies
• Willing to return for required follow-up (posttest) visits

People who meet the inclusion criteria are then eligible to participate in the study.

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Exclusion criteria comprise characteristics used to identify potential research participants who should not be included in a study. These can also include those that lead to participants withdrawing from a research study after being initially included.

In other words, individuals who meet the inclusion criteria may also possess additional characteristics that can interfere with the outcome of the study. For this reason, they must be excluded.

Typical exclusion criteria can be:

• Ethical considerations , such as being a minor or being unable to give informed consent
• Practical considerations, such as not being able to read

If potential participants possess any additional characteristics that can affect the results, such as another medical condition or a pregnancy, these are also often grounds for exclusion.

• The patient requires valve or other cardiac surgery
• The patient is unable to carry out any physical activity without discomfort
• The patient had a stroke within three months prior to enrollment
• The patient refuses to give informed consent
• The patient is a candidate for coronary bypass surgery or something similar

People who meet one or more of the exclusion criteria must be disqualified. This means that they can’t participate in the study even if they meet the inclusion criteria.

It is important that researchers clearly define the appropriate inclusion and exclusion criteria prior to recruiting participants for their experiment or trial.

Here are some examples of effective and ineffective ways to phrase your criteria:

Inclusion criteria

Bad example: “Subjects will be included in the study if they have insomnia.”

This is too vague. How are you going to establish that participants have insomnia?

Good example: “Subjects will be included in the study if they have been diagnosed with insomnia by a physician and have had symptoms (i.e., trouble falling and/or staying asleep) for at least 3 nights a week for a minimum of 3 months.”

Here, the diagnosis and symptoms are clear. Specifying the time frame ensures that the condition (insomnia) is more likely to be stable throughout the study.

Exclusion criteria

Bad example: “Subjects will be excluded from the study if they are taking medications.”

This is too broad. There are many forms of medication, and some surely will not interfere with your study results. Excluding anyone who is using any type of medication—be it painkillers, birth control, or antidepressants—makes recruitment of study participants for your sample difficult. This, in turn, affects the feasibility of your study.

Good example: “Subjects will be excluded from the study if they are currently on any medication affecting sleep, prescription drugs, or other drugs that in the opinion of the research team may interfere with the results of the study.”

Researchers review inclusion and exclusion criteria with each potential participant to determine their eligibility.

Defining inclusion and exclusion criteria is important in any type of research that examines characteristics of a specific subset of a population . This helps researchers identify the study population in a consistent, reliable, and objective manner. As a result, study participants are more likely to have the attributes that will make it possible to robustly answer the research question .

In clinical trials, establishing inclusion and exclusion criteria minimizes the likelihood of harming participants (e.g., excluding pregnant women) and safeguards vulnerable individuals from exploitation (e.g., excluding individuals who are unable to comprehend what the research entails.) Ethical considerations like these are critical in human-based research.

The main goal of clinical trials is to prove that a medication is safe and effective when used by the target population it was designed for. Therefore, ensuring that study participants are representative of the target population is crucial to the success of the study.

By applying inclusion and exclusion criteria to recruit participants, researchers can ensure that participants are indeed representative of the target population, ensuring external validity . Relatedly, defining robust inclusion and exclusion criteria strengthens your claim that causal relationships exist between your treatment and control groups , ensuring internal validity .

Strong inclusion and exclusion criteria also help other researchers, because they can follow what you did and how you selected participants, allowing them to accurately replicate or reproduce your study.

Ethnographies and a few other types of qualitative research do not usually specify exclusion criteria. However, inclusion criteria help researchers define the community of interest—for example, users of Apple watches. In this way, they can find individuals who have attributes that can help them meet the research objectives .

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Inclusion and exclusion criteria are predominantly used in non-probability sampling . In purposive sampling and snowball sampling , restrictions apply as to who can be included in the sample .

Inclusion and exclusion criteria are typically presented and discussed in the methodology section of your thesis or dissertation .

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Implementation Science

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Ownership Structure and Firm Performance: A Comprehensive Review and Empirical Analysis

• Published: 04 April 2024

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• Sanjana Bhakar   ORCID: orcid.org/0000-0002-0936-9651 1 ,
• Priti Sharma 1 &
• Sanjiv Kumar 1  

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Ownership structure and firm performance are the two important ingredients for a firm to sustain in the market for a prolonged time. Ample research has statistically proven the significant impact of ownership structure on firm performance. Ergo, this study aims to critically review and analyse the mechanisms of ownership structure (OS) and their impact on the firm performance (FP) with the help of content analysis and systematic literature review (SLR). This study used the combined literature from Scopus and Web of Science databases from 1977 to 2022. A total of 552 relevant documents have been extracted, out of which 40 documents were found suitable based on inclusion and exclusion criteria using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework of SLR has been applied using R studio software. Major findings of the study revealed that ownership concentration was found significant in affecting firm performance. However, other mechanisms such as managerial ownership, government ownership, institutional ownership, foreign ownership and family ownership showed mixed results such as positive, negative or insignificant. This study on the one side contributes to the existing literature and also helps the policymakers in maximising the performance of the firm by suggesting ways to reduce conflicts of interest between managers and shareholders and will also be significant to the practitioners, scholars and managers in comprehending dynamics of corporate governance practices. Further, it will help investors give special consideration to a particular type of ownership structure while making investment decisions.

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Abbreviations

Corporate governance

• Ownership structure
• Firm performance
• Ownership concentration

Concentrated ownership

Managerial ownership

Foreign ownership

Family ownership

Government ownership

State-owned

Institutional ownership

Dependent variable

Independent variable

Control variables

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FT ranking: The Americas’ Fastest-Growing Companies 2024

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The FT is pleased to announce its fifth annual ranking of the fastest-growing companies in the Americas, covering a period that took in the pandemic and the end of rock-bottom interest rates.

Based on disclosed revenue growth between 2019 and 2022, the median revenue of the ranked companies, at $20.5mn, was slightly lower than it had been last year, but was still well ahead of the $12.2mn median from the previous year’s ranking, which included the pre-pandemic period. 

This year’s top-ranked companies were the telephone marketing business Marketcall, cannabinoid product manufacturer MC Global, nutritional supplements seller Thesis, ecommerce services provider myFBAprep, and healthcare educational-materials supplier Archer Review (analysis continues below the table) .

(continued) The sector with the most entries in the ranking was IT and software, with 102 companies, or one-fifth of the total. Other entries were spread across a broad range of industries. Second-most-common were advertising and marketing businesses, followed by fintech, financial services and insurance groups. The least represented sector was aerospace and defence, with just two companies.

The vast majority of the companies included were, as before, from the US — 387 of the 500. However, for the first time, there were more entries from Latin America than from Canada.

Within the US, the cities of New York and San Francisco and their surrounding areas were home to the most entries, with roughly 40 each, but the others were spread across the country. Outside those two centres, other notable clusters of fast-growing companies were Dallas-Fort Worth in Texas with 14, and Washington DC and surrounding areas with 20.

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The top companies by revenue included some of the US’s most recognisable names — including Amazon, Google’s owner Alphabet, Pfizer, and Tesla but also several lesser known businesses, such as IT company TD Synnex, oilfield services company EnterpriseProducts, and food logistics company Performance Food Group.

Our list was compiled with Statista , a research company, and ranks businesses across the Americas by their compound annual growth rate (CAGR) in revenue between 2019 and 2022. The ranking is not necessarily a reflection of the size of countries’ economies but, rather, their ability to innovate and the willingness of their high-growth companies to be candid with financial information.

Readers can use the arrow buttons at the top of the columns in the table (above) to sort by country, sector or revenue, in order to analyse the data in more detail. 

Because many fast-growing companies tend to be privately held and do not publicly disclose detailed financial data, a ranking such as this can never claim to be complete. But the rigorous screening process (please refer to the methodology below) — which also requires senior executives to sign off on the figures submitted by their companies — means the ranking can offer readers a meaningful insight into the health of these private companies.

Methodology

The FT Americas’ Fastest-Growing Companies 2024 is a list of the 500 companies in the Americas that have the highest growth in publicly disclosed revenues between 2019 and 2022.

The ranking was created through a complex procedure. Although the search was extensive, the ranking does not claim to be complete, as some companies did not want to make their figures public or did not participate for other reasons.

The project was advertised online and in print, allowing all eligible companies to register via websites created by Statista and the Financial Times. In addition, through research in company databases and other public sources, Statista identified tens of thousands of companies in the Americas as potential candidates for the FT ranking. These companies were invited to participate in the competition by post, email and telephone.

The application phase ran from August to December 2023. The submitted revenue figures had to be certified by the chief financial officer, chief executive, or a member of the executive committee, of the company. Companies with three or fewer employees, or companies that are not a legal entity, were subject to additional checks to verify their revenue numbers.

CRITERIA FOR INCLUSION IN THE LIST

To be included in the list of the Americas’ fastest-growing companies, a company had to meet the following criteria:

• Revenue of at least $100,000 generated in 2019 (or currency value equivalent according to the average of the actual fiscal year);

• Revenue of at least $1.5mn generated in 2022 (or currency value equivalent according to the average of the actual fiscal year);

• An independent entity (not a subsidiary or branch office of any kind);

• Revenue growth between 2019 and 2022 that was primarily organic (ie “internally” stimulated);

• Headquartered in one of 20 American countries. Companies from these countries were eligible to participate: Argentina, Belize, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, the US, Uruguay, Venezuela.

CALCULATION OF GROWTH RATES

The calculation of company growth rates is based on the revenue figures submitted by the companies in the respective national currency. For better comparability in the ranking, the revenue figures were converted into US dollars. The average exchange rate for the financial year indicated by the company was used for this purpose.

The compound annual growth rate (CAGR) was calculated as follows:

((revenue2022 / revenue2019)^(1/3)) - 1 = CAGR

The absolute growth between 2019 and 2022 was calculated as follows:

(revenue2022 / revenue2019) - 1 = Growth rate

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All information reported by the companies was processed and checked by Statista. Missing data entries (employee numbers, address data, etc) were researched in detail. Companies that did not fulfil the criteria for inclusion in the ranking were deleted.

In addition to the companies that responded to invitations to participate, Statista included some well-known companies noted for their remarkable growth. Financial information was collected via desk research using official sources, such as publicly available earnings presentations, investor relations websites, and annual reports.

The minimum CAGR required to be included in the ranking this year was 9 per cent.

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• Published: 08 April 2024

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

• Felicity Liew 1   na1 ,
• Claudia Efstathiou   ORCID: orcid.org/0000-0001-6125-8126 1   na1 ,
• Sara Fontanella 1 ,
• Matthew Richardson 2 ,
• Ruth Saunders 2 ,
• Dawid Swieboda 1 ,
• Jasmin K. Sidhu 1 ,
• Stephanie Ascough 1 ,
• Shona C. Moore   ORCID: orcid.org/0000-0001-8610-2806 3 ,
• Noura Mohamed 4 ,
• Jose Nunag   ORCID: orcid.org/0000-0002-4218-0500 5 ,
• Clara King 5 ,
• Olivia C. Leavy 2 , 6 ,
• Omer Elneima 2 ,
• Hamish J. C. McAuley 2 ,
• Aarti Shikotra 7 ,
• Amisha Singapuri   ORCID: orcid.org/0009-0002-4711-7516 2 ,
• Marco Sereno   ORCID: orcid.org/0000-0003-4573-9303 2 ,
• Victoria C. Harris 2 ,
• Linzy Houchen-Wolloff   ORCID: orcid.org/0000-0003-4940-8835 8 ,
• Neil J. Greening   ORCID: orcid.org/0000-0003-0453-7529 2 ,
• Nazir I. Lone   ORCID: orcid.org/0000-0003-2707-2779 9 ,
• Matthew Thorpe 10 ,
• A. A. Roger Thompson   ORCID: orcid.org/0000-0002-0717-4551 11 ,
• Sarah L. Rowland-Jones 11 ,
• Annemarie B. Docherty   ORCID: orcid.org/0000-0001-8277-420X 10 ,
• James D. Chalmers 12 ,
• Ling-Pei Ho   ORCID: orcid.org/0000-0001-8319-301X 13 ,
• Alexander Horsley   ORCID: orcid.org/0000-0003-1828-0058 14 ,
• Betty Raman 15 ,
• Krisnah Poinasamy 16 ,
• Michael Marks 17 , 18 , 19 ,
• Onn Min Kon 1 ,
• Luke S. Howard   ORCID: orcid.org/0000-0003-2822-210X 1 ,
• Daniel G. Wootton 3 ,
• Jennifer K. Quint 1 ,
• Thushan I. de Silva   ORCID: orcid.org/0000-0002-6498-9212 11 ,
• Antonia Ho 20 ,
• Christopher Chiu   ORCID: orcid.org/0000-0003-0914-920X 1 ,
• Ewen M. Harrison   ORCID: orcid.org/0000-0002-5018-3066 10 ,
• William Greenhalf 21 ,
• J. Kenneth Baillie   ORCID: orcid.org/0000-0001-5258-793X 10 , 22 , 23 ,
• Malcolm G. Semple   ORCID: orcid.org/0000-0001-9700-0418 3 , 24 ,
• Lance Turtle 3 , 24 ,
• Rachael A. Evans   ORCID: orcid.org/0000-0002-1667-868X 2 ,
• Louise V. Wain 2 , 6 ,
• Christopher Brightling 2 ,
• Ryan S. Thwaites   ORCID: orcid.org/0000-0003-3052-2793 1   na1 ,
• Peter J. M. Openshaw   ORCID: orcid.org/0000-0002-7220-2555 1   na1 ,
• PHOSP-COVID collaborative group &

ISARIC investigators

Nature Immunology ( 2024 ) Cite this article

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• Inflammasome
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One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood 1 . Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain–gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

One in ten severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections results in post-acute sequelae of coronavirus disease 2019 (PASC) or long coronavirus disease (COVID), which affects 65 million people worldwide 1 . Long COVID (LC) remains common, even after mild acute infection with recent variants 2 , and it is likely LC will continue to cause substantial long-term ill health, requiring targeted management based on an understanding of how disease phenotypes relate to underlying mechanisms. Persistent inflammation has been reported in adults with LC 1 , 3 , but studies have been limited in size, timing of samples or breadth of immune mediators measured, leading to inconsistent or absent associations with symptoms. Markers of oxidative stress, metabolic disturbance, vasculoproliferative processes and IFN-, NF-κB- or monocyte-related inflammation have been suggested 3 , 4 , 5 , 6 .

The PHOSP-COVID study, a multicenter United Kingdom study of patients previously hospitalized with COVID-19, has reported inflammatory profiles in 626 adults with health impairment after COVID-19, identified through clustering. Elevated IL-6 and markers of mucosal inflammation were observed in those with severe impairment compared with individuals with milder impairment 7 . However, LC is a heterogeneous condition that may be a distinct form of health impairment after COVID-19, and it remains unclear whether there are inflammatory changes specific to LC symptom subtypes. Determining whether activated inflammatory pathways underlie all cases of LC or if mechanisms differ according to clinical presentation is essential for developing effective therapies and has been highlighted as a top research priority by patients and clinicians 8 .

In this Letter, in a prospective multicenter study, we measured 368 plasma proteins in 657 adults previously hospitalized for COVID-19 (Fig. 1a and Table 1 ). Individuals in our cohort experienced a range of acute COVID-19 severities based on World Health Organization (WHO) progression scores 9 ; WHO 3–4 (no oxygen support, n  = 133 and median age of 55 years), WHO 5–6 (oxygen support, n  = 353 and median age of 59 years) and WHO 7–9 (critical care, n  = 171 and median age of 57 years). Participants were hospitalized for COVID-19 ≥3 months before sample collection (median 6.1 months, interquartile range (IQR) 5.1–6.8 months and range 3.0–8.3 months) and confirmed clinically ( n  = 36/657) or by PCR ( n  = 621/657). Symptom data indicated 233/657 (35%) felt fully recovered at 6 months (hereafter ‘recovered’) and the remaining 424 (65%) reported symptoms consistent with the WHO definition for LC (symptoms ≥3 months post infection 10 ). Given the diversity of LC presentations, patients were grouped according to symptom type (Fig. 1b ). Groups were defined using symptoms and health deficits that have been commonly reported in the literature 1 ( Methods ). A multivariate penalized logistic regression model (PLR) was used to explore associations of clinical covariates and immune mediators at 6 months between recovered patients ( n  = 233) and each LC group (cardiorespiratory symptoms, cardioresp, n  = 398, Fig. 1c ; fatigue, n  = 384, Fig. 1d ; affective symptoms, anxiety/depression, n  = 202, Fig. 1e ; gastrointestinal symptoms, GI, n  = 132, Fig. 1f ; and cognitive impairment, cognitive, n  = 61, Fig. 1g ). Women ( n  = 239) were more likely to experience CardioResp (odds ratio (OR 1.14), Fatigue (OR 1.22), GI (OR 1.13) and Cognitive (OR 1.03) outcomes (Fig. 1c,d,f,g ). Repeated cross-validation was used to optimize and assess model performance ( Methods and Extended Data Fig. 1 ). Pre-existing conditions, such as chronic lung disease, neurological disease and cardiovascular disease (Supplementary Table 1 ), were associated with all LC groups (Fig. 1c–g ). Age, C-reactive protein (CRP) and acute disease severity were not associated with any LC group (Table 1 ).

a , Distribution of time from COVID-19 hospitalization at sample collection. All samples were cross-sectional. The vertical red line indicates the 3 month cutoff used to define our final cohort and samples collected before 3 months were excluded. b , An UpSet plot describing pooled LC groups. The horizontal colored bars represent the number of patients in each symptom group: cardiorespiratory (Cardio_Resp), fatigue, cognitive, GI and anxiety/depression (Anx_Dep). Vertical black bars represent the number of patients in each symptom combination group. To prevent patient identification, where less than five patients belong to a combination group, this has been represented as ‘<5’. The recovered group ( n  = 233) were used as controls. c – g , Forest plots of Olink protein concentrations (NPX) associated with Cardio_Resp ( n  = 365) ( c ), fatigue (n = 314) ( d ), Anx_Dep ( n  = 202) ( e ), GI ( n  = 124) ( f ) and cognitive ( n  = 60) ( g ). Neuro_Psych, neuropsychiatric. The error bars represent the median accuracy of the model. h , i , Distribution of Olink values (NPX) for IL-1R2 ( h ) and MATN2, neurofascin and sCD58 ( i ) measured between symptomatic and recovered individuals in recovered ( n  = 233), Cardio_Resp ( n  = 365), fatigue ( n  = 314) and Anx_Dep ( n  = 202) groups ( h ) and MATN2 in GI ( n  = 124), neurofascin in cognitive ( n  = 60) and sCD58 in Cardio_Resp and recovered groups ( i ). The box plot center line represents the median, the boundaries represent IQR and the whisker length represents 1.5× IQR. The median values were compared between groups using two-sided Wilcoxon signed-rank test, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001.

To study the association of peripheral inflammation with symptoms, we analyzed cross-sectional data collected approximately 6 months after hospitalizations. We measured 368 immune mediators from plasma collected contemporaneously with symptom data. Mediators suggestive of myeloid inflammation were associated with all symptoms (Fig. 1c–h ). Elevated IL-1R2, an IL-1 receptor expressed by monocytes and macrophages modulating inflammation 11 and MATN2, an extracellular matrix protein that modulates tissue inflammation through recruitment of innate immune cells 12 , were associated with cardioresp (IL-1R2 OR 1.14, Fig. 1c,h ), fatigue (IL-1R2 OR 1.45, Fig. 1d,h ), anxiety/depression (IL-1R2 OR 1.34. Fig. 1e,h ) and GI (MATN2 OR 1.08, Fig. 1f ). IL-3RA, an IL-3 receptor, was associated with cardioresp (OR 1.07, Fig. 1c ), fatigue (OR 1.21, Fig. 1d ), anxiety/depression (OR 1.12, Fig. 1e ) and GI (OR 1.06, Fig. 1f ) groups, while CSF3, a cytokine promoting neutrophilic inflammation 13 , was elevated in cardioresp (OR 1.06, Fig. 1c ), fatigue (OR 1.12, Fig. 1d ) and GI (OR 1.08, Fig. 1f ).

Elevated COLEC12, which initiates inflammation in tissues by activating the alternative complement pathway 14 , associated with cardioresp (OR 1.09, Fig. 1c ), fatigue (OR 1.19, Fig. 1d ) and anxiety/depression (OR 1.11, Fig. 1e ), but not with GI (Fig. 1f ) and only weakly with cognitive (OR 1.02, Fig. 1g ). C1QA, a degradation product released by complement activation 15 was associated with GI (OR 1.08, Fig. 1f ) and cognitive (OR 1.03, Fig. 1g ). C1QA, which is known to mediate dementia-related neuroinflammation 16 , had the third strongest association with cognitive (Fig. 1g ). These observations indicated that myeloid inflammation and complement activation were associated with LC.

Increased expression of DPP10 and SCG3 was observed in the GI group compared with recovered (DPP10 OR 1.07 and SCG3 OR 1.08, Fig. 1f ). DPP10 is a membrane protein that modulates tissue inflammation, and increased DPP10 expression is associated with inflammatory bowel disease 17 , 18 , suggesting that GI symptoms may result from enteric inflammation. Elevated SCG3, a multifunctional protein that has been associated with irritable bowel syndrome 19 , suggested that noninflammatory disturbance of the brain–gut axis or dysbiosis, may occur in the GI group. The cognitive group was associated with elevated CTSO (OR 1.04), NFASC (OR 1.03) and SPON-1 (OR 1.02, Fig. 1g,i ). NFASC and SPON-1 regulate neural growth 20 , 21 , while CTSO is a cysteine proteinase supporting tissue turnover 22 . The increased expression of these three proteins as well as C1QA and DPP10 in the cognitive group (Fig. 1g ) suggested neuroinflammation and alterations in nerve tissue repair, possibly resulting in neurodegeneration. Together, our findings indicated that complement activation and myeloid inflammation were common to all LC groups, but subtle differences were observed in the GI and cognitive groups, which may have mechanistic importance. Acutely elevated fibrinogen during hospitalization has been reported to be predictive of LC cognitive deficits 23 . We found elevated fibrinogen in LC relative to recovered (Extended Data Fig. 2a ; P  = 0.0077), although this was not significant when restricted to the cognitive group ( P  = 0.074), supporting our observation of complement pathway activation in LC and in keeping with reports that complement dysregulation and thrombosis drive severe COVID-19 (ref. 24 ).

Elevated sCD58 was associated with lower odds of all LC symptoms and was most pronounced in cardioresp (OR 0.85, Fig. 1c,i ), fatigue (OR 0.80, Fig. 1d ) and anxiety/depression (OR 0.83, Fig. 1e ). IL-2 was negatively associated with the cardioresp (Fig. 1c , OR 0.87), fatigue (Fig. 1d , OR 0.80), anxiety/depression (Fig. 1e , OR 0.84) and cognitive (Fig. 1g , OR 0.96) groups. Both IL-2 and sCD58 have immunoregulatory functions 25 , 26 . Specifically, sCD58 suppresses IL-1- or IL-6-dependent interactions between CD2 + monocytes and CD58 + T or natural killer cells 26 . The association of sCD58 with recovered suggests a central role of dysregulated myeloid inflammation in LC. Elevated markers of tissue repair, IDS and DNER 27 , 28 , were also associated with recovered relative to all LC groups (Fig. 1c–g ). Taken together, our data suggest that suppression of myeloid inflammation and enhanced tissue repair were associated with recovered, supporting the use of immunomodulatory agents in therapeutic trials 29 (Supplementary Table 2 ).

We next sought to validate the experimental and analytical approaches used. Although Olink has been validated against other immunoassay platforms, showing superior sensitivity and specificity 30 , 31 , we confirmed the performance of Olink against chemiluminescent immunoassays within our cohort. We performed chemiluminescent immunoassays on plasma from a subgroup of 58 participants (recovered n  = 13 and LC n  = 45). There were good correlations between results from Olink (normalized protein expression (NPX)) and chemiluminescent immunoassays (pg ml −1 ) for CSF3, IL-1R2, IL-3RA, TNF and TFF2 (Extended Data Fig. 3 ). Most samples did not have concentrations of IL-2 detectable using a mesoscale discovery chemiluminescent assay, limiting this analysis to 14 samples (recovered n  = 4, LC n  = 10, R  = 0.55 and P  = 0.053, Extended Data Fig. 3 ). We next repeated our analysis using alternative definitions of LC. The Centers for Disease Control and Prevention and National Institute for Health and Care Excellence definitions for LC include symptoms occurring 1 month post infection 32 , 33 . Using the 1 month post-infection definition included 62 additional participants to our analysis (recovered n  = 21, 3 females and median age 61 years and LC n  = 41, 15 females and median age 60 years, Extended Data Fig. 2c ) and found that inflammatory associations with each LC group were consistent with our analysis based on the WHO definition (Extended Data Fig. 2d–h ). Finally, to validate the analytical approach (PLR) we examined the distribution of data, prioritizing proteins that were most strongly associated with each LC/recovered group (IL-1R2, MATN2, NFASC and sCD58). Each protein was significantly elevated in the LC group compared with recovered (Fig. 1h,i and Extended Data Fig. 4 ), consistent with the PLR. Alternative regression approaches (unadjusted regression models and partial least squares, PLS) reported results consistent with the original analysis of protein associations and LC outcome in the WHO-defined cohort (Fig. 1c–g , Supplementary Table 3 and Extended Data Figs. 5 and 6 ). The standard errors of PLS estimates were wide (Extended Data Fig. 6 ), consistent with previous demonstrations that PLR is the optimal method to analyze high-dimensional data where variables may have combined effects 34 . As inflammatory proteins are often colinear, working in-tandem to mediate effects, we prioritized PLR results to draw conclusions.

To explore the relationship between inflammatory mediators associated with different LC symptoms, we performed a network analysis of Olink mediators highlighted by PLR within each LC group. COLEC12 and markers of endothelial and mucosal inflammation (MATN2, PCDH1, ROBO1, ISM1, ANGPTL2, TGF-α and TFF2) were highly correlated within the cardioresp, fatigue and anxiety/depression groups (Fig. 2 and Extended Data Fig. 7 ). Elevated PCDH1, an adhesion protein modulating airway inflammation 35 , was highly correlated with other inflammatory proteins associated with the cardioresp group (Fig. 2 ), suggesting that systemic inflammation may arise from the lung in these individuals. This was supported by increased expression of IL-3RA, which regulates innate immune responses in the lung through interactions with circulating IL-3 (ref. 36 ), in fatigue (Figs. 1d and 2 ), which correlated with markers of tissue inflammation, including PCDH1 (Fig. 2 ). MATN2 and ISM1, mucosal proteins that enhance inflammation 37 , 38 , were highly correlated in the GI group (Fig. 2 ), highlighting the role of tissue-specific inflammation in different LC groups. SCG3 correlated less closely with mediators in the GI group (Fig. 2 ), suggesting that the brain–gut axis may contribute separately to some GI symptoms. SPON-1, which regulates neural growth 21 , was the most highly correlated mediator in the cognitive group (Fig. 2 and Extended Data Fig. 7 ), highlighting that processes within nerve tissue may underlie this group. These observations suggested that inflammation might arise from mucosal tissues and that additional mechanisms may contribute to pathophysiology underlying the GI and cognitive groups.

Network analysis of Olink mediators associated with cardioresp ( n  = 365), fatigue ( n  = 314), anxiety/depression ( n  = 202), GI ( n  = 124) and cognitive groups ( n  = 60). Each node corresponds to a protein mediator identified by PLR. The edges (blue lines) were weighted according to the size of Spearman’s rank correlation coefficient between proteins. All edges represent positive and significant correlations ( P  < 0.05) after FDR adjustment.

Women were more likely to experience LC (Table 1 ), as found in previous studies 1 . As estrogen can influence immunological responses 39 , we investigated whether hormonal differences between men and women with LC in our cohort explained this trend. We grouped men and women with LC symptoms into two age groups (those younger than 50 years and those 50 years and older, using age as a proxy for menopause status in women) and compared mediator levels between men and women in each age group, prioritizing those identified by PLR to be higher in LC compared with recovered. As we aimed to understand whether women with LC had stronger inflammatory responses than men with LC, we did not assess differences in men and women in the recovered group. IL-1R2 and MATN2 were significantly higher in women ≥50 years than men ≥50 years in the cardioresp group (Fig. 3a , IL-1R2 and MATN2) and the fatigue group (Fig. 3b ). In the GI group, CSF3 was higher in women ≥50 years compared with men ≥50 years (Fig. 3c ), indicating that the inflammatory markers observed in women were not likely to be estrogen-dependent. Women have been reported to have stronger innate immune responses to infection and to be at greater risk of autoimmunity 39 , possibly explaining why some women in the ≥50 years group had higher inflammatory proteins than men the same group. Proteins associated with the anxiety/depression (IL-1R2 P  = 0.11 and MATN2 P  = 0.61, Extended Data Fig. 8a ) and cognitive groups (CTSO P  = 0.64 and NFASC P  = 0.41, Extended Data Fig. 8b ) were not different between men and women in either age group, consistent with the absent/weak association between sex and these outcomes identified by PLR (Fig. 1e,g ). Though our findings suggested that nonhormonal differences in inflammatory responses may explain why some women are more likely to have LC, they require confirmation in adequately powered studies.

a – c , Olink-measured plasma protein levels (NPX) of IL-1R2 and MATN2 ( a and b ) and CSF3 ( c ) between LC men and LC women divided by age (<50 or ≥50 years) in the cardiorespiratory group (<50 years n  = 8 and ≥50 years n  = 270) ( a ), fatigue group (<50 years n  = 81 and ≥50 years n  = 227) ( b ) and GI group (<50 years n  = 34 and ≥50 years n  = 82) ( c ). the median values were compared between men and women using two-sided Wilcoxon signed-rank test, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001. The box plot center line represents the median, the boundaries represent IQR and the whisker length represents 1.5× IQR.

To test whether local respiratory tract inflammation persisted after COVID-19, we compared nasosorption samples from 89 participants (recovered, n  = 31; LC, n  = 33; and healthy SARS-CoV-2 naive controls, n  = 25, Supplementary Tables 4 and 5 ). Several inflammatory markers were elevated in the upper respiratory tract post COVID (including IL-1α, CXCL10, CXCL11, TNF, VEGF and TFF2) when compared with naive controls, but similar between recovered and LC (Fig. 4a ). In the cardioresp group ( n  = 29), inflammatory mediators elevated in plasma (for example, IL-6, APO-2, TGF-α and TFF2) were not elevated in the upper respiratory tract (Extended Data Fig. 9a ) and there was no correlation between plasma and nasal mediator levels (Extended Data Fig. 9b ). This exploratory analysis suggested upper respiratory tract inflammation post COVID was not specifically associated with cardiorespiratory symptoms.

a , Nasal cytokines measured by immunoassay in post-COVID participants ( n  = 64) compared with healthy SARS-CoV-2 naive controls ( n  = 25), and between the the cardioresp group ( n  = 29) and the recovered group ( n  = 31). The red values indicate significantly increased cytokine levels after FDR adjustment ( P  < 0.05) using two-tailed Wilcoxon signed-rank test. b , SARS-CoV-2 N antigen measured in sputum by electrochemiluminescence from recovered ( n  = 17) and pooled LC ( n  = 23) groups, compared with BALF from SARS-CoV-2 naive controls ( n  = 9). The horizontal dashed line indicates the lower limit of detection of the assay. c , Plasma S- and N-specific IgG responses measured by electrochemiluminescence in the LC ( n  = 35) and recovered ( n  = 19) groups. The median values were compared using two-sided Wilcoxon signed-rank tests, NS P  > 0.05, * P  < 0.05, ** P  < 0.01, *** P  < 0.001 and **** P  < 0.0001. The box plot center lines represent the median, the boundaries represent IQR and the whisker length represents 1.5× IQR.

To explore whether SARS-CoV-2 persistence might explain the inflammatory profiles observed in the cardioresp group, we measured SARS-CoV-2 nucleocapsid (N) antigen in sputum from 40 participants (recovered n  = 17 and LC n  = 23) collected approximately 6 months post hospitalization (Supplementary Table 6 ). All samples were compared with prepandemic bronchoalveolar lavage fluid ( n  = 9, Supplementary Table 4 ). Only four samples (recovered n  = 2 and LC n  = 2) had N antigen above the assay’s lower limit of detection, and there was no difference in N antigen concentrations between LC and recovered (Fig. 4b , P  = 0.78). These observations did not exclude viral persistence, which might require tissues samples for detection 40 , 41 . On the basis of the hypothesis that persistent viral antigen might prevent a decline in antibody levels over time, we examined the titers of SARS-CoV-2-specific antibodies in unvaccinated individuals (recovered n  = 19 and LC n  = 35). SARS-CoV-2 N-specific ( P  = 0.023) and spike (S)-specific ( P  = 0.0040) immunoglobulin G (IgG) levels were elevated in LC compared with recovered (Fig. 4c ).

Overall, we identified myeloid inflammation and complement activation in the cardioresp, fatigue, anxiety/depression, cognitive and GI groups 6 months after hospitalization (Extended Data Fig. 10 ). Our findings build on results of smaller studies 5 , 6 , 42 and are consistent with a genome-wide association study that identified an independent association between LC and FOXP4 , which modulates neutrophilic inflammation and immune cell function 43 , 44 . In addition, we identified tissue-specific inflammatory elements, indicating that myeloid disturbance in different tissues may result in distinct symptoms. Multiple mechanisms for LC have been suggested, including autoimmunity, thrombosis, vascular dysfunction, SARS-CoV-2 persistence and latent virus reactivation 1 . All these processes involve myeloid inflammation and complement activation 45 . Complement activation in LC has been suggested in a proteomic study in 97 mostly nonhospitalized COVID-19 cases 42 and a study of 48 LC patients, of which one-third experienced severe acute disease 46 . As components of the complement system are known to have a short half-life 47 , ongoing complement activation suggests active inflammation rather than past tissue damage from acute infection.

Despite the heterogeneity of LC and the likelihood of coexisting or multiple etiologies, our work suggests some common pathways that might be targeted therapeutically and supports the rationale for several drugs currently under trial. Our finding of increased sCD58 levels (associated with suppression of monocyte–lymphocyte interactions 26 ) in the recovered group, strengthens our conclusion that myeloid inflammation is central to the biology of LC and that trials of steroids, IL-1 antagonists, JAK inhibitors, naltrexone and colchicine are justified. Although anticoagulants such as apixaban might prevent thrombosis downstream of complement dysregulation, they can also increase the risk of serious bleeding when given after COVID-19 hospitalization 48 . Thus, clinical trials, already underway, need to carefully assess the risks and benefits of anticoagulants (Supplementary Table 2 ).

Our finding of elevated S- and N-specific IgG in LC could suggest viral persistence, as found in other studies 6 , 42 , 49 . Our network analysis indicated that inflammatory proteins in the cardioresp group interacted strongly with ISM1 and ROBO1, which are expressed during respiratory tract infection and regulate lung inflammation 50 , 51 . Although we were unable to find SARS-CoV-2 antigen in sputum from our LC cases, we did not test for viral persistence in GI tract and lung tissue 40 , 41 or in plasma 52 . Evidence of SARS-CoV-2 persistence would justify trials of antiviral drugs (singly or in combination) in LC. It is also possible that autoimmune processes could result in an innate inflammatory profile in LC. Autoreactive B cells have been identified in LC patients with higher SARS-CoV-2-specific antibody titers in a study of mostly mild acute COVID cases (59% WHO 2–3) 42 , a different population from our study of hospitalized cases.

Our observations of distinct protein profiles in GI and cognitive groups support previous reports on distinct associations between Epstein–Barr virus reactivation and neurological symptoms, or autoantibodies and GI symptoms relative to other forms of LC 49 , 53 . We did not assess autoantibody induction but found evidence of brain–gut axis disturbance (SCG3) in the GI group, which occurs in many autoimmune diseases 54 . We found signatures suggestive of neuroinflammation (C1QA) in the cognitive group, consistent with findings of brain abnormalities on magnetic resonance imaging after COVID-19 hospitalization 55 , as well as findings of microglial activation in mice after COVID-19 (ref. 56 ). Proinflammatory signatures dominated in the cardioresp, fatigue and anxiety/depression groups and were consistent with those seen in non-COVID depression, suggesting shared mechanisms 57 . The association between markers of myeloid inflammation, including IL-3RA, and symptoms was greatest for fatigue. Whilst membrane-bound IL-3RA facilitates IL-3 signaling upstream of myelopoesis 36 its soluble form (measured in plasma) can bind IL-3 and can act as a decoy receptor, preventing monocyte maturation and enhancing immunopathology 58 . Monocytes from individuals with post-COVID fatigue are reported to have abnormal expression profiles (including reduced CXCR2), suggestive of altered maturation and migration 5 , 59 . Lung-specific inflammation was suggested by the association between PCDH1 (an airway epithelial adhesion molecule 35 ) and cardioresp symptoms.

Our observations do not align with all published observations on LC. One proteomic study of 55 LC cases after generally mild (WHO 2–3) acute disease found that TNF and IFN signatures were elevated in LC 3 . Vasculoproliferative processes and metabolic disturbance have been reported in LC 4 , 60 , but these studies used uninfected healthy individuals for comparison and cannot distinguish between LC-specific phenomena and residual post-COVID inflammation. A study of 63 adults (LC, n  = 50 and recovered, n  = 13) reported no association between immune cell activation and LC 3 months after infection 61 , though myeloid inflammation was not directly measured, and 3 months post infection may be too early to detect subtle differences between LC and recovered cases due to residual acute inflammation.

Our study has limitations. We designed the study to identify inflammatory markers identifying pathways underlying LC subgroups rather than diagnostic biomarkers. The ORs we report are small, but associations were consistent across alternative methods of analysis and when using different LC definitions. Small effect sizes can be expected when using PLR, which shrinks correlated mediator coefficients to reflect combined effects and prevent colinear inflation 62 , and could also result from measurement of plasma mediators that may underestimate tissue inflammation. Although our LC cohort is large compared with most other published studies, some of our subgroups are small (only 60 cases were designated cognitive). Though the performance of the cognitive PLR model was adequate, our findings should be validated in larger studies. It should be noted that our cohort of hospitalized cases may not represent all types of LC, especially those occurring after mild infection. We looked for an effect of acute disease severity within our study and did not find it, and are reassured that the inflammatory profiles we observed were consistent with those seen in smaller studies including nonhospitalized cases 42 , 46 . Studies of posthospital LC may be confounded by ‘posthospital syndrome’, which encompasses general and nonspecific effects of hospitalization (particularly intensive care) 63 .

In conclusion, we found markers of myeloid inflammation and complement activation in our large prospective posthospital cohort of patients with LC, in addition to distinct inflammatory patterns in patients with cognitive impairment or gastrointestinal symptoms. These findings show the need to consider subphenotypes in managing patients with LC and support the use of antiviral or immunomodulatory agents in controlled therapeutic trials.

Study design and ethics

After hospitalization for COVID-19, adults who had no comorbidity resulting in a prognosis of less than 6 months were recruited to the PHOSP-COVID study ( n  = 719). Patients hospitalized between February 2020 and January 2021 were recruited. Both sexes were recruited and gender was self-reported (female, n  = 257 and male, n  = 462). Written informed consent was obtained from all patients. Ethical approvals for the PHOSP-COVID study were given by Leeds West Research Ethics Committee (20/YH/0225).

Symptom data and samples were prospectively collected from individuals approximately 6 months (IQR 5.1–6.8 months and range 3.0–8.3 months) post hospitalization (Fig. 1a ), via the PHOSP-COVID multicenter United Kingdom study 64 . Data relating to patient demographics and acute admission were collected via the International Severe Acute Respiratory and Emerging Infection Consortium World Health Organization Clinical Characterisation Protocol United Kingdom (ISARIC4C study; IRAS260007/IRAS126600) (ref. 65 ). Adults hospitalized during the SARS-CoV-2 pandemic were systematically recruited into ISARIC4C. Written informed consent was obtained from all patients. Ethical approval was given by the South Central–Oxford C Research Ethics Committee in England (reference 13:/SC/0149), Scotland A Research Ethics Committee (20/SS/0028) and WHO Ethics Review Committee (RPC571 and RPC572l, 25 April 2013).

Data were collected to account for variables affecting symptom outcome, via hospital records and self-reporting. Acute disease severity was classified according to the WHO clinical progression score: WHO class 3–4: no oxygen therapy; class 5: oxygen therapy; class 6: noninvasive ventilation or high-flow nasal oxygen; and class 7–9: managed in critical care 9 . Clinical data were used to place patients into six categories: ‘recovered’, ‘GI’, ‘cardiorespiratory’, ‘fatigue’, ‘cognitive impairment’ and ‘anxiety/depression’ (Supplementary Table 7 ). Patient-reported symptoms and validated clinical scores were used when feasible, including Medical Research Council (MRC) breathlessness score, dyspnea-12 score, Functional Assessment of Chronic Illness Therapy (FACIT) score, Patient Health Questionnaire (PHQ)-9 and Generalized Anxiety Disorder (GAD)-7. Cognitive impairment was defined as a Montreal Cognitive Assessment score <26. GI symptoms were defined as answering ‘Yes’ to the presence of at least two of the listed symptoms. ‘Recovered’ was defined by self-reporting. Patients were placed in multiple groups if they experienced a combination of symptoms.

Matched nasal fluid and sputum samples were prospectively collected from a subgroup of convalescent patients approximately 6 months after hospitalization via the PHOSP-COVID study. Nasal and bronchoalveolar lavage fluid (BALF) collected from healthy volunteers before the COVID-19 pandemic were used as controls (Supplementary Table 4 ). Written consent was obtained for all individuals and ethical approvals were given by London–Harrow Research Ethics Committee (13/LO/1899) for the collection of nasal samples and the Health Research Authority London–Fulham Research Ethics Committee (IRAS project ID 154109; references 14/LO/1023, 10/H0711/94 and 11/LO/1826) for BALF samples.

Ethylenediaminetetraacetic acid plasma was collected from whole blood taken by venepuncture and frozen at −80 °C as previously described 7 , 66 . Nasal fluid was collected using a NasosorptionTM FX·I device (Hunt Developments), which uses a synthetic absorptive matrix to collect concentrated nasal fluid. Samples were eluted and stored as previously described 67 . Sputum samples were collected via passive expectoration and frozen at −80 °C without the addition of buffers. Sputum samples from convalescent individuals were compared with BALF from healthy SARS-CoV-2-naive controls, collected before the pandemic. BALF samples were used to act as a comparison for lower respiratory tract samples since passively expectorated sputum from healthy SARS-CoV-2-naive individuals was not available. BALF samples were obtained by instillation and recovery of up to 240 ml of normal saline via a fiberoptic bronchoscope. BALF was filtered through 100 µM strainers into sterile 50 ml Falcon tubes, then centrifuged for 10 min at 400  g at 4 °C. The resulting supernatant was transferred into sterile 50 ml Falcon tubes and frozen at −80 °C until use. The full methods for BALF collection and processing have been described previously 68 , 69 .

Immunoassays

To determine inflammatory signatures that associated with symptom outcomes, plasma samples were analyzed on an Olink Explore 384 Inflammation panel 70 . Supplementary Table 8 (Appendix 1 ) lists all the analytes measured. To ensure the validity of results, samples were run in a single batch with the use of negative controls, plate controls in triplicate and repeated measurement of patient samples between plates in duplicate. Samples were randomized between plates according to site and sample collection date. Randomization between plates was blind to LC/recovered outcome. Data were first normalized to an internal extension control that was included in each sample well. Plates were standardized by normalizing to interplate controls, run in triplicate on each plate. Each plate contained a minimum of four patient samples, which were duplicates on another plate; these duplicate pairs allowed any plate to be linked to any other through the duplicates. Data were then intensity normalized across all cohort samples. Finally, Olink results underwent quality control processing and samples or analytes that did not reach quality control standards were excluded. Final normalized relative protein quantities were reported as log 2 NPX values.

To further validate our findings, we performed conventional electrochemiluminescence (ECL) assays and enzyme-linked immunosorbent assay for Olink mediators that were associated with symptom outcome ( Supplementary Methods ). Contemporaneously collected plasma samples were available from 58 individuals. Like most omics platforms, Olink measures relative quantities, so perfect agreement with conventional assays that measure absolute concentrations is not expected.

Sputum samples were thawed before analysis and sputum plugs were extracted with the addition of 0.1% dithiothreitol creating a one in two sample dilution, as previously described 71 . SARS-CoV-2 S and N proteins were measured by ECL S-plex assay at a fixed dilution of one in two (Mesoscale Diagnostics), as per the manufacturers protocol 72 . Control BALF samples were thawed and measured on the same plate, neat. The S-plex assay is highly sensitive in detecting viral antigen in respiratory tract samples 73 .

Nasal cytokines were measured by ECL (mesoscale discovery) and Luminex bead multiplex assays (Biotechne). The full methods and list of analytes are detailed in Supplementary Methods .

Statistics and reproducibility

Clinical data was collected via the PHOSP REDCap database, to which access is available under reasonable request as per the data sharing statement in the manuscript. All analyses were performed within the Outbreak Data Analysis Platform (ODAP). All data and code can be accessed using information in the ‘Data sharing’ and ‘Code sharing’ statements at the end of the manuscript. No statistical method was used to predetermine sample size. Data distribution was assumed to be normal but this was not formally tested. Olink assays and immunoassays were randomized and investigators were blinded to outcomes.

To determine protein signatures that associated with each symptom outcome, a ridge PLR was used. PLR shrinks coefficients to account for combined effects within high-dimensional data, preventing false discovery while managing multicollinearity 34 . Thus, PLR was chosen a priori as the most appropriate model to assess associations between a large number of explanatory variables (that may work together to mediate effects) and symptom outcome 34 , 62 , 70 , 74 . In keeping with our aim to perform an unbiased exploration of inflammatory process, the model alpha was set to zero, facilitating regularization without complete penalization of any mediator. This enabled review of all possible mediators that might associate with LC 62 .

A 50 repeats tenfold nested cross-validation was used to select the optimal lambda for each model and assess its accuracy (Extended Data Fig. 1 ). The performance of the cognitive impairment model was influenced by the imbalance in size of the symptom group ( n  = 60) relative to recovered ( n  = 250). The model was weighted to account for this imbalance resulting in a sensitivity of 0.98, indicating its validity. We have expanded on the model performance and validation approaches in Supplementary Information .

Age, sex, acute disease severity and preexisting comorbidities were included as covariates in the PLR analysis (Supplementary Tables 1 and 3 ). Covariates were selected a priori using features reported to influence the risk of LC and inflammatory responses 1 , 39 , 64 , 75 . Ethnicity was not included since it has been shown not to predict symptom outcome in this cohort 64 . Individuals with missing data were excluded from the regression analysis. Each symptom group was compared with the ‘recovered’ group. The model coefficients of each covariate were converted into ORs for each outcome and visualized in a forest plot, after removing variables associated with regularized OR between 0.98 and 1.02 or in cases where most variables fell outside of this range, using mediators associated with the highest decile of coefficients either side of this range. This enabled exclusion of mediators with effect sizes that were unlikely to have clinical or mechanistic importance since the ridge PLR shrinks and orders coefficients according to their relative importance rather than making estimates with standard error. Thus, confidence intervals cannot be appropriately derived from PLR, and forest plot error bars were calculated using the median accuracy of the model generated by the nested cross-validation. To verify observations made through PLR analysis, we also performed an unadjusted PLR, an unadjusted logistic regression and a PLS analysis. Univariate analyses using Wilcoxon signed-rank test was also performed (Supplementary Table 8 , Appendix 1 ). Analyses were performed in R version 4.2.0 using ‘data.table v1.14.2’, ‘EnvStats v2.7.0’ ‘tidyverse v1.3.2’, ‘lme4 v1.1-32’, ‘caret v6.0-93’, ‘glmnet v4.1-6’, ‘mdatools v0.14.0’, ‘ggpubbr v0.4.0’ and ‘ggplot2 v3.3.6’ packages.

To further investigate the relationship between proteins elevated in each symptom group, we performed a correlation network analysis using Spearman’s rank correlation coefficient and false discovery rate (FDR) thresholding. The mediators visualized in the PLR forest plots, which were associated with cardiorespiratory symptoms, fatigue, anxiety/depression GI symptoms and cognitive impairment were used, respectively. Analyses were performed in R version 4.2.0 using ‘bootnet v1.5.6 ’ and ‘qgraph v1.9.8 ’ packages.

To determine whether differences in protein levels between men and women related to hormonal differences, we divided each symptom group into premenopausal and postmenopausal groups using an age cutoff of 50 years old. Differences between sexes in each group were determined using the Wilcoxon signed-rank test. To understand whether antigen persistence contributed to inflammation in adults with LC, the median viral antigen concentration from sputum/BALF samples and cytokine concentrations from nasal samples were compared using the Wilcoxon signed-rank test. All tests were two-tailed and statistical significance was defined as a P value < 0.05 after adjustment for FDR ( q -value of 0.05). Analyses were performed in R version 4.2.0 using ‘bootnet v1.5.6’ and ‘qgraph v1.9.8’ packages.

Extended Data Fig. 10 was made using Biorender, accessed at www.biorender.com .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

This is an open access article under the CC BY 4.0 license.

The PHOSP-COVID protocol, consent form, definition and derivation of clinical characteristics and outcomes, training materials, regulatory documents, information about requests for data access, and other relevant study materials are available online at ref. 76 . Access to these materials can be granted by contacting [email protected] and [email protected].

The ISARIC4C protocol, data sharing and publication policy are available at https://isaric4c.net . ISARIC4C’s Independent Data and Material Access Committee welcomes applications for access to data and materials ( https://isaric4c.net ).

The datasets used in the study contain extensive clinical information at an individual level that prevent them from being deposited in an public depository due to data protection policies of the study. Study data can only be accessed via the ODAP, a protected research environment. All data used in this study are available within ODAP and accessible under reasonable request. Data access criteria and information about how to request access is available online at ref. 76 . If criteria are met and a request is made, access can be gained by signing the eDRIS user agreement.

Code availability

Code was written within the ODAP, using R v4.2.0 and publicly available packages (‘data.table v1.14.2’, ‘EnvStats v2.7.0’, ‘tidyverse v1.3.2’, ‘lme4 v1.1-32’, ‘caret v6.0-93’, ‘glmnet v4.1-6’, ‘mdatools v0.14.0’, ‘ggpubbr v0.4.0’, ‘ggplot2 v3.3.6’, ‘bootnet v1.5.6’ and ‘qgraph v1.9.8’ packages). No new algorithms or functions were created and code used in-built functions in listed packages available on CRAN. The code used to generate data and to analyze data is publicly available at https://github.com/isaric4c/wiki/wiki/ISARIC ; https://github.com/SurgicalInformatics/cocin_cc and https://github.com/ClaudiaEfstath/PHOSP_Olink_NatImm .

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Acknowledgements

This research used data assets made available by ODAP as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref. MC_PC_20058). This work is supported by the following grants: the PHOSP-COVD study is jointly funded by UK Research and Innovation and National Institute of Health and Care Research (NIHR; grant references MR/V027859/1 and COV0319). ISARIC4C is supported by grants from the National Institute for Health and Care Research (award CO-CIN-01) and the MRC (grant MC_PC_19059) Liverpool Experimental Cancer Medicine Centre provided infrastructure support for this research (grant reference C18616/A25153). Other grants that have supported this work include the UK Coronavirus Immunology Consortium (funder reference 1257927), the Imperial Biomedical Research Centre (NIHR Imperial BRC, grant IS-BRC-1215-20013), the Health Protection Research Unit in Respiratory Infections at Imperial College London and NIHR Health Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool, both in partnership with Public Health England, (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), Health Data Research UK (grant code 2021.0155), MRC (grant code MC_UU_12014/12) and NIHR Clinical Research Network for providing infrastructure support for this research. We also acknowledge the support of the MRC EMINENT Network (MR/R502121/1), which is cofunded by GSK, the Comprehensive Local Research Networks, the MRC HIC-Vac network (MR/R005982/1) and the RSV Consortium in Europe Horizon 2020 Framework Grant 116019. F.L. is supported by an MRC clinical training fellowship (award MR/W000970/1). C.E. is funded by NIHR (grant P91258-4). L.-P.H. is supported by Oxford NIHR Biomedical Research Centre. A.A.R.T. is supported by a British Heart Foundation (BHF) Intermediate Clinical Fellowship (FS/18/13/33281). S.L.R.-J. receives support from UK Research and Innovation (UKRI), Global Challenges Research Fund (GCRF), Rosetrees Trust, British HIV association (BHIVA), European & Developing Countries Clinical Trials Partnership (EDCTP) and Globvac. J.D.C. has grants from AstraZeneca, Boehringer Ingelheim, GSK, Gilead Sciences, Grifols, Novartis and Insmed. R.A.E. holds a NIHR Clinician Scientist Fellowship (CS-2016-16-020). A. Horsley is currently supported by UK Research and Innovation, NIHR and NIHR Manchester BRC. B.R. receives support from BHF Oxford Centre of Research Excellence, NIHR Oxford BRC and MRC. D.G.W. is supported by an NIHR Advanced Fellowship. A. Ho has received support from MRC and for the Coronavirus Immunology Consortium (MR/V028448/1). L.T. is supported by the US Food and Drug Administration Medical Countermeasures Initiative contract 75F40120C00085 and the National Institute for Health Research Health Protection Research Unit in Emerging and Zoonotic Infections (NIHR200907) at the University of Liverpool in partnership with UK Health Security Agency (UK-HSA), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford. L.V.W. has received support from UKRI, GSK/Asthma and Lung UK and NIHR for this study. M.G.S. has received support from NIHR UK, MRC UK and Health Protection Research Unit in Emerging and Zoonotic Infections, University of Liverpool. J.K.B. is supported by the Wellcome Trust (223164/Z/21/Z) and UKRI (MC_PC_20004, MC_PC_19025, MC_PC_1905, MRNO2995X/1 and MC_PC_20029). The funders were not involved in the study design, interpretation of data or writing of this manuscript. The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care (DHSC), the Department for International Development (DID), NIHR, MRC, the Wellcome Trust, UK-HSA, the National Health Service or the Department of Health. P.J.M.O. is supported by a NIHR Senior Investigator Award (award 201385). We thank all the participants and their families. We thank the many research administrators, health-care and social-care professionals who contributed to setting up and delivering the PHOSP-COVID study at all of the 65 NHS trusts/health boards and 25 research institutions across the United Kingdom, as well as those who contributed to setting up and delivering the ISARIC4C study at 305 NHS trusts/health boards. We also thank all the supporting staff at the NIHR Clinical Research Network, Health Research Authority, Research Ethics Committee, Department of Health and Social Care, Public Health Scotland and Public Health England. We thank K. Holmes at the NIHR Office for Clinical Research Infrastructure for her support in coordinating the charities group. The PHOSP-COVID industry framework was formed to provide advice and support in commercial discussions, and we thank the Association of the British Pharmaceutical Industry as well the NIHR Office for Clinical Research Infrastructure for coordinating this. We are very grateful to all the charities that have provided insight to the study: Action Pulmonary Fibrosis, Alzheimer’s Research UK, Asthma and Lung UK, British Heart Foundation, Diabetes UK, Cystic Fibrosis Trust, Kidney Research UK, MQ Mental Health, Muscular Dystrophy UK, Stroke Association Blood Cancer UK, McPin Foundations and Versus Arthritis. We thank the NIHR Leicester Biomedical Research Centre patient and public involvement group and Long Covid Support. We also thank G. Khandaker and D. C. Newcomb who provided valuable feedback on this work. Extended Data Fig. 10 was created using Biorender.

Author information

These authors contributed equally: Felicity Liew, Claudia Efstathiou, Ryan S. Thwaites, Peter J. M. Openshaw.

Authors and Affiliations

National Heart and Lung Institute, Imperial College London, London, UK

Felicity Liew, Claudia Efstathiou, Sara Fontanella, Dawid Swieboda, Jasmin K. Sidhu, Stephanie Ascough, Onn Min Kon, Luke S. Howard, Jennifer K. Quint, Christopher Chiu, Ryan S. Thwaites, Peter J. M. Openshaw, Jake Dunning & Peter J. M. Openshaw

Institute for Lung Health, Leicester NIHR Biomedical Research Centre, University of Leicester, Leicester, UK

Matthew Richardson, Ruth Saunders, Olivia C. Leavy, Omer Elneima, Hamish J. C. McAuley, Amisha Singapuri, Marco Sereno, Victoria C. Harris, Neil J. Greening, Rachael A. Evans, Louise V. Wain, Christopher Brightling & Ananga Singapuri

NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

Shona C. Moore, Daniel G. Wootton, Malcolm G. Semple, Lance Turtle, William A. Paxton & Georgios Pollakis

The Imperial Clinical Respiratory Research Unit, Imperial College NHS Trust, London, UK

Noura Mohamed

Cardiovascular Research Team, Imperial College Healthcare NHS Trust, London, UK

Jose Nunag & Clara King

Department of Population Health Sciences, University of Leicester, Leicester, UK

Olivia C. Leavy, Louise V. Wain & Beatriz Guillen-Guio

NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester, UK

Aarti Shikotra

Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University of Leicester, Leicester, UK

Linzy Houchen-Wolloff

Usher Institute, University of Edinburgh, Edinburgh, UK

Nazir I. Lone, Luke Daines, Annemarie B. Docherty, Nazir I. Lone, Matthew Thorpe, Annemarie B. Docherty, Thomas M. Drake, Cameron J. Fairfield, Ewen M. Harrison, Stephen R. Knight, Kenneth A. Mclean, Derek Murphy, Lisa Norman, Riinu Pius & Catherine A. Shaw

Centre for Medical Informatics, The Usher Institute, University of Edinburgh, Edinburgh, UK

Matthew Thorpe, Annemarie B. Docherty, Ewen M. Harrison, J. Kenneth Baillie, Sarah L. Rowland-Jones, A. A. Roger Thompson & Thushan de Silva

Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK

A. A. Roger Thompson, Sarah L. Rowland-Jones, Thushan I. de Silva & James D. Chalmers

University of Dundee, Ninewells Hospital and Medical School, Dundee, UK

James D. Chalmers & Ling-Pei Ho

MRC Human Immunology Unit, University of Oxford, Oxford, UK

Ling-Pei Ho & Alexander Horsley

Division of Infection, Immunity and Respiratory Medicine, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK

Alexander Horsley & Betty Raman

Radcliffe Department of Medicine, University of Oxford, Oxford, UK

Betty Raman & Krisnah Poinasamy

Asthma + Lung UK, London, UK

Krisnah Poinasamy & Michael Marks

Department of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK

Michael Marks

Hospital for Tropical Diseases, University College London Hospital, London, UK

Division of Infection and Immunity, University College London, London, UK

Michael Marks & Mahdad Noursadeghi

MRC Centre for Virus Research, School of Infection and Immunity, University of Glasgow, Glasgow, UK

Antonia Ho & William Greenhalf

Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

William Greenhalf & J. Kenneth Baillie

The Roslin Institute, University of Edinburgh, Edinburgh, UK

J. Kenneth Baillie, J. Kenneth Baillie, Sara Clohisey, Fiona Griffiths, Ross Hendry, Andrew Law & Wilna Oosthuyzen

Pandemic Science Hub, University of Edinburgh, Edinburgh, UK

J. Kenneth Baillie

The Pandemic Institute, University of Liverpool, Liverpool, UK

Malcolm G. Semple & Lance Turtle

University of Manchester, Manchester, UK

Kathryn Abel, Perdita Barran, H. Chinoy, Bill Deakin, M. Harvie, C. A. Miller, Stefan Stanel & Drupad Trivedi

Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, UK

Kathryn Abel & J. Kenneth Baillie

North Bristol NHS Trust and University of Bristol, Bristol, UK

H. Adamali, David Arnold, Shaney Barratt, A. Dipper, Sarah Dunn, Nick Maskell, Anna Morley, Leigh Morrison, Louise Stadon, Samuel Waterson & H. Welch

University of Edinburgh, Manchester, UK

Davies Adeloye, D. E. Newby, Riinu Pius, Igor Rudan, Manu Shankar-Hari, Catherine Sudlow, Sarah Walmsley & Bang Zheng

King’s College Hospital NHS Foundation Trust and King’s College London, London, UK

Oluwaseun Adeyemi, Rita Adrego, Hosanna Assefa-Kebede, Jonathon Breeze, S. Byrne, Pearl Dulawan, Amy Hoare, Caroline Jolley, Abigail Knighton, M. Malim, Sheetal Patale, Ida Peralta, Natassia Powell, Albert Ramos, K. Shevket, Fabio Speranza & Amelie Te

Guy’s and St Thomas’ NHS Foundation Trust, London, UK

Laura Aguilar Jimenez, Gill Arbane, Sarah Betts, Karen Bisnauthsing, A. Dewar, Nicholas Hart, G. Kaltsakas, Helen Kerslake, Murphy Magtoto, Philip Marino, L. M. Martinez, Marlies Ostermann, Jennifer Rossdale & Teresa Solano

Royal Free London NHS Foundation Trust, London, UK

Shanaz Ahmad, Simon Brill, John Hurst, Hannah Jarvis, C. Laing, Lai Lim, S. Mandal, Darwin Matila, Olaoluwa Olaosebikan & Claire Singh

University Hospital Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK

N. Ahmad Haider, Catherine Atkin, Rhiannon Baggott, Michelle Bates, A. Botkai, Anna Casey, B. Cooper, Joanne Dasgin, Camilla Dawson, Katharine Draxlbauer, N. Gautam, J. Hazeldine, T. Hiwot, Sophie Holden, Karen Isaacs, T. Jackson, Vicky Kamwa, D. Lewis, Janet Lord, S. Madathil, C. McGee, K. Mcgee, Aoife Neal, Alex Newton-Cox, Joseph Nyaboko, Dhruv Parekh, Z. Peterkin, H. Qureshi, Liz Ratcliffe, Elizabeth Sapey, J. Short, Tracy Soulsby, J. Stockley, Zehra Suleiman, Tamika Thompson, Maximina Ventura, Sinead Walder, Carly Welch, Daisy Wilson, S. Yasmin & Kay Por Yip

Stroke Association, London, UK

Rubina Ahmed & Richard Francis

University College London Hospital and University College London, London, UK

Nyarko Ahwireng, Dongchun Bang, Donna Basire, Jeremy Brown, Rachel Chambers, A. Checkley, R. Evans, M. Heightman, T. Hillman, Joseph Jacob, Roman Jastrub, M. Lipman, S. Logan, D. Lomas, Marta Merida Morillas, Hannah Plant, Joanna Porter, K. Roy & E. Wall

Oxford University Hospitals NHS Foundation Trust and University of Oxford, Oxford, UK

Mark Ainsworth, Asma Alamoudi, Angela Bloss, Penny Carter, M. Cassar, Jin Chen, Florence Conneh, T. Dong, Ranuromanana Evans, V. Ferreira, Emily Fraser, John Geddes, F. Gleeson, Paul Harrison, May Havinden-Williams, P. Jezzard, Ivan Koychev, Prathiba Kurupati, H. McShane, Clare Megson, Stefan Neubauer, Debby Nicoll, C. Nikolaidou, G. Ogg, Edmund Pacpaco, M. Pavlides, Yanchun Peng, Nayia Petousi, John Pimm, Najib Rahman, M. J. Rowland, Kathryn Saunders, Michael Sharpe, Nick Talbot, E. M. Tunnicliffe & C. Xie

St George’s University Hospitals NHS Foundation Trust, London, UK

Mariam Ali, Raminder Aul, A. Dunleavy, D. Forton, Mark Mencias, N. Msimanga, T. Samakomva, Sulman Siddique, Vera Tavoukjian & J. Teixeira

University Hospitals of Leicester NHS Trust and University of Leicester, Leicester, UK

M. Aljaroof, Natalie Armstrong, H. Arnold, Hnin Aung, Majda Bakali, M. Bakau, E. Baldry, Molly Baldwin, Charlotte Bourne, Michelle Bourne, Nigel Brunskill, P. Cairns, Liesel Carr, Amanda Charalambou, C. Christie, Melanie Davies, Enya Daynes, Sarah Diver, Rachael Dowling, Sarah Edwards, C. Edwardson, H. Evans, J. Finch, Sarah Glover, Nicola Goodman, Bibek Gooptu, Kate Hadley, Pranab Haldar, Beverley Hargadon, W. Ibrahim, L. Ingram, Kamlesh Khunti, A. Lea, D. Lee, Gerry McCann, P. McCourt, Teresa Mcnally, George Mills, Will Monteiro, Manish Pareek, S. Parker, Anne Prickett, I. N. Qureshi, A. Rowland, Richard Russell, Salman Siddiqui, Sally Singh, J. Skeemer, M. Soares, E. Stringer, T. Thornton, Martin Tobin, T. J. C. Ward, F. Woodhead, Tom Yates & A. J. Yousuf

University of Exeter, Exeter, UK

Louise Allan, Clive Ballard & Andrew McGovern

University of Leicester, Leicester, UK

Richard Allen, Michelle Bingham, Terry Brugha, Selina Finney, Rob Free, Don Jones, Claire Lawson, Daniel Lozano-Rojas, Gardiner Lucy, Alistair Moss, Elizabeta Mukaetova-Ladinska, Petr Novotny, Kimon Ntotsis, Charlotte Overton, John Pearl, Tatiana Plekhanova, M. Richardson, Nilesh Samani, Jack Sargant, Ruth Saunders, M. Sharma, Mike Steiner, Chris Taylor, Sarah Terry, C. Tong, E. Turner, J. Wormleighton & Bang Zhao

Liverpool University Hospitals NHS Foundation Trust and University of Liverpool, Liverpool, UK

Lisa Allerton, Ann Marie Allt, M. Beadsworth, Anthony Berridge, Jo Brown, Shirley Cooper, Andy Cross, Sylviane Defres, S. L. Dobson, Joanne Earley, N. French, Kera Hainey, Hayley Hardwick, Jenny Hawkes, Victoria Highett, Sabina Kaprowska, Angela Key, Lara Lavelle-Langham, N. Lewis-Burke, Gladys Madzamba, Flora Malein, Sophie Marsh, Chloe Mears, Lucy Melling, Matthew Noonan, L. Poll, James Pratt, Emma Richardson, Anna Rowe, Victoria Shaw, K. A. Tripp, Lilian Wajero, S. A. Williams-Howard, Dan Wootton & J. Wyles

Sherwood Forest Hospitals NHS Foundation Trust, Nottingham, UK

Lynne Allsop, Kaytie Bennett, Phil Buckley, Margaret Flynn, Mandy Gill, Camelia Goodwin, M. Greatorex, Heidi Gregory, Cheryl Heeley, Leah Holloway, Megan Holmes, John Hutchinson, Jill Kirk, Wayne Lovegrove, Terri Ann Sewell, Sarah Shelton, D. Sissons, Katie Slack, Susan Smith, D. Sowter, Sarah Turner, V. Whitworth & Inez Wynter

Nottingham University Hospitals NHS Trust and University of Nottingham, London, UK

Paula Almeida, Akram Hosseini, Robert Needham & Karen Shaw

Manchester University NHS Foundation Trust and University of Manchester, London, UK

Bashar Al-Sheklly, Cristina Avram, John Blaikely, M. Buch, N. Choudhury, David Faluyi, T. Felton, T. Gorsuch, Neil Hanley, Tracy Hussell, Zunaira Kausar, Natasha Odell, Rebecca Osbourne, Karen Piper Hanley, K. Radhakrishnan & Sue Stockdale

Imperial College London, London, UK

Danny Altmann, Anew Frankel, Luke S. Howard, Desmond Johnston, Liz Lightstone, Anne Lingford-Hughes, William Man, Steve McAdoo, Jane Mitchell, Philip Molyneaux, Christos Nicolaou, D. P. O’Regan, L. Price, Jennifer K. Quint, David Smith, Jonathon Valabhji, Simon Walsh, Martin Wilkins & Michelle Willicombe

Hampshire Hospitals NHS Foundation Trust, Basingstoke, UK

Maria Alvarez Corral, Ava Maria Arias, Emily Bevan, Denise Griffin, Jane Martin, J. Owen, Sheila Payne, A. Prabhu, Annabel Reed, Will Storrar, Nick Williams & Caroline Wrey Brown

British Heart Foundation, Birmingham, UK

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NHS Greater Glasgow and Clyde Health Board and University of Glasgow, Glasgow, UK

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University of Oxford, Oxford, UK

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A. Ashish, Josh Cooper & Emma Robinson

Leeds Teaching Hospitals and University of Leeds, Leeds, UK

Andrew Ashworth, Paul Beirne, Jude Clarke, C. Coupland, Matthhew Dalton, Clair Favager, Jodie Glossop, John Greenwood, Lucy Hall, Tim Hardy, Amy Humphries, Jennifer Murira, Dan Peckham, S. Plein, Jade Rangeley, Gwen Saalmink, Ai Lyn Tan, Elaine Wade, Beverley Whittam, Nicola Window & Janet Woods

University of Liverpool, Liverpool, UK

M. Ashworth, D. Cuthbertson, G. Kemp, Anne McArdle, Benedict Michael, Will Reynolds, Lisa Spencer, Ben Vinson, Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Nicola Carlucci, Emily Cass, Benjamin W. A. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Alejandra Doce Carracedo, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W. S. Fisher, Lisa Flaherty, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Karl Holden, Anthony Holmes, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A. Livoti, Maria Mancini, Hannah Massey, Nicole Maziere, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Ellen G. Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, P. Matthew Ridley, Debby Sales, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby van Tonder, Eve Wilcock & J. Eunice Zhang

University College London, London, UK

Shahab Aslani, Amita Banerjee, R. Batterham, Gabrielle Baxter, Robert Bell, Anthony David, Emma Denneny, Alun Hughes, W. Lilaonitkul, P. Mehta, Ashkan Pakzad, Bojidar Rangelov, B. Williams, James Willoughby & Moucheng Xu

Hull University Teaching Hospitals NHS Trust and University of Hull, Hull, UK

Paul Atkin, K. Brindle, Michael Crooks, Katie Drury, Nicholas Easom, Rachel Flockton, L. Holdsworth, A. Richards, D. L. Sykes, Susannah Thackray-Nocera & C. Wright

East Kent Hospitals University NHS Foundation Trust, Canterbury, UK

Liam Austin, Eva Beranova, Tracey Cosier, Joanne Deery, Tracy Hazelton, Carly Price, Hazel Ramos, Reanne Solly, Sharon Turney & Heather Weston

Baillie Gifford Pandemic Science Hub, Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Nikos Avramidis, J. Kenneth Baillie, Erola Pairo-Castineira & Konrad Rawlik

Roslin Institute, University of Edinburgh, Edinburgh, UK

Nikos Avramidis, J. Kenneth Baillie & Erola Pairo-Castineira

Newcastle upon Tyne Hospitals NHS Foundation Trust and University of Newcastle, Newcastle upon Tyne, UK

A. Ayoub, J. Brown, G. Burns, Gareth Davies, Anthony De Soyza, Carlos Echevarria, Helen Fisher, C. Francis, Alan Greenhalgh, Philip Hogarth, Joan Hughes, Kasim Jiwa, G. Jones, G. MacGowan, D. Price, Avan Sayer, John Simpson, H. Tedd, S. Thomas, Sophie West, M. Witham, S. Wright & A. Young

East Cheshire NHS Trust, Macclesfield, UK

Marta Babores, Maureen Holland, Natalie Keenan, Sharlene Shashaa & Helen Wassall

Sheffield Teaching NHS Foundation Trust and University of Sheffield, Sheffield, UK

J. Bagshaw, M. Begum, K. Birchall, Robyn Butcher, H. Carborn, Flora Chan, Kerry Chapman, Yutung Cheng, Luke Chetham, Cameron Clark, Zach Coburn, Joby Cole, Myles Dixon, Alexandra Fairman, J. Finnigan, H. Foot, David Foote, Amber Ford, Rebecca Gregory, Kate Harrington, L. Haslam, L. Hesselden, J. Hockridge, Ailsa Holbourn, B. Holroyd-Hind, L. Holt, Alice Howell, E. Hurditch, F. Ilyas, Claire Jarman, Allan Lawrie, Ju Hee Lee, Elvina Lee, Rebecca Lenagh, Alison Lye, Irene Macharia, M. Marshall, Angeline Mbuyisa, J. McNeill, Sharon Megson, J. Meiring, L. Milner, S. Misra, Helen Newell, Tom Newman, C. Norman, Lorenza Nwafor, Dibya Pattenadk, Megan Plowright, Julie Porter, Phillip Ravencroft, C. Roddis, J. Rodger, Peter Saunders, J. Sidebottom, Jacqui Smith, Laurie Smith, N. Steele, G. Stephens, R. Stimpson, B. Thamu, N. Tinker, Kim Turner, Helena Turton, Phillip Wade, S. Walker, James Watson, Imogen Wilson & Amira Zawia

University of Nottingham, Nottingham, UK

David Baguley, Chris Coleman, E. Cox, Laura Fabbri, Susan Francis, Ian Hall, E. Hufton, Simon Johnson, Fasih Khan, Paaig Kitterick, Richard Morriss, Nick Selby, Iain Stewart & Louise Wright

Wirral University Teaching Hospital, Wirral, UK

Elisabeth Bailey, Anne Reddington & Andrew Wight

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Edinburgh, UK

University of Swansea, Swansea, UK

University of Southampton, London, UK

David Baldwin, P. C. Calder, Nathan Huneke & Gemma Simons

Royal Brompton and Harefield Clinical Group, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

R. E. Barker, Daniele Cristiano, N. Dormand, P. George, Mahitha Gummadi, S. Kon, Kamal Liyanage, C. M. Nolan, B. Patel, Suhani Patel, Oliver Polgar, L. Price, P. Shah, Suver Singh & J. A. Walsh

York and Scarborough NHS Foundation Trust, York, UK

Laura Barman, Claire Brookes, K. Elliott, L. Griffiths, Zoe Guy, Kate Howard, Diana Ionita, Heidi Redfearn, Carol Sarginson & Alison Turnbull

NHS Highland, Inverness, UK

Fiona Barrett, A. Donaldson & Beth Sage

Royal Papworth Hospital NHS Foundation Trust, Cambridge, UK

Helen Baxendale, Lucie Garner, C. Johnson, J. Mackie, Alice Michael, J. Newman, Jamie Pack, K. Paques, H. Parfrey, J. Parmar & A. Reddy

University Hospitals of Derby and Burton, Derby, UK

Paul Beckett, Caroline Dickens & Uttam Nanda

NHS Lanarkshire, Hamilton, UK

Murdina Bell, Angela Brown, M. Brown, R. Hamil, Karen Leitch, L. Macliver, Manish Patel, Jackie Quigley, Andrew Smith & B. Welsh

Cambridge University Hospitals NHS Foundation Trust, NIHR Cambridge Clinical Research Facility and University of Cambridge, Cambridge, UK

Areti Bermperi, Isabel Cruz, K. Dempsey, Anne Elmer, Jonathon Fuld, H. Jones, Sherly Jose, Stefan Marciniak, M. Parkes, Carla Ribeiro, Jessica Taylor, Mark Toshner, L. Watson & J. Worsley

Loughborough University, Loughborough, UK

Lettie Bishop & David Stensel

Betsi Cadwallader University Health Board, Bangor, UK

Annette Bolger, Ffyon Davies, Ahmed Haggar, Joanne Lewis, Arwel Lloyd, R. Manley, Emma McIvor, Daniel Menzies, K. Roberts, W. Saxon, David Southern, Christian Subbe & Victoria Whitehead

Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK

Charlotte Bolton, J. Bonnington, Melanie Chrystal, Catherine Dupont, Paul Greenhaff, Ayushman Gupta, W. Jang, S. Linford, Laura Matthews, Athanasios Nikolaidis, Sabrina Prosper & Andrew Thomas

King’s College London, London, UK

Kate Bramham, M. Brown, Khalida Ismail, Tim Nicholson, Carmen Pariante, Claire Sharpe, Simon Wessely & J. Whitney

Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK

Lucy Brear, Karen Regan, Dinesh Saralaya & Kim Storton

South London and Maudsley NHS Foundation Trust and King’s College London, London, UK

G. Breen & M. Hotopf

London School of Hygiene and Tropical Medicine, London, UK

Andrew Briggs

Whittington Health NHS Trust, London, UK

E. Bright, P. Crisp, Ruvini Dharmagunawardena & M. Stern

Cardiff and Vale University Health Board, Cardiff, UK

Lauren Broad, Teriann Evans, Matthew Haynes, L. Jones, Lucy Knibbs, Alison McQueen, Catherine Oliver, Kerry Paradowski, Ramsey Sabit & Jenny Williams

Yeovil District Hospital NHS Foundation Trust, Yeovil, UK

Andrew Broadley

University of Birmingham, Birmingham, UK

Mattew Broome, Paul McArdle, Paul Moss, David Thickett, Rachel Upthegrove, Dan Wilkinson, David Wraith & Erin L. Aldera

BHF Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK

Anda Bularga

University of Cambridge, Cambridge, UK

Ed Bullmore, Jonathon Heeney, Claudia Langenberg, William Schwaeble, Charlotte Summers & J. Weir McCall

NIHR Leicester Biomedical Research Centre–Respiratory Patient and Public Involvement Group, Leicester, UK

Jenny Bunker, Rhyan Gill & Rashmita Nathu

Imperial College Healthcare NHS Trust and Imperial College London, London, UK

L. Burden, Ellen Calvelo, Bethany Card, Caitlin Carr, Edwin Chilvers, Donna Copeland, P. Cullinan, Patrick Daly, Lynsey Evison, Tamanah Fayzan, Hussain Gordon, Sulaimaan Haq, Gisli Jenkins, Clara King, Onn Min Kon, Katherine March, Myril Mariveles, Laura McLeavey, Silvia Moriera, Unber Munawar, Uchechi Nwanguma, Lorna Orriss-Dib, Alexandra Ross, Maura Roy, Emily Russell, Katherine Samuel, J. Schronce, Neil Simpson, Lawrence Tarusan, David Thomas, Chloe Wood & Najira Yasmin

Harrogate and District NHD Foundation Trust, Harrogate, UK

Tracy Burdett, James Featherstone, Cathy Lawson, Alison Layton, Clare Mills & Lorraine Stephenson

Newcastle University/Chair of NIHR Dementia TRC, Newcastle, UK

Oxford University Hospitals NHS Foundation Trust, Oxford, UK

A. Burns & N. Kanellakis

Tameside and Glossop Integrated Care NHS Foundation Trust, Ashton-under-Lyne, UK

Al-Tahoor Butt, Martina Coulding, Heather Jones, Susan Kilroy, Jacqueline McCormick, Jerome McIntosh, Heather Savill, Victoria Turner & Joanne Vere

University of Oxford, Nuffield Department of Medicine, Oxford, UK

University of Glasgow, Glasgow, UK

Jonathon Cavanagh, S. MacDonald, Kate O’Donnell, John Petrie, Naveed Sattar & Mark Spears

United Lincolnshire Hospitals NHS Trust, Grantham, UK

Manish Chablani & Lynn Osborne

Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Trudie Chalder

University Hospital of South Manchester NHS Foundation Trust, Manchester, UK

N. Chaudhuri

University Hospital Southampton NHS Foundation Trust and University of Southampton, Southampton, UK

Caroline Childs, R. Djukanovic, S. Fletcher, Matt Harvey, Mark Jones, Elizabeth Marouzet, B. Marshall, Reena Samuel, T. Sass, Tim Wallis & Helen Wheeler

King’s College Hospital/Guy’s and St Thomas’ NHS FT, London, UK

A. Chiribiri & C. O’Brien

Barts Health NHS Trust, London, UK

K. Chong-James, C. David, W. Y. James, Paul Pfeffer & O. Zongo

NHS Lothian and University of Edinburgh, Edinburgh, UK

Gaunab Choudhury, S. Clohisey, Andrew Deans, J. Furniss, Ewen Harrison, S. Kelly & Aziz Sheikh

School of Cardiovascular Medicine and Sciences. King’s College London, London, UK

Phillip Chowienczyk

Lewisham and Greenwich NHS Trust, London, UK

Hywel Dda University Health Board, Haverfordwest, UK

S. Coetzee, Kim Davies, Rachel Ann Hughes, Ronda Loosley, Heather McGuinness, Abdelrahman Mohamed, Linda O’Brien, Zohra Omar, Emma Perkins, Janet Phipps, Gavin Ross, Abigail Taylor, Helen Tench & Rebecca Wolf-Roberts

NHS Tayside and University of Dundee, Dundee, UK

David Connell, C. Deas, Anne Elliott, J. George, S. Mohammed, J. Rowland, A. R. Solstice, Debbie Sutherland & Caroline Tee

Swansea Bay University Health Board, Port Talbot, UK

Lynda Connor, Amanda Cook, Gwyneth Davies, Tabitha Rees, Favas Thaivalappil & Caradog Thomas

Faculty of Medicine, Nursing and Health Sciences, School of Biomedical Sciences, Monash University, Melbourne, Victoria, Australia

Eamon Coughlan

Rotherham NHS Foundation Trust, Rotherham, UK

Alison Daniels, Anil Hormis, Julie Ingham & Lisa Zeidan

Salford Royal NHS Foundation Trust, Salford, UK

P. Dark, Nawar Diar-Bakerly, D. Evans, E. Hardy, Alice Harvey, D. Holgate, Sean Knight, N. Mairs, N. Majeed, L. McMorrow, J. Oxton, Jessica Pendlebury, C. Summersgill, R. Ugwuoke & S. Whittaker

Cwm Taf Morgannwg University Health Board, Mountain Ash, UK

Ellie Davies, Cerys Evenden, Alyson Hancock, Kia Hancock, Ceri Lynch, Meryl Rees, Lisa Roche, Natalie Stroud & T. Thomas-Woods

Borders General Hospital, NHS Borders, Melrose, UK

Joy Dawson, Hosni El-Taweel & Leanne Robinson

Aneurin Bevan University Health Board, Caerleon, UK

Amanda Dell, Sara Fairbairn, Nancy Hawkings, Jill Haworth, Michaela Hoare, Victoria Lewis, Alice Lucey, Georgia Mallison, Heeah Nassa, Chris Pennington, Andrea Price, Claire Price, Andrew Storrie, Gemma Willis & Susan Young

University of Exeter Medical School, Exeter, UK

London North West University Healthcare NHS Trust, London, UK

Shalin Diwanji, Sambasivarao Gurram, Padmasayee Papineni, Sheena Quaid, Gerlynn Tiongson & Ekaterina Watson

Alzheimer’s Research UK, Cambridge, UK

Hannah Dobson

Health and Care Research Wales, Cardiff, UK

Yvette Ellis

University of Bristol, Bristol, UK

Jonathon Evans

University of Sheffield, Sheffield, UK

L. Finnigan, Laura Saunders & James Wild

Great Western Hospital Foundation Trust, Swindon, UK

Eva Fraile & Jacinta Ugoji

Royal Devon and Exeter NHS Trust, Barnstaple, UK

Michael Gibbons

Kettering General Hospital NHS Trust, Kettering, UK

Anne-Marie Guerdette, Melanie Hewitt, R. Reddy, Katie Warwick & Sonia White

NIHR Leicester Biomedical Research Centre, Leicester, UK

Beatriz Guillen-Guio

University of Leeds, Leeds, UK

Elspeth Guthrie & Max Henderson

Royal Surrey NHS Foundation Trust, Cranleigh, UK

Mark Halling-Brown & Katherine McCullough

Chesterfield Royal Hospital NHS Trust, Calow, UK

Edward Harris & Claire Sampson

Long Covid Support, London, UK

Claire Hastie, Natalie Rogers & Nikki Smith

King’s College Hospital, NHS Foundation Trust and King’s College London, London, UK

Department of Oncology and Metabolism, University of Sheffield, Sheffield, UK

Simon Heller

NIHR Office for Clinical Research Infrastructure, London, UK

Katie Holmes

Asthma UK and British Lung Foundation Partnership, London, UK

Ian Jarrold & Samantha Walker

North Middlesex University Hospital NHS Trust, London, UK

Bhagy Jayaraman & Tessa Light

Action for Pulmonary Fibrosis, Peterborough, UK

Cardiff University, National Centre for Mental Health, Cardiff, UK

McPin Foundation, London, UK

Thomas Kabir

Roslin Institute, The University of Edinburgh, Edinburgh, UK

Steven Kerr

The Hillingdon Hospitals NHS Foundation Trust, London, UK

Samantha Kon, G. Landers, Harpreet Lota, Mariam Nasseri & Sofiya Portukhay

Queen Mary University of London, London, UK

Ania Korszun

Swansea University, Swansea Welsh Network, Hywel Dda University Health Board, Swansea, UK

Royal Infirmary of Edinburgh, NHS Lothian, Edinburgh, UK

Nazir I. Lone

Barts Heart Centre, London, UK

Barts Health NHS Trust and Queen Mary University of London, London, UK

Adrian Martineau

Salisbury NHS Foundation Trust, Salisbury, UK

Wadzanai Matimba-Mupaya & Sophia Strong-Sheldrake

University of Newcastle, Newcastle, UK

Hamish McAllister-Williams, Stella-Maria Paddick, Anthony Rostron & John Paul Taylor

Gateshead NHS Trust, Gateshead, UK

W. McCormick, Lorraine Pearce, S. Pugmire, Wendy Stoker & Ann Wilson

Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester, UK

Katherine McIvor

Kidney Research UK, Peterborough, UK

Aisling McMahon

NHS Dumfries and Galloway, Dumfries, UK

Michael McMahon & Paula Neill

Swansea University, Swansea, UK

MQ Mental Health Research, London, UK

Lea Milligan

BHF Centre for Cardiovascular Science, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK

Nicholas Mills

Shropshire Community Health NHS Trust, Shropshire, UK

Sharon Painter, Johanne Tomlinson & Louise Warburton

Somerset NHS Foundation Trust, Taunton, UK

Sue Palmer, Dawn Redwood, Jo Tilley, Carinna Vickers & Tania Wainwright

Francis Crick Institute, London, UK

Markus Ralser

Manchester University NHD Foundation Trust, Manchester, UK

Pilar Rivera-Ortega

Diabetes UK, University of Glasgow, Glasgow, UK

Elizabeth Robertson

Barnsley Hospital NHS Foundation Trust, Barnsley, UK

Amy Sanderson

MRC–University of Glasgow Centre for Virus Research, Glasgow, UK

Janet Scott

Diabetes UK, London, UK

Kamini Shah

British Heart Foundation Centre, King’s College London, London, UK

King’s College Hospital NHS Foundation Trust, London, UK

University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK

Institute of Cardiovascular and Medical Sciences, BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, UK

University College London NHS Foundation Trust, London and Barts Health NHS Trust, London, UK

Northumbria University, Newcastle upon Tyne, UK

Ioannis Vogiatzis

Swansea University and Swansea Welsh Network, Swansea, UK

N. Williams

DUK | NHS Digital, Salford Royal Foundation Trust, Salford, UK

Queen Alexandra Hospital, Portsmouth, UK

• Kayode Adeniji

Princess Royal Hospital, Haywards Heath, UK

Daniel Agranoff & Chi Eziefula

Bassetlaw Hospital, Bassetlaw, UK

Darent Valley Hospital, Dartford, UK

Queen Elizabeth the Queen Mother Hospital, Margate, UK

Ana Alegria

School of Informatics, University of Edinburgh, Edinburgh, UK

Beatrice Alex, Benjamin Bach & James Scott-Brown

North East and North Cumbria Ingerated, Newcastle upon Tyne, UK

Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Petros Andrikopoulos, Kanta Chechi, Marc-Emmanuel Dumas, Julian Griffin, Sonia Liggi & Zoltan Takats

Section of Genomic and Environmental Medicine, Respiratory Division, National Heart and Lung Institute, Imperial College London, London, UK

Petros Andrikopoulos, Marc-Emmanuel Dumas, Michael Olanipekun & Anthonia Osagie

John Radcliffe Hospital, Oxford, UK

Brian Angus

Royal Albert Edward Infirmary, Wigan, UK

Abdul Ashish

Manchester Royal Infirmary, Manchester, UK

Dougal Atkinson

MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Western General Hospital, Crewe Road, Edinburgh, UK

Section of Molecular Virology, Imperial College London, London, UK

Wendy S. Barclay

Furness General Hospital, Barrow-in-Furness, UK

Shahedal Bari

Hull University Teaching Hospital Trust, Kingston upon Hull, UK

Gavin Barlow

Hillingdon Hospital, Hillingdon, UK

Stella Barnass

St Thomas’ Hospital, London, UK

Nicholas Barrett

Coventry and Warwickshire, Coventry, UK

Christopher Bassford

St Michael’s Hospital, Bristol, UK

Sneha Basude

Stepping Hill Hospital, Stockport, UK

David Baxter

Royal Liverpool University Hospital, Liverpool, UK

Michael Beadsworth

Bristol Royal Hospital Children’s, Bristol, UK

Jolanta Bernatoniene

Scarborough Hospital, Scarborough, UK

John Berridge

Golden Jubilee National Hospital, Clydebank, UK

Colin Berry

Liverpool Heart and Chest Hospital, Liverpool, UK

Nicola Best

Centre for Inflammation Research, The Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK

Debby Bogaert & Clark D. Russell

James Paget University Hospital, Great Yarmouth, UK

Pieter Bothma & Darell Tupper-Carey

Aberdeen Royal Infirmary, Aberdeen, UK

Robin Brittain-Long

Adamson Hospital, Cupar, UK

Naomi Bulteel

Royal Devon and Exeter Hospital, Exeter, UK

Worcestershire Royal Hospital, Worcester, UK

Andrew Burtenshaw

ISARIC Global Support Centre, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Gail Carson, Laura Merson & Louise Sigfrid

Conquest Hospital, Hastings, UK

Vikki Caruth

The James Cook University Hospital, Middlesbrough, UK

David Chadwick

Dorset County Hospital, Dorchester, UK

Duncan Chambler

Antimicrobial Resistance and Hospital Acquired Infection Department, Public Health England, London, UK

Meera Chand

Department of Epidemiology and Biostatistics, School of Public Health, Faculty of Medicine, Imperial College London, London, UK

Kanta Chechi

Royal Bournemouth General Hospital, Bournemouth, UK

Harrogate Hospital, Harrogate, UK

Jenny Child

Royal Blackburn Teaching Hospital, Blackburn, UK

Srikanth Chukkambotla

Edinburgh Clinical Research Facility, University of Edinburgh, Edinburgh, UK

Richard Clark, Audrey Coutts, Lorna Donelly, Angie Fawkes, Tammy Gilchrist, Katarzyna Hafezi, Louise MacGillivray, Alan Maclean, Sarah McCafferty, Kirstie Morrice, Lee Murphy & Nicola Wrobel

Torbay Hospital, Torquay, UK

Northern General Hospital, Sheffield, UK

Paul Collini, Cariad Evans & Gary Mills

Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK

Marie Connor, Jo Dalton, Chloe Donohue, Carrol Gamble, Michelle Girvan, Sophie Halpin, Janet Harrison, Clare Jackson, Laura Marsh, Stephanie Roberts & Egle Saviciute

Department of Infectious Disease, Imperial College London, London, UK

Graham S. Cooke & Shiranee Sriskandan

St Georges Hospital (Tooting), London, UK

Catherine Cosgrove

Blackpool Victoria Hospital, Blackpool, UK

Jason Cupitt & Joanne Howard

The Royal London Hospital, London, UK

Maria-Teresa Cutino-Moguel

MRC-University of Glasgow Centre for Virus Research, Glasgow, UK

Ana da Silva Filipe, Antonia Y. W. Ho, Sarah E. McDonald, Massimo Palmarini, David L. Robertson, Janet T. Scott & Emma C. Thomson

Salford Royal Hospital, Salford, UK

University Hospital of North Durham, Durham, UK

Chris Dawson

Norfolk and Norwich University Hospital, Norwich, UK

Samir Dervisevic

Intensive Care Unit, Royal Infirmary Edinburgh, Edinburgh, UK

Annemarie B. Docherty & Seán Keating

Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK

Cara Donegan & Rebecca G. Spencer

Salisbury District Hospital, Salisbury, UK

Phil Donnison

National Phenome Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Gonçalo dos Santos Correia, Matthew Lewis, Lynn Maslen, Caroline Sands, Zoltan Takats & Panteleimon Takis

Section of Bioanalytical Chemistry, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Gonçalo dos Santos Correia, Matthew Lewis, Lynn Maslen, Caroline Sands & Panteleimon Takis

Guy’s and St Thomas’, NHS Foundation Trust, London, UK

Sam Douthwaite, Michael MacMahon, Marlies Ostermann & Manu Shankar-Hari

The Royal Oldham Hospital, Oldham, UK

Andrew Drummond

European Genomic Institute for Diabetes, Institut Pasteur de Lille, Lille University Hospital, University of Lille, Lille, France

Marc-Emmanuel Dumas

McGill University and Genome Quebec Innovation Centre, Montreal, Qeubec, Canada

National Infection Service, Public Health England, London, UK

Jake Dunning & Maria Zambon

Hereford Count Hospital, Hereford, UK

Ingrid DuRand

Southampton General Hospital, Southampton, UK

Ahilanadan Dushianthan

Northampton General Hospital, Northampton, UK

Tristan Dyer

University Hospital of Wales, Cardiff, UK

Chrisopher Fegan

University Hospitals Bristol NHS Foundation Trust, Bristol, UK

Liverpool School of Tropical Medicine, Liverpool, UK

Tom Fletcher

Leighton Hospital, Crewe, UK

Duncan Fullerton & Elijah Matovu

Manor Hospital, Walsall, UK

Scunthorpe Hospital, Scunthorpe, UK

Sanjeev Garg

Cambridge University Hospital, Cambridge, UK

Effrossyni Gkrania-Klotsas

West Suffolk NHS Foundation Trust, Bury St Edmunds, UK

Basingstoke and North Hampshire Hospital, Basingstoke, UK

Arthur Goldsmith

North Cumberland Infirmary, Carlisle, UK

Clive Graham

Paediatric Liver, GI and Nutrition Centre and MowatLabs, King’s College Hospital, London, UK

Tassos Grammatikopoulos

Institute of Liver Studies, King’s College London, London, UK

Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK

Christopher A. Green

Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Liverpool, UK

William Greenhalf

Institute for Global Health, University College London, London, UK

Rishi K. Gupta

NIHR Health Protection Research Unit, Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, UK

Hayley Hardwick, Malcolm G. Semple, Tom Solomon & Lance C. W. Turtle

Warwick Hospital, Warwick, UK

Elaine Hardy

Birmingham Children’s Hospital, Birmingham, UK

Stuart Hartshorn

Nottingham City Hospital, Nottingham, UK

Daniel Harvey

Glangwili Hospital Child Health Section, Carmarthen, UK

Peter Havalda

Alder Hey Children’s Hospital, Liverpool, UK

Daniel B. Hawcutt

Department of Infectious Diseases, Queen Elizabeth University Hospital, Glasgow, UK

Antonia Y. W. Ho

Bronglais General Hospital, Aberystwyth, UK

Maria Hobrok

Worthing Hospital, Worthing, UK

Luke Hodgson

Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Peter W. Horby

Rotheram District General Hospital, Rotheram, UK

Anil Hormis

Virology Reference Department, National Infection Service, Public Health England, Colindale Avenue, London, UK

Samreen Ijaz

Royal Free Hospital, London, UK

Michael Jacobs & Padmasayee Papineni

Homerton Hospital, London, UK

Airedale Hospital, Airedale, UK

Paul Jennings

Basildon Hospital, Basildon, UK

Agilan Kaliappan

The Christie NHS Foundation Trust, Manchester, UK

Vidya Kasipandian

University Hospital Lewisham, London, UK

Stephen Kegg

The Whittington Hospital, London, UK

Michael Kelsey

Southmead Hospital, Bristol, UK

Jason Kendall

Sheffield Childrens Hospital, Sheffield, UK

Caroline Kerrison

Royal United Hospital, Bath, UK

Ian Kerslake

Department of Pharmacology, University of Liverpool, Liverpool, UK

Nuffield Department of Medicine, Peter Medawar Building for Pathogen Research, University of Oxford, Oxford, UK

Paul Klenerman

Translational Gastroenterology Unit, Nuffield Department of Medicine, University of Oxford, Oxford, UK

Public Health Scotland, Edinburgh, UK

Susan Knight, Eva Lahnsteiner & Sarah Tait

Western General Hospital, Edinburgh, UK

Oliver Koch

Southend University Hospital NHS Foundation Trust, Southend-on-Sea, UK

Gouri Koduri

Hinchingbrooke Hospital, Huntingdon, UK

George Koshy & Tamas Leiner

Royal Preston Hospital, Fulwood, UK

Shondipon Laha

University Hospital (Coventry), Coventry, UK

Steven Laird

The Walton Centre, Liverpool, UK

Susan Larkin

ISARIC, Global Support Centre, COVID-19 Clinical Research Resources, Epidemic diseases Research Group, Oxford (ERGO), University of Oxford, Oxford, UK

James Lee & Daniel Plotkin

Centre for Health Informatics, Division of Informatics, Imaging and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK

Gary Leeming

Hull Royal Infirmary, Hull, UK

Patrick Lillie

Nottingham University Hospitals NHS Trust:, Nottingham, UK

Wei Shen Lim

Darlington Memorial Hospital, Darlington, UK

Queen Elizabeth Hospital (Gateshead), Gateshead, UK

Vanessa Linnett

Warrington Hospital, Warrington, UK

Jeff Little

Bristol Royal Hospital for Children, Bristol, UK

Mark Lyttle

St Mary’s Hospital (Isle of Wight), Isle of Wight, UK

Emily MacNaughton

The Tunbridge Wells Hospital, Royal Tunbridge Wells, UK

Ravish Mankregod

Huddersfield Royal, Huddersfield, UK

Countess of Chester Hospital, Liverpool, UK

Ruth McEwen & Lawrence Wilson

Frimley Park Hospital, Frimley, UK

Manjula Meda

Nuffield Department of Medicine, John Radcliffe Hospital, Oxford, UK

Alexander J. Mentzer

Department of Microbiology/Infectious Diseases, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

Alison M. Meynert & Murray Wham

St James University Hospital, Leeds, UK

Jane Minton

Arrowe Park Hospital, Birkenhead, UK

Kavya Mohandas

Great Ormond Street Hospital, London, UK

Royal Shrewsbury Hospital, Shrewsbury, UK

Addenbrookes Hospital, Cambridge, UK

Elinoor Moore

Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK

Shona C. Moore, William A. Paxton & Georgios Pollakis

East Surrey Hospital (Redhill), Redhill, UK

Patrick Morgan

Burton Hospital, Burton, UK

Craig Morris & Tim Reynolds

Peterborough City Hospital, Peterborough, UK

Katherine Mortimore

Kent and Canterbury Hospital, Canterbury, UK

Samuel Moses

Weston Area General Trust, Bristol, UK

Mbiye Mpenge

Bedfordshire Hospital, Bedfordshire, UK

Rohinton Mulla

Glasgow Royal Infirmary, Glasgow, UK

Michael Murphy

Macclesfield General Hospital, Macclesfield, UK

Thapas Nagarajan

Derbyshire Healthcare, Derbyshire, UK

Megan Nagel

Chelsea and Westminster Hospital, London, UK

Mark Nelson & Matthew K. O’Shea

Watford General Hospital, Watford, UK

Lillian Norris & Tom Stambach

EPCC, University of Edinburgh, Edinburgh, UK

Lucy Norris

Section of Biomolecular Medicine, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, London, UK

Michael Olanipekun

Imperial College Healthcare NHS Trust: London, London, UK

Peter J. M. Openshaw

Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK

Anthonia Osagie

Prince Philip Hospital, Llanelli, UK

Igor Otahal & Andrew Workman

George Eliot Hospital – Acute Services, Nuneaton, UK

Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

Carlo Palmieri

Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK

Kettering General Hospital, Kettering, UK

Selva Panchatsharam

University Hospitals of North Midlands NHS Trust, North Midlands, UK

Danai Papakonstantinou

Russells Hall Hospital, Dudley, UK

Hassan Paraiso

Harefield Hospital, Harefield, UK

Lister Hospital, Lister, UK

Natalie Pattison

Musgrove Park Hospital, Taunton, UK

Justin Pepperell

Kingston Hospital, Kingston, UK

Mark Peters

Queen’s Hospital, Romford, UK

Mandeep Phull

Southport and Formby District General Hospital, Southport, UK

Stefania Pintus

St George’s University of London, London, UK

Tim Planche

King’s College Hospital (Denmark Hill), London, UK

Centre for Clinical Infection and Diagnostics Research, Department of Infectious Diseases, School of Immunology and Microbial Sciences, King’s College London, London, UK

Nicholas Price

Department of Infectious Diseases, Guy’s and St Thomas’ NHS Foundation Trust, London, UK

The Clatterbridge Cancer Centre NHS Foundation, Bebington, UK

David Price

The Great Western Hospital, Swindon, UK

Rachel Prout

Ninewells Hospital, Dundee, UK

Nikolas Rae

Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK

Andrew Rambaut

Poole Hospital NHS Trust, Poole, UK

Henrik Reschreiter

William Harvey Hospital, Ashford, UK

Neil Richardson

King’s Mill Hospital, Sutton-in-Ashfield, UK

Mark Roberts

Liverpool Women’s Hospital, Liverpool, UK

Devender Roberts

Pinderfields Hospital, Wakefield, UK

Alistair Rose

North Devon District Hospital, Barnstaple, UK

Guy Rousseau

Queen Elizabeth Hospital, Birmingham, UK

Tameside General Hospital, Ashton-under-Lyne, UK

Brendan Ryan

City Hospital (Birmingham), Birmingham, UK

Taranprit Saluja

Department of Pediatrics and Virology, St Mary’s Medical School Bldg, Imperial College London, London, UK

Vanessa Sancho-Shimizu

The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, UK

Matthias Schmid

NHS Greater Glasgow and Clyde, Glasgow, UK

Janet T. Scott

Respiratory Medicine, Institute in The Park, University of Liverpool, Alder Hey Children’s Hospital, Liverpool, UK

Malcolm G. Semple

Broomfield Hospital, Broomfield, UK

Stoke Mandeville, UK

Prad Shanmuga

University Hospital of North Tees, Stockton-on-Tees, UK

Anil Sharma

Institute of Translational Medicine, University of, Liverpool, Merseyside, UK

Victoria E. Shaw

Royal Manchester Children’s Hospital, Manchester, UK

Anna Shawcross

New Cross Hospital, Wolverhampton, UK

Jagtur Singh Pooni

Bedford Hospital, Bedford, UK

Jeremy Sizer

Colchester General Hospital, Colchester, UK

Richard Smith

University Hospital Birmingham NHS Foundation Trust, Birmingham, UK

Catherine Snelson & Tony Whitehouse

Walton Centre NHS Foundation Trust, Liverpool, UK

Tom Solomon

Chesterfield Royal Hospital, Calow, UK

Nick Spittle

MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK

Shiranee Sriskandan

Princess Alexandra Hospital, Harlow, UK

Nikki Staines & Shico Visuvanathan

Milton Keynes Hospital, Eaglestone, UK

Richard Stewart

Division of Structural Biology, The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

David Stuart

Royal Bolton Hopital, Farnworth, UK

Pradeep Subudhi

Department of Medicine, University of Cambridge, Cambridge, UK

Charlotte Summers

Department of Child Life and Health, University of Edinburgh, Edinburgh, UK

Olivia V. Swann

Royal Gwent (Newport), Newport, UK

Tamas Szakmany

The Royal Marsden Hospital (London), London, UK

Kate Tatham

Blood Borne Virus Unit, Virus Reference Department, National Infection Service, Public Health England, London, UK

Richard S. Tedder

Transfusion Microbiology, National Health Service Blood and Transplant, London, UK

Department of Medicine, Imperial College London, London, UK

Queen Victoria Hospital (East Grinstead), East Grinstead, UK

Leeds Teaching Hospitals NHS Trust, Leeds, UK

Robert Thompson

Royal Stoke University Hospital, Stoke-on-Trent, UK

Chris Thompson

Whiston Hospital, Rainhill, UK

Ascanio Tridente

Tropical and Infectious Disease Unit, Royal Liverpool University Hospital, Liverpool, UK

Lance C. W. Turtle

Croydon University Hospital, Thornton Heath, UK

Mary Twagira

Gloucester Royal, Gloucester, UK

Nick Vallotton

West Hertfordshire Teaching Hospitals NHS Trust, Hertfordshire, UK

Rama Vancheeswaran

North Middlesex Hospital, London, UK

Rachel Vincent

Medway Maritime Hospital, Gillingham, UK

Lisa Vincent-Smith

Royal Papworth Hospital Everard, Cambridge, UK

Alan Vuylsteke

Derriford (Plymouth), Plymouth, UK

St Helier Hospital, Sutton, UK

Rachel Wake

Royal Berkshire Hospital, Reading, UK

Andrew Walden

Royal Liverpool Hospital, Liverpool, UK

Ingeborg Welters

Bradford Royal infirmary, Bradford, UK

Paul Whittaker

Central Middlesex, London, UK

Ashley Whittington

Royal Cornwall Hospital (Tresliske), Truro, UK

Meme Wijesinghe

North Bristol NHS Trust, Bristol, UK

Martin Williams

St. Peter’s Hospital, Runnymede, UK

Stephen Winchester

Leicester Royal Infirmary, Leicester, UK

Martin Wiselka

Grantham and District Hospital, Grantham, UK

Adam Wolverson

Aintree University Hospital, Liverpool, UK

Daniel G. Wootton

North Tyneside General Hospital, North Shields, UK

Bryan Yates

Queen Elizabeth Hospital, King’s Lynn, UK

Peter Young

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• , Mark Mencias
• , Daniel Menzies
• , Marta Merida Morillas
• , Alice Michael
• , Benedict Michael
• , C. A. Miller
• , Lea Milligan
• , Nicholas Mills
• , Clare Mills
• , George Mills
• , L. Milner
• , Jane Mitchell
• , Abdelrahman Mohamed
• , Noura Mohamed
• , S. Mohammed
• , Philip Molyneaux
• , Will Monteiro
• , Silvia Moriera
• , Anna Morley
• , Leigh Morrison
• , Richard Morriss
• , A. Morrow
• , Paul Moss
• , Alistair Moss
• , K. Motohashi
• , N. Msimanga
• , Elizabeta Mukaetova-Ladinska
• , Unber Munawar
• , Jennifer Murira
• , Uttam Nanda
• , Heeah Nassa
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• , Rashmita Nathu
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• , Lynn Osborne
• , Rebecca Osbourne
• , Marlies Ostermann
• , Charlotte Overton
• , Jamie Pack
• , Edmund Pacpaco
• , Stella-Maria Paddick
• , Sharon Painter
• , Erola Pairo-Castineira
• , Ashkan Pakzad
• , Sue Palmer
• , Padmasayee Papineni
• , K. Paques
• , Kerry Paradowski
• , Manish Pareek
• , Dhruv Parekh
• , H. Parfrey
• , Carmen Pariante
• , S. Parker
• , M. Parkes
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• , Sheetal Patale
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• , Dibya Pattenadk
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• , Lorraine Pearce
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• , Dan Peckham
• , Jessica Pendlebury
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• , Ida Peralta
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• , Z. Peterkin
• , Tunde Peto
• , Nayia Petousi
• , John Petrie
• , Paul Pfeffer
• , Janet Phipps
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• , John Pimm
• , Karen Piper Hanley
• , Riinu Pius
• , Hannah Plant
• , Tatiana Plekhanova
• , Megan Plowright
• , Krisnah Poinasamy
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• , Liz Ratcliffe
• , Phillip Ravencroft
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• , Heidi Redfearn
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• , Tabitha Rees
• , Karen Regan
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• , Natalie Rogers
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• , William Schwaeble
• , Janet Scott
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• , Malcolm G. Semple
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• , Sharlene Shashaa
• , Alison Shaw
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• , Gemma Simons
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• , John Simpson
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• , Claire Singh
• , Sally Singh
• , D. Sissons
• , J. Skeemer
• , Katie Slack
• , David Smith
• , Nikki Smith
• , Andrew Smith
• , Jacqui Smith
• , Laurie Smith
• , Susan Smith
• , M. Soares
• , Teresa Solano
• , Reanne Solly
• , A. R. Solstice
• , Tracy Soulsby
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• , D. Sowter
• , Mark Spears
• , Lisa Spencer
• , Fabio Speranza
• , Louise Stadon
• , Stefan Stanel
• , R. Steeds
• , N. Steele
• , Mike Steiner
• , David Stensel
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• , Lorraine Stephenson
• , Iain Stewart
• , R. Stimpson
• , Sue Stockdale
• , J. Stockley
• , Wendy Stoker
• , Roisin Stone
• , Will Storrar
• , Andrew Storrie
• , Kim Storton
• , E. Stringer
• , Sophia Strong-Sheldrake
• , Natalie Stroud
• , Christian Subbe
• , Catherine Sudlow
• , Zehra Suleiman
• , Charlotte Summers
• , C. Summersgill
• , Debbie Sutherland
• , D. L. Sykes
• , Nick Talbot
• , Ai Lyn Tan
• , Lawrence Tarusan
• , Vera Tavoukjian
• , Jessica Taylor
• , Abigail Taylor
• , Chris Taylor
• , John Paul Taylor
• , Amelie Te
• , Caroline Tee
• , J. Teixeira
• , Helen Tench
• , Sarah Terry
• , Susannah Thackray-Nocera
• , Favas Thaivalappil
• , David Thickett
• , David Thomas
• , S. Thomas
• , Caradog Thomas
• , Andrew Thomas
• , T. Thomas-Woods
• , A. A. Roger Thompson
• , Tamika Thompson
• , T. Thornton
• , Matthew Thorpe
• , Ryan S. Thwaites
• , Jo Tilley
• , N. Tinker
• , Gerlynn Tiongson
• , Martin Tobin
• , Johanne Tomlinson
• , Mark Toshner
• , T. Treibel
• , K. A. Tripp
• , Drupad Trivedi
• , E. M. Tunnicliffe
• , Alison Turnbull
• , Kim Turner
• , Sarah Turner
• , Victoria Turner
• , E. Turner
• , Sharon Turney
• , Lance Turtle
• , Helena Turton
• , Jacinta Ugoji
• , R. Ugwuoke
• , Rachel Upthegrove
• , Jonathon Valabhji
• , Maximina Ventura
• , Joanne Vere
• , Carinna Vickers
• , Ben Vinson
• , Ioannis Vogiatzis
• , Elaine Wade
• , Phillip Wade
• , Louise V. Wain
• , Tania Wainwright
• , Lilian Wajero
• , Sinead Walder
• , Samantha Walker
• , S. Walker
• , Tim Wallis
• , Sarah Walmsley
• , Simon Walsh
• , J. A. Walsh
• , Louise Warburton
• , T. J. C. Ward
• , Katie Warwick
• , Helen Wassall
• , Samuel Waterson
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• , Ekaterina Watson
• , James Watson
• , M. Webster
• , J. Weir McCall
• , Carly Welch
• , Simon Wessely
• , Sophie West
• , Heather Weston
• , Helen Wheeler
• , Sonia White
• , Victoria Whitehead
• , J. Whitney
• , S. Whittaker
• , Beverley Whittam
• , V. Whitworth
• , Andrew Wight
• , James Wild
• , Martin Wilkins
• , Dan Wilkinson
• , Nick Williams
• , N. Williams
• , B. Williams
• , Jenny Williams
• , S. A. Williams-Howard
• , Michelle Willicombe
• , Gemma Willis
• , James Willoughby
• , Ann Wilson
• , Imogen Wilson
• , Daisy Wilson
• , Nicola Window
• , M. Witham
• , Rebecca Wolf-Roberts
• , Chloe Wood
• , F. Woodhead
• , Janet Woods
• , Dan Wootton
• , J. Wormleighton
• , J. Worsley
• , David Wraith
• , Caroline Wrey Brown
• , C. Wright
• , S. Wright
• , Louise Wright
• , Inez Wynter
• , Moucheng Xu
• , Najira Yasmin
• , S. Yasmin
• , Tom Yates
• , Kay Por Yip
• , Susan Young
• , Bob Young
• , A. J. Yousuf
• , Amira Zawia
• , Lisa Zeidan
• , Bang Zhao
• , Bang Zheng
•  & O. Zongo
• , Daniel Agranoff
• , Ken Agwuh
• , Katie A. Ahmed
• , Dhiraj Ail
• , Erin L. Aldera
• , Ana Alegria
• , Beatrice Alex
• , Sam Allen
• , Petros Andrikopoulos
• , Brian Angus
• , Jane A. Armstrong
• , Abdul Ashish
• , Milton Ashworth
• , Innocent G. Asiimwe
• , Dougal Atkinson
• , Benjamin Bach
• , Siddharth Bakshi
• , Wendy S. Barclay
• , Shahedal Bari
• , Gavin Barlow
• , Samantha L. Barlow
• , Stella Barnass
• , Nicholas Barrett
• , Christopher Bassford
• , Sneha Basude
• , David Baxter
• , Michael Beadsworth
• , Jolanta Bernatoniene
• , John Berridge
• , Nicola Best
• , Debby Bogaert
• , Laura Booth
• , Pieter Bothma
• , Benjamin Brennan
• , Robin Brittain-Long
• , Katie Bullock
• , Naomi Bulteel
• , Tom Burden
• , Andrew Burtenshaw
• , Nicola Carlucci
• , Gail Carson
• , Vikki Caruth
• , Emily Cass
• , Benjamin W. A. Catterall
• , David Chadwick
• , Duncan Chambler
• , Meera Chand
• , Kanta Chechi
• , Nigel Chee
• , Jenny Child
• , Srikanth Chukkambotla
• , Richard Clark
• , Tom Clark
• , Jordan J. Clark
• , Emily A. Clarke
• , Sara Clohisey
• , Sarah Cole
• , Paul Collini
• , Marie Connor
• , Graham S. Cooke
• , Louise Cooper
• , Catherine Cosgrove
• , Audrey Coutts
• , Helen Cox
• , Jason Cupitt
• , Maria-Teresa Cutino-Moguel
• , Ana da Silva Filipe
• , Jo Dalton
• , Paul Dark
• , Christopher Davis
• , Chris Dawson
• , Thushan de Silva
• , Samir Dervisevic
• , Oslem Dincarslan
• , Alejandra Doce Carracedo
• , Cara Donegan
• , Lorna Donelly
• , Phil Donnison
• , Chloe Donohue
• , Gonçalo dos Santos Correia
• , Sam Douthwaite
• , Thomas M. Drake
• , Andrew Drummond
• , Marc-Emmanuel Dumas
• , Chris Dunn
• , Jake Dunning
• , Ingrid DuRand
• , Ahilanadan Dushianthan
• , Tristan Dyer
• , Philip Dyer
• , Angela Elliott
• , Cariad Evans
• , Anthony Evans
• , Chi Eziefula
• , Cameron J. Fairfield
• , Angie Fawkes
• , Chrisopher Fegan
• , Lorna Finch
• , Adam Finn
• , Lewis W. S. Fisher
• , Lisa Flaherty
• , Tom Fletcher
• , Terry Foster
• , Duncan Fullerton
• , Carrol Gamble
• , Isabel Garcia-Dorival
• , Atul Garg
• , Sanjeev Garg
• , Tammy Gilchrist
• , Michelle Girvan
• , Effrossyni Gkrania-Klotsas
• , Jo Godden
• , Arthur Goldsmith
• , Clive Graham
• , Tassos Grammatikopoulos
• , Christopher A. Green
• , Julian Griffin
• , Fiona Griffiths
• , Philip Gunning
• , Rishi K. Gupta
• , Katarzyna Hafezi
• , Sophie Halpin
• , Elaine Hardy
• , Ewen M. Harrison
• , Janet Harrison
• , Catherine Hartley
• , Stuart Hartshorn
• , Daniel Harvey
• , Peter Havalda
• , Daniel B. Hawcutt
• , Ross Hendry
• , Antonia Y. W. Ho
• , Maria Hobrok
• , Luke Hodgson
• , Karl Holden
• , Anthony Holmes
• , Peter W. Horby
• , Joanne Howard
• , Samreen Ijaz
• , Clare Jackson
• , Michael Jacobs
• , Susan Jain
• , Paul Jennings
• , Rebecca L. Jensen
• , Christopher B. Jones
• , Trevor R. Jones
• , Agilan Kaliappan
• , Vidya Kasipandian
• , Seán Keating
• , Stephen Kegg
• , Michael Kelsey
• , Jason Kendall
• , Caroline Kerrison
• , Ian Kerslake
• , Shadia Khandaker
• , Katharine King
• , Robyn T. Kiy
• , Stephen R. Knight
• , Susan Knight
• , Oliver Koch
• , Gouri Koduri
• , George Koshy
• , Chrysa Koukorava
• , Shondipon Laha
• , Eva Lahnsteiner
• , Steven Laird
• , Annette Lake
• , Suzannah Lant
• , Susan Larkin
• , Diane Latawiec
• , Andrew Law
• , James Lee
• , Gary Leeming
• , Daniella Lefteri
• , Tamas Leiner
• , Lauren Lett
• , Matthew Lewis
• , Sonia Liggi
• , Patrick Lillie
• , Wei Shen Lim
• , James Limb
• , Vanessa Linnett
• , Jeff Little
• , Lucia A. Livoti
• , Mark Lyttle
• , Louise MacGillivray
• , Alan Maclean
• , Michael MacMahon
• , Emily MacNaughton
• , Maria Mancini
• , Ravish Mankregod
• , Laura Marsh
• , Lynn Maslen
• , Hannah Massey
• , Huw Masson
• , Elijah Matovu
• , Nicole Maziere
• , Sarah McCafferty
• , Katherine McCullough
• , Sarah E. McDonald
• , Sarah McDonald
• , Laurence McEvoy
• , Ruth McEwen
• , John McLauchlan
• , Kenneth A. Mclean
• , Manjula Meda
• , Alexander J. Mentzer
• , Laura Merson
• , Soeren Metelmann
• , Alison M. Meynert
• , Nahida S. Miah
• , Joanna Middleton
• , Gary Mills
• , Jane Minton
• , Joyce Mitchell
• , Kavya Mohandas
• , James Moon
• , Elinoor Moore
• , Shona C. Moore
• , Patrick Morgan
• , Kirstie Morrice
• , Craig Morris
• , Katherine Mortimore
• , Samuel Moses
• , Mbiye Mpenge
• , Rohinton Mulla
• , Derek Murphy
• , Lee Murphy
• , Michael Murphy
• , Ellen G. Murphy
• , Thapas Nagarajan
• , Megan Nagel
• , Mark Nelson
• , Lisa Norman
• , Lillian Norris
• , Lucy Norris
• , Mahdad Noursadeghi
• , Michael Olanipekun
• , Wilna Oosthuyzen
• , Anthonia Osagie
• , Matthew K. O’Shea
• , Igor Otahal
• , Mark Pais
• , Massimo Palmarini
• , Carlo Palmieri
• , Selva Panchatsharam
• , Danai Papakonstantinou
• , Hassan Paraiso
• , Brij Patel
• , Natalie Pattison
• , William A. Paxton
• , Rebekah Penrice-Randal
• , Justin Pepperell
• , Mark Peters
• , Mandeep Phull
• , Jack Pilgrim
• , Stefania Pintus
• , Tim Planche
• , Daniel Plotkin
• , Georgios Pollakis
• , Frank Post
• , Nicholas Price
• , David Price
• , Tessa Prince
• , Rachel Prout
• , Nikolas Rae
• , Andrew Rambaut
• , Henrik Reschreiter
• , Tim Reynolds
• , Neil Richardson
• , P. Matthew Ridley
• , Mark Roberts
• , Stephanie Roberts
• , Devender Roberts
• , David L. Robertson
• , Alistair Rose
• , Guy Rousseau
• , Bobby Ruge
• , Clark D. Russell
• , Brendan Ryan
• , Debby Sales
• , Taranprit Saluja
• , Vanessa Sancho-Shimizu
• , Caroline Sands
• , Egle Saviciute
• , Matthias Schmid
• , Janet T. Scott
• , James Scott-Brown
• , Aarti Shah
• , Prad Shanmuga
• , Anil Sharma
• , Catherine A. Shaw
• , Victoria E. Shaw
• , Anna Shawcross
• , Rebecca K. Shears
• , Jagtur Singh Pooni
• , Jeremy Sizer
• , Benjamin Small
• , Richard Smith
• , Catherine Snelson
• , Tom Solomon
• , Rebecca G. Spencer
• , Nick Spittle
• , Shiranee Sriskandan
• , Nikki Staines
• , Tom Stambach
• , Richard Stewart
• , David Stuart
• , Krishanthi S. Subramaniam
• , Pradeep Subudhi
• , Olivia V. Swann
• , Tamas Szakmany
• , Agnieska Szemiel
• , Aislynn Taggart
• , Sarah Tait
• , Zoltan Takats
• , Panteleimon Takis
• , Jolanta Tanianis-Hughes
• , Kate Tatham
• , Richard S. Tedder
• , Jo Thomas
• , Jordan Thomas
• , Robert Thompson
• , Chris Thompson
• , Emma C. Thomson
• , Ascanio Tridente
• , Erwan Trochu
• , Darell Tupper-Carey
• , Lance C. W. Turtle
• , Mary Twagira
• , Nick Vallotton
• , Libby van Tonder
• , Rama Vancheeswaran
• , Rachel Vincent
• , Lisa Vincent-Smith
• , Shico Visuvanathan
• , Alan Vuylsteke
• , Sam Waddy
• , Rachel Wake
• , Andrew Walden
• , Ingeborg Welters
• , Murray Wham
• , Tony Whitehouse
• , Paul Whittaker
• , Ashley Whittington
• , Meme Wijesinghe
• , Eve Wilcock
• , Martin Williams
• , Lawrence Wilson
• , Stephen Winchester
• , Martin Wiselka
• , Adam Wolverson
• , Daniel G. Wootton
• , Andrew Workman
• , Nicola Wrobel
• , Bryan Yates
• , Peter Young
• , Maria Zambon
•  & J. Eunice Zhang

Contributions

F.L. recruited participants, acquired clinical samples, analyzed and interpreted data and cowrote the manuscript, including all drafting and revisions. C.E. analyzed and interpreted data and cowrote this manuscript, including all drafting and revisions. S.F. and M.R. supported the analysis and interpretation of data as well as drafting and revisions. D.S., J.K.S., S.C.M., S.A., N.M., J.N., C.K., O.C.L., O.E., H.J.C.M., A. Shikotra, A. Singapuri, M.S., V.C.H., M.T., N.J.G., N.I.L. and C.C. contributed to acquisition of data underlying this study. L.H.-W., A.A.R.T., S.L.R.-J., L.S.H., O.M.K., D.G.W., T.I.d.S. and A. Ho made substantial contributions to conception/design and implementation of this work and/or acquisition of clinical samples for this work. They have supported drafting and revisions of the manuscript. E.M.H., J.K.Q. and A.B.D. made substantial contributions to the study design as well as data access, linkage and analysis. They have supported drafting and revisions of this work. J.D.C., L.-P.H., A. Horsley, B.R., K.P., M.M. and W.G. made substantial contributions to the conception and design of this work and have supported drafting and revisions of this work. J.K.B. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, has made substantial contributions to conception and design of this work and has supported drafting and revisions of this work. M.G.S. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, sponsor/protocol chief investigator, has made substantial contributions to conception and design of this work and has supported drafting and revisions of this work. R.A.E. and L.V.W. are co-leads of PHOSP-COVID, made substantial contributions to conception and design of this work, the acquisition and analysis of data, and have supported drafting and revisions of this work. C.B. is the chief investigator of PHOSP-COVID and has made substantial contributions to conception and design of this work. R.S.T. and L.T. made substantial contributions to the acquisition, analysis and interpretation of the data underlying this study and have contributed to drafting and revisions of this work. P.J.M.O. obtained funding for ISARIC4C, is ISARIC4C consortium co-lead, sponsor/protocol chief investigator and has made substantial contributions to conception and design of this work. R.S.T. and P.J.M.O. have also made key contributions to interpretation of data and have co-written this manuscript. All authors have read and approve the final version to be published. All authors agree to accountability for all aspects of this work. All investigators within ISARIC4C and the PHOSP-COVID consortia have made substantial contributions to the conception or design of this study and/or acquisition of data for this study. The full list of authors within these groups is available in Supplementary Information .

Corresponding authors

Correspondence to Ryan S. Thwaites or Peter J. M. Openshaw .

Ethics declarations

Competing interests.

F.L., C.E., D.S., J.K.S., S.C.M., C.D., C.K., N.M., L.N., E.M.H., A.B.D., J.K.Q., L.-P.H., K.P., L.S.H., O.M.K., S.F., T.I.d.S., D.G.W., R.S.T. and J.K.B. have no conflicts of interest. A.A.R.T. receives speaker fees and support to attend meetings from Janssen Pharmaceuticals. S.L.R.-J. is on the data safety monitoring board for Bexero trial in HIV+ adults in Kenya. J.D.C. is the deputy chief editor of the European Respiratory Journal and receives consulting fees from AstraZeneca, Boehringer Ingelheim, Chiesi, GSK, Insmed, Janssen, Novartis, Pfizer and Zambon. A. Horsley is deputy chair of NIHR Translational Research Collaboration (unpaid role). B.R. receives honoraria from Axcella therapeutics. R.A.E. is co-lead of PHOSP-COVID and receives fees from AstraZenaca/Evidera for consultancy on LC and from AstraZenaca for consultancy on digital health. R.A.E. has received speaker fees from Boehringer in June 2021 and has held a role as European Respiratory Society Assembly 01.02 Pulmonary Rehabilitation secretary. R.A.E. is on the American Thoracic Society Pulmonary Rehabilitation Assembly program committee. L.V.W. also receives funding from Orion pharma and GSK and holds contracts with Genentech and AstraZenaca. L.V.W. has received consulting fees from Galapagos and Boehringer, is on the data advisory board for Galapagos and is Associate Editor for the European Respiratory Journal . A. Ho is a member of NIHR Urgent Public Health Group (June 2020–March 2021). M.M. is an applicant on the PHOSP study funded by NIHR/DHSC. M.G.S. acts as an independent external and nonremunerated member of Pfizer’s External Data Monitoring Committee for their mRNA vaccine program(s), is Chair of Infectious Disease Scientific Advisory Board of Integrum Scientific LLC, and is director of MedEx Solutions Ltd. and majority owner of MedEx Solutions Ltd. and minority owner of Integrum Scientific LLC. M.G.S.’s institution has been in receipt of gifts from Chiesi Farmaceutici S.p.A. of Clinical Trial Investigational Medicinal Product without encumbrance and distribution of same to trial sites. M.G.S. is a nonrenumerated member of HMG UK New Emerging Respiratory Virus Threats Advisory Group and has previously been a nonrenumerated member of the Scientific Advisory Group for Emergencies (SAGE). C.B. has received consulting fees and/or grants from GSK, AstraZeneca, Genentech, Roche, Novartis, Sanofi, Regeneron, Chiesi, Mologic and 4DPharma. L.T. has received consulting fees from MHRA, AstraZeneca and Synairgen and speakers’ fees from Eisai Ltd., and support for conference attendance from AstraZeneca. L.T. has a patent pending with ZikaVac. P.J.M.O. reports grants from the EU Innovative Medicines Initiative 2 Joint Undertaking during the submitted work; grants from UK Medical Research Council, GSK, Wellcome Trust, EU Innovative Medicines Initiative, UK National Institute for Health Research and UK Research and Innovation–Department for Business, Energy and Industrial Strategy; and personal fees from Pfizer, Janssen and Seqirus, outside the submitted work.

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Extended data

Extended data fig. 1 penalized logistic regression performance..

Graphs show classification error and Area under curve (AUC) from the 50 repeats tenfold nested cross-validation used to optimise and assess the performance of PLR testing associations with each LC outcome relative to Recovered (n = 233): Cardio_Resp (n = 398), Fatigue (n = 384), Anxiety/Depression (n = 202), GI (n = 132), ( e ) Cognitive (n = 6). The distributions of classification error and area under curve (AUC) from the nested cross-validation are shown. Box plot centre line represents the Median and boundaries of the box represent interquartile range (IQR), the whisker length represent 1.5xIQR.

Extended Data Fig. 2 Associations with long COVID symptoms in full study cohort.

( a ) Fibrinogen levels at 6 months were compared between pooled LC cases (n = 295) and Recovered (n = 233) and between the Cognitive group (n = 41) and Recovered (n = 233). Box plot centre line represent the Median and boundaries of the box represent interquartile range (IQR), the whisker length represents 1.5xIQR, any outliers beyond the whisker range are shown as individual dots. Median differences were compared using two-sided Wilcoxon signed-rank test *= p  < 0·05, **= p  < 0·01, ***= p  < 0·001, ****= p  < 0·0001. Unadjusted p-values are reported. b ) Distribution of time from COVID-19 hospitalisation at sample collection applying CDC and NICE definitions of LC (n = 719) ( c ) Upset plot of symptom groups. Horizontal coloured bars represent the number of patients in each symptom group: Cardiorespiratory (Cardio_Resp), Fatigue, Cognitive, Gastrointestinal (GI) and Anxiety/Depression (Anx_Dep). Vertical black bars represent the number of patients in each symptom combination group. To prevent patient identification, where less than 5 patients belong to a combination group, this has been represented as ‘<5’. The Recovered group (n = 250) were used as controls. Forest plots show Olink protein concentrations (NPX) associated with ( d ) Cardio_Resp (n = 398), ( e ) Fatigue (n = 342), ( f ) Anx_Dep (n = 219), ( g ) GI (n = 134), and ( h ) Cognitive (n = 65). Error bars represent the median accuracy of the model.

Extended Data Fig. 3 Validation of olink measurements using conventional assays in plasma.

Olink measured protein (NPX) were compared to chemiluminescence assays (ECL or ELISA, log2[pg/mL]) to validate our findings, where contemporaneously collected plasma samples were available (n = 58). Results from key mediators associated with LC groups were validated: CSF3, IL1R2, IL2, IL3RA, TNFa, TFF2. R = spearman rank correlation coefficient and shaded areas indicated the 95% confidence interval. Samples that fell below the lower limit of detection for a given assay were excluded and the ‘n’ value on each panel indicates the number of samples above this limit.

Extended Data Fig. 4 Univariate analysis of proteins associated with each symptom.

Olink measured plasma protein levels (NPX) compared between LC groups (Cardio_Resp, n = 398, Fatigue n = 384, Anxiety/Depression, n = 202, GI, n = 132 and Cognitive, n = 60) and Recovered (n = 233). Proteins identified by PLR were compared between groups. Median differences were compared using two-sided Wilcoxon signed-rank test. * = p < 0·05, ** = p < 0·01, *** = p < 0·001, ****= p < 0·0001 after FDR adjustment. Box plot centre line represent the Median and boundaries of the box represent interquartile range (IQR), the whisker length represents 1.5xIQR, any outliers beyond the whisker range are shown as individual dots.

Extended Data Fig. 5 Unadjusted Penalised Logistic Regression.

Olink measured proteins (NPX) and their association with Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65). Forest plots show odds of each LC outcome vs Recovered (n = 233), using PLR without adjusting for clinical co-variates. Error bars represent the median accuracy of the model.

Extended Data Fig. 6 Partial Least Squares analysis.

Olink measured proteins (NPX) and their association with Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups. Forest plots show odds of LC outcome vs Recovered (n = 233), using PLS analysis. Error bars represent the standard error of the coefficient estimate.

Extended Data Fig. 7 Network analysis centrality.

Each graph shows the centrality score for each Olink measured protein (NPX) found to have significant associations with other proteins that were elevated in the Cardio_Resp (n = 398), Fatigue (n = 342), Anx_Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups relative to Recovered (n = 233).

Extended Data Fig. 8 Inflammation in men and women with long COVID.

Olink measured plasma protein levels (NPX) between men and women with symptoms, divided by age (<50 or >=50years): (a) shows IL1R2 and MATN2 in the Anxiety/Depression group (<50 n = 55, >=50 n = 133), (b) shows CTSO and NFASC in the Cognitive group (<50 n = 11, >=50 n = 50). Median values were compared between men and women using two-sided Wilcoxon signed-rank test. Box plot centre line represent the Median and boundaries represent interquartile range (IQR), the whisker length represents 1.5xIQR.

Extended Data Fig. 9 Inflammation in the upper respiratory tract.

Nasal cytokines measured by immunoassay in the CardioResp Group (n = 29) and Recovered (n = 31): ( a ) shows IL1a, IL1b, IL-6, APO-2, TGFa, TFF2. Median differences were compared using two-sided Wilcoxon signed-rank test. Box plot centre line represents the Median and boundaries of the box represent interquartile range (IQR), the whisker length represent 1.5xIQR. ( b ) Shows cytokines measured by immunoassay in paired plasma and nasal (n = 70). Correlations between IL1a, IL1b, IL-6, APO-2, TGFa and TFF2 in nasal and plasma samples were compared using Spearman’s rank correlation coefficient ( R ). Shaded areas indicated the 95% confidence interval of R.

Extended Data Fig. 10 Graphical abstract.

Summary of interpretation of key findings from Olink measured proteins and their association with CardioResp (n = 398), Fatigue (n = 342), Anx/Dep (n = 219), GI (n = 134), and Cognitive (n = 65) groups relative to Recovered (n = 233).

Supplementary information

Supplementary information.

Supplementary Methods, Statistics and reproducibility statement, Supplementary Results, Supplementary Tables 1–7, Extended data figure legends, Appendix 1 (Supplementary Table 8), Appendix 2 (PHOSP-COVID author list) and Appendix 3 (ISARIC4C author list).

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Liew, F., Efstathiou, C., Fontanella, S. et al. Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease. Nat Immunol (2024). https://doi.org/10.1038/s41590-024-01778-0

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Computer Science > Machine Learning

Title: red teaming gpt-4v: are gpt-4v safe against uni/multi-modal jailbreak attacks.

Abstract: Various jailbreak attacks have been proposed to red-team Large Language Models (LLMs) and revealed the vulnerable safeguards of LLMs. Besides, some methods are not limited to the textual modality and extend the jailbreak attack to Multimodal Large Language Models (MLLMs) by perturbing the visual input. However, the absence of a universal evaluation benchmark complicates the performance reproduction and fair comparison. Besides, there is a lack of comprehensive evaluation of closed-source state-of-the-art (SOTA) models, especially MLLMs, such as GPT-4V. To address these issues, this work first builds a comprehensive jailbreak evaluation dataset with 1445 harmful questions covering 11 different safety policies. Based on this dataset, extensive red-teaming experiments are conducted on 11 different LLMs and MLLMs, including both SOTA proprietary models and open-source models. We then conduct a deep analysis of the evaluated results and find that (1) GPT4 and GPT-4V demonstrate better robustness against jailbreak attacks compared to open-source LLMs and MLLMs. (2) Llama2 and Qwen-VL-Chat are more robust compared to other open-source models. (3) The transferability of visual jailbreak methods is relatively limited compared to textual jailbreak methods. The dataset and code can be found here https://anonymous.4open.science/r/red_teaming_gpt4-C1CE/README.md .

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Don't have glasses to watch today's solar eclipse? Here are 7 safe alternatives.

The 2024 solar eclipse has finally arrived! This afternoon, millions will have their eyes on the sky to witness the celestial event.

Many locations giving away or selling solar eclipse glasses are out of stock, so you may not find a pair within the next few hours.

People are also reading: Where to buy solar eclipse glasses, according to the experts

Don't panic though − there are alternative forms of eye protection you can use instead.

Watching the light pass through tree leaves. Peering into a modified cereal box. Angling a kitchen colander. You can view the solar eclipse using these safe, alternative methods and more.

Live updates: Everything you need to know for today’s solar eclipse

Tree leaves project mini solar eclipses

A fun way to view the eclipse is by watching the light pass through tree leaves. Sunlight will project mini eclipses onto the ground as the light passes through gaps in the leaves.

You will be able to track the progress of the eclipse from start to finish, and see a cool natural effect.

Use a colander to show the solar eclipse

Similar to leaves, you can use a colander to project mini eclipses. The light will pass through the small holes in a colander to create a dazzling sight of tiny eclipses on the ground or on a canvas.

Senior lecturer and astronomy coordinator at UT Sean Lindsay likes this method as it's a more direct and safe way of showing the progress of an eclipse.

More: What time is the total solar eclipse on April 8? Search your ZIP code for a viewing guide

Create a pinhole projector to see the solar eclipse

With a  pinhole projector , sunlight travels through the pinhole to create a small image of the sun. The projector makes it safe to observe the solar eclipse if you don't have eclipse glasses.

All you need to do is take a piece of cardboard, cut an inch square in the middle, tape foil over the square and poke a small hole in the foil. Afterwards, angle the cardboard so the light travels through the hole and onto another piece of cardboard to show an image of the sun.

Warby Parker provided a guide on how to  DIY your own pinhole projector , or check out  NASA's video tutorial .

How to turn a box into a pinhole projector to view the eclipse

Another way to make a pinhole projector includes a box, tape, scissors, foil and paper.

Find a cardboard box you can comfortably place over your head. Cut a square hole on one end of the box and tape the foil over it. Poke a small hole in the foil. On the opposite side, tape a piece of paper inside the box.

Once its ready, turn your back to the sun and place the box over your head. Angle the box so the light goes through the hole and projects onto the paper. Now you can see the eclipse.

Watch: Can't watch Monday's total solar eclipse in-person? Watch our livestream here.

Convert a cereal box into a solar eclipse viewer

You can also make a pinhole projector using a cereal box.  NASA provides instructions  on how to craft one.

Empty the contents of the box and place a white piece of paper or cardboard at the bottom. Cut both ends of the top leaving just the center flaps. Tape the center to keep it closed. Cover one of the openings with foil and poke a small hole into the foil, but leave the other side open.

Once the cereal box is ready, you will need to turn away from the sun, angle the box so sunlight goes through the hole and peer into the box from the opening. The light will project an image of the sun onto the bottom of the box.

Turn a shoebox into a solar eclipse viewer

The cereal box method  works with shoeboxes , too.

Cut a small hole on one end of the shoebox and tape foil over it. Poke a small hole in the foil. Tape a small piece of paper inside the shoebox on the other end.

You can cut a hole to look into either on the foil side or on the long side of the shoebox near the paper for a closer view. When it's ready, close the shoebox and angle it so the sunlight passes through the pinhole, down the length of the shoebox and onto the paper. It will project an image of the sun inside.

Use your hands to view the solar eclipse

Take both hands and  overlap your fingers  with one hand vertical and the other horizontal. Your fingers should cross over each other and form square gaps.

Now, angle your hands so sunlight hits them. Sunlight will pass through the gaps like pinholes, projecting mini eclipses onto the ground or a canvas.

Are you in the path of totality?

Can't see our graphics? Search your ZIP code for a complete eclipse viewing guide