presents written research methodology

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Organizing Your Social Sciences Research Paper

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

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

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

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

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

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

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

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

In addition, an effectively written methodology section should:

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

NOTE :   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE : If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE :   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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The Ultimate Guide To Research Methodology

Research methodology is a crucial aspect of any investigative process, serving as the blueprint for the entire research journey. If you are stuck in the methodology section of your research paper , then this blog will guide you on what is a research methodology, its types and how to successfully conduct one. 

Table of Contents

What Is Research Methodology?

Research methodology can be defined as the systematic framework that guides researchers in designing, conducting, and analyzing their investigations. It encompasses a structured set of processes, techniques, and tools employed to gather and interpret data, ensuring the reliability and validity of the research findings. 

Research methodology is not confined to a singular approach; rather, it encapsulates a diverse range of methods tailored to the specific requirements of the research objectives.

Here is why Research methodology is important in academic and professional settings.

Facilitating Rigorous Inquiry

Research methodology forms the backbone of rigorous inquiry. It provides a structured approach that aids researchers in formulating precise thesis statements , selecting appropriate methodologies, and executing systematic investigations. This, in turn, enhances the quality and credibility of the research outcomes.

Ensuring Reproducibility And Reliability

In both academic and professional contexts, the ability to reproduce research outcomes is paramount. A well-defined research methodology establishes clear procedures, making it possible for others to replicate the study. This not only validates the findings but also contributes to the cumulative nature of knowledge.

Guiding Decision-Making Processes

In professional settings, decisions often hinge on reliable data and insights. Research methodology equips professionals with the tools to gather pertinent information, analyze it rigorously, and derive meaningful conclusions.

This informed decision-making is instrumental in achieving organizational goals and staying ahead in competitive environments.

Contributing To Academic Excellence

For academic researchers, adherence to robust research methodology is a hallmark of excellence. Institutions value research that adheres to high standards of methodology, fostering a culture of academic rigour and intellectual integrity. Furthermore, it prepares students with critical skills applicable beyond academia.

Enhancing Problem-Solving Abilities

Research methodology instills a problem-solving mindset by encouraging researchers to approach challenges systematically. It equips individuals with the skills to dissect complex issues, formulate hypotheses , and devise effective strategies for investigation.

Understanding Research Methodology

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

Basics Of Research

Research, in its essence, is a systematic and organized process of inquiry aimed at expanding our understanding of a particular subject or phenomenon. It involves the exploration of existing knowledge, the formulation of hypotheses, and the collection and analysis of data to draw meaningful conclusions. 

Research is a dynamic and iterative process that contributes to the continuous evolution of knowledge in various disciplines.

Types of Research

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

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

Components Of Research Methodology

To conduct effective research, one must go through the different components of research methodology. These components form the scaffolding that supports the entire research process, ensuring its coherence and validity.

Research Design

Research design serves as the blueprint for the entire research project. It outlines the overall structure and strategy for conducting the study. The three primary types of research design are:

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

Data Collection Methods

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

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

Data Analysis Techniques

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

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

The research paper we write have:

• Precision and Clarity
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Choosing a Research Method

Selecting an appropriate research method is a critical decision in the research process. It determines the approach, tools, and techniques that will be used to answer the research questions. 

Quantitative Research Methods

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

Experimental Research

Experimental research involves manipulating variables to observe the effect on another variable under controlled conditions. It aims to establish cause-and-effect relationships.

Key Characteristics:

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

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

Survey Research

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

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

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

Descriptive Research

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

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

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

Qualitative Research Methods

Qualitative research emphasizes exploring and understanding the depth and complexity of phenomena through non-numerical data.

A case study is an in-depth exploration of a particular person, group, event, or situation. It involves detailed, context-rich analysis.

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

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

Ethnography

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

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

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

Grounded Theory

Grounded theory aims to develop theories grounded in the data itself. It involves systematic data collection and analysis to construct theories from the ground up.

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

Applications: Commonly applied in sociology, nursing, and management studies to generate theories from empirical data.

Research design is the structural framework that outlines the systematic process and plan for conducting a study. It serves as the blueprint, guiding researchers on how to collect, analyze, and interpret data.

Exploratory, Descriptive, And Explanatory Designs

Exploratory design.

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

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

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

Descriptive Design

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

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

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

Explanatory Design

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

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

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

Cross-Sectional Vs. Longitudinal Designs

Cross-sectional design.

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

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

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

Longitudinal Design

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

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

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

Experimental Vs Non-experimental Designs

Experimental design.

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

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

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

Non-Experimental Design

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

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

Applications: Suitable for studying complex phenomena in real-world settings where manipulation may not be ethical or feasible.

Effective data collection is fundamental to the success of any research endeavour. 

Designing Effective Surveys

Objective Design:

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

Structured Format:

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

Pilot Testing:

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

Sampling Strategy:

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

Conducting Interviews

Establishing Rapport:

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

Open-Ended Questions:

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

Active Listening:

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

Ethical Considerations:

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

Observation

1. participant observation.

Immersive Participation:

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

Field Notes:

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

Ethical Awareness:

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

2. Non-participant Observation

Objective Observation:

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

Data Reliability:

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

Contextual Understanding:

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

Archival Research

1. using existing data.

Identifying Relevant Archives:

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

Data Verification:

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

Ethical Use:

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

2. Challenges and Considerations

Incomplete or Inaccurate Archives:

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

Temporal Bias:

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

Access Limitations:

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

Common Challenges in Research Methodology

Conducting research is a complex and dynamic process, often accompanied by a myriad of challenges. Addressing these challenges is crucial to ensure the reliability and validity of research findings.

Sampling Issues

Sampling bias:.

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

Sample Size Determination:

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

Data Quality And Validity

Measurement error:.

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

Construct Validity:

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

Time And Resource Constraints

Timeline pressures:.

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

Resource Availability:

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

Managing Bias in Research

Selection bias:.

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

Confirmation Bias:

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

Tips On How To Write A Research Methodology

Conducting successful research relies not only on the application of sound methodologies but also on strategic planning and effective collaboration. Here are some tips to enhance the success of your research methodology:

Tip 1. Clear Research Objectives

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

Tip 2. Comprehensive Literature Review

A thorough literature review provides a foundation for understanding existing knowledge and identifying gaps. Invest time in reviewing relevant literature to inform your research design and methodology.

Tip 3. Detailed Research Plan

A detailed plan serves as a roadmap, ensuring all aspects of the research are systematically addressed. Develop a detailed research plan outlining timelines, milestones, and tasks.

Tip 4. Ethical Considerations

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

Tip 5. Stay Updated On Methodologies

Research methodologies evolve, and staying updated is essential for employing the most effective techniques. Engage in continuous learning by attending workshops, conferences, and reading recent publications.

Tip 6. Adaptability In Methods

Unforeseen challenges may arise during research, necessitating adaptability in methods. Be flexible and willing to modify your approach when needed, ensuring the integrity of the study.

Tip 7. Iterative Approach

Research is often an iterative process, and refining methods based on ongoing findings enhance the study’s robustness. Regularly review and refine your research design and methods as the study progresses.

Frequently Asked Questions

What is the research methodology.

Research methodology is the systematic process of planning, executing, and evaluating scientific investigation. It encompasses the techniques, tools, and procedures used to collect, analyze, and interpret data, ensuring the reliability and validity of research findings.

What are the methodologies in research?

Research methodologies include qualitative and quantitative approaches. Qualitative methods involve in-depth exploration of non-numerical data, while quantitative methods use statistical analysis to examine numerical data. Mixed methods combine both approaches for a comprehensive understanding of research questions.

How to write research methodology?

To write a research methodology, clearly outline the study’s design, data collection, and analysis procedures. Specify research tools, participants, and sampling methods. Justify choices and discuss limitations. Ensure clarity, coherence, and alignment with research objectives for a robust methodology section.

How to write the methodology section of a research paper?

In the methodology section of a research paper, describe the study’s design, data collection, and analysis methods. Detail procedures, tools, participants, and sampling. Justify choices, address ethical considerations, and explain how the methodology aligns with research objectives, ensuring clarity and rigour.

What is mixed research methodology?

Mixed research methodology combines both qualitative and quantitative research approaches within a single study. This approach aims to enhance the details and depth of research findings by providing a more comprehensive understanding of the research problem or question.

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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.

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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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Research Methodology: An Introduction

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Digital age brings the most dramatic changes in this study and research discipline as well as in other fields of human activities. Scientific research is known for a very long time, however in comparison with other research fields the business and management researches are a little bit younger. The information technologies and new research methodologies that have recently emerged, dramatically change the nature of the research. Therefore, researchers should be ready to absorb new possibilities and follow basic roles coming from earlier stages of the discipline. The intention of this chapter is to provide a brief introduction to those aspects of pertinent research to beginner researchers. The chapter presents the nature of scientific research so that it may be clearly understood and uses, as its basic approach, the fundamental principles of problem solving. The scope of the research provides an overviews the entire assumptions about reality, knowledge and human nature, key terms of theory and research presented. Main concepts of the research are discussed and all this is oriented to business, management and economic science specific.

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A. Saunders, M. Lewis, P. Thornhill, Research Methods for Business Students , 6th edn (Harlow: Pearson, 2012)

Google Scholar  

B. Blumberg, D.R. Cooper, P.S. Schindler, Business Research Methods , 3rd edn. (McGraw-Hill, London, 2011)

A. Bryman, E. Bell, Business Research Methods , 3rd edn. (Oxford University Press, Oxford, 2015)

A.J. Veal, Business Research Methods: A Managerial Approach (Pearson Education Australia, Frenchs Forest, 2005)

Y.K. Singh, Fundamental of Research Methodology and Statistics (New Age International (P) Ltd, 2006)

A.A. Berger, No Title Media and communication research methods: an introduction to qualitative and quantitative approaches , Third Edition (Sage, Thousand Oaks (Calif.), 2013)

A. Carsrud, M. Brannback, Handbook of Research Methods and Applications in Entrepreneurship and Small Business (Edward Elgar, Cheltenham, 2014)

D.R. Cooper, P.S. Schindler, Business Research Methods , 12th edn. (McGraw-Hill Education, New York, 2013)

M. Easterby-Smith, R. Thorpe, P.R. Jackson, Management Research , 4th edn. (SAGE Publications Ltd, London, 2012)

S. Greener, Business Research Methods (Ventus Publishing ApS, Denmark, 2008)

S.L. Jackson, Research Methods and Statistics: A Critical Thinking Approach (Wadsworth Cengage Learning, Belmont (Calif.), 2012)

B.C. Lindlof, T.R., Taylor, Qualitative Communication Research Methods (Sage, Thousand Oaks (Calif.), 2011)

A.J. Pickard, Research Methods in Information (Facet, London, 2013)

T. Stokes, P., Wall, Research Method (Palgrave Macmillan, London, 2014)

A.H. Walle, Qualitative Research in Business: A Practical Overview (Cambridge Scholars Publishing, Newcastle upon Tyne, 2015)

W.G. Zikmund, B.J. Babin, J.C. Carr, M. Griffin, Business Research Methods, 9th edition (South-Western: Cengage Learning, 2013)

D.C. Mark Teale, V. Dispenza, J. Flynn, Management Decision Making: Towards an Integrative Approach (Financial Times/Prentice Hall, USA, 2002)

B.C. Agrawal, Anthropological Methods for Communication Research: Experiences and Encounters During SITE (Concept Publishing Company, Delhi, 1985)

Britanica, An Inquiry into the Nature and Causes of the Wealth of Nations | work by Smith | Britannica.com . www.britannica.com/topic/An-Inquiry-into-the-Nature-and-Causes-of-the-Wealth-of-Nations . [Accessed: 16 Dec 2015]

L.B. Christensen, R.B. Johnson, L.A. Turner, Research Methods, Design, and Analysis (Pearson, Boston (Mass.), 2013)

N. Blaikie, Designing Social Research (2009)

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Davidavičienė, V. (2018). Research Methodology: An Introduction. In: Marx Gómez, J., Mouselli, S. (eds) Modernizing the Academic Teaching and Research Environment. Progress in IS. Springer, Cham. https://doi.org/10.1007/978-3-319-74173-4_1

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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How to Write Research Methodology

Last Updated: May 21, 2023 Approved

This article was co-authored by Alexander Ruiz, M.Ed. and by wikiHow staff writer, Jennifer Mueller, JD . Alexander Ruiz is an Educational Consultant and the Educational Director of Link Educational Institute, a tutoring business based in Claremont, California that provides customizable educational plans, subject and test prep tutoring, and college application consulting. With over a decade and a half of experience in the education industry, Alexander coaches students to increase their self-awareness and emotional intelligence while achieving skills and the goal of achieving skills and higher education. He holds a BA in Psychology from Florida International University and an MA in Education from Georgia Southern University. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 521,205 times.

The research methodology section of any academic research paper gives you the opportunity to convince your readers that your research is useful and will contribute to your field of study. An effective research methodology is grounded in your overall approach – whether qualitative or quantitative – and adequately describes the methods you used. Justify why you chose those methods over others, then explain how those methods will provide answers to your research questions. [1] X Research source

Describing Your Methods

• In your restatement, include any underlying assumptions that you're making or conditions that you're taking for granted. These assumptions will also inform the research methods you've chosen.
• Generally, state the variables you'll test and the other conditions you're controlling or assuming are equal.

• If you want to research and document measurable social trends, or evaluate the impact of a particular policy on various variables, use a quantitative approach focused on data collection and statistical analysis.
• If you want to evaluate people's views or understanding of a particular issue, choose a more qualitative approach.
• You can also combine the two. For example, you might look primarily at a measurable social trend, but also interview people and get their opinions on how that trend is affecting their lives.

• For example, if you conducted a survey, you would describe the questions included in the survey, where and how the survey was conducted (such as in person, online, over the phone), how many surveys were distributed, and how long your respondents had to complete the survey.
• Include enough detail that your study can be replicated by others in your field, even if they may not get the same results you did. [4] X Research source

• Qualitative research methods typically require more detailed explanation than quantitative methods.
• Basic investigative procedures don't need to be explained in detail. Generally, you can assume that your readers have a general understanding of common research methods that social scientists use, such as surveys or focus groups.

• For example, suppose you conducted a survey and used a couple of other research papers to help construct the questions on your survey. You would mention those as contributing sources.

Justifying Your Choice of Methods

• Describe study participants specifically, and list any inclusion or exclusion criteria you used when forming your group of participants.
• Justify the size of your sample, if applicable, and describe how this affects whether your study can be generalized to larger populations. For example, if you conducted a survey of 30 percent of the student population of a university, you could potentially apply those results to the student body as a whole, but maybe not to students at other universities.

• Reading other research papers is a good way to identify potential problems that commonly arise with various methods. State whether you actually encountered any of these common problems during your research.

• If you encountered any problems as you collected data, explain clearly the steps you took to minimize the effect that problem would have on your results.

• In some cases, this may be as simple as stating that while there were numerous studies using one method, there weren't any using your method, which caused a gap in understanding of the issue.
• For example, there may be multiple papers providing quantitative analysis of a particular social trend. However, none of these papers looked closely at how this trend was affecting the lives of people.

Connecting Your Methods to Your Research Goals

• Depending on your research questions, you may be mixing quantitative and qualitative analysis – just as you could potentially use both approaches. For example, you might do a statistical analysis, and then interpret those statistics through a particular theoretical lens.

• For example, suppose you're researching the effect of college education on family farms in rural America. While you could do interviews of college-educated people who grew up on a family farm, that would not give you a picture of the overall effect. A quantitative approach and statistical analysis would give you a bigger picture.

• If in answering your research questions, your findings have raised other questions that may require further research, state these briefly.
• You can also include here any limitations to your methods, or questions that weren't answered through your research.

• Generalization is more typically used in quantitative research. If you have a well-designed sample, you can statistically apply your results to the larger population your sample belongs to.

Template to Write Research Methodology

Community Q&A

• Organize your methodology section chronologically, starting with how you prepared to conduct your research methods, how you gathered data, and how you analyzed that data. [13] X Research source Thanks Helpful 0 Not Helpful 0
• Write your research methodology section in past tense, unless you're submitting the methodology section before the research described has been carried out. [14] X Research source Thanks Helpful 2 Not Helpful 0
• Discuss your plans in detail with your advisor or supervisor before committing to a particular methodology. They can help identify possible flaws in your study. [15] X Research source Thanks Helpful 0 Not Helpful 0

You Might Also Like

• ↑ http://expertjournals.com/how-to-write-a-research-methodology-for-your-academic-article/
• ↑ http://libguides.usc.edu/writingguide/methodology
• ↑ https://www.skillsyouneed.com/learn/dissertation-methodology.html
• ↑ https://uir.unisa.ac.za/bitstream/handle/10500/4245/05Chap%204_Research%20methodology%20and%20design.pdf
• ↑ https://elc.polyu.edu.hk/FYP/html/method.htm

About This Article

To write a research methodology, start with a section that outlines the problems or questions you'll be studying, including your hypotheses or whatever it is you're setting out to prove. Then, briefly explain why you chose to use either a qualitative or quantitative approach for your study. Next, go over when and where you conducted your research and what parameters you used to ensure you were objective. Finally, cite any sources you used to decide on the methodology for your research. To learn how to justify your choice of methods in your research methodology, scroll down! Did this summary help you? Yes No

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• How to Write Your Methods

Ensure understanding, reproducibility and replicability

What should you include in your methods section, and how much detail is appropriate?

Why Methods Matter

The methods section was once the most likely part of a paper to be unfairly abbreviated, overly summarized, or even relegated to hard-to-find sections of a publisher’s website. While some journals may responsibly include more detailed elements of methods in supplementary sections, the movement for increased reproducibility and rigor in science has reinstated the importance of the methods section. Methods are now viewed as a key element in establishing the credibility of the research being reported, alongside the open availability of data and results.

A clear methods section impacts editorial evaluation and readers’ understanding, and is also the backbone of transparency and replicability.

For example, the Reproducibility Project: Cancer Biology project set out in 2013 to replicate experiments from 50 high profile cancer papers, but revised their target to 18 papers once they understood how much methodological detail was not contained in the original papers.

What to include in your methods section

What you include in your methods sections depends on what field you are in and what experiments you are performing. However, the general principle in place at the majority of journals is summarized well by the guidelines at PLOS ONE : “The Materials and Methods section should provide enough detail to allow suitably skilled investigators to fully replicate your study. ” The emphases here are deliberate: the methods should enable readers to understand your paper, and replicate your study. However, there is no need to go into the level of detail that a lay-person would require—the focus is on the reader who is also trained in your field, with the suitable skills and knowledge to attempt a replication.

A constant principle of rigorous science

A methods section that enables other researchers to understand and replicate your results is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail . Reproducibility is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information. If a journal still has word-limits—either for the overall article or specific sections—and requires some methodological details to be in a supplemental section, that is OK as long as the extra details are searchable and findable .

Imagine replicating your own work, years in the future

As part of PLOS’ presentation on Reproducibility and Open Publishing (part of UCSF’s Reproducibility Series ) we recommend planning the level of detail in your methods section by imagining you are writing for your future self, replicating your own work. When you consider that you might be at a different institution, with different account logins, applications, resources, and access levels—you can help yourself imagine the level of specificity that you yourself would require to redo the exact experiment. Consider:

• Which details would you need to be reminded of? 
• Which cell line, or antibody, or software, or reagent did you use, and does it have a Research Resource ID (RRID) that you can cite?
• Which version of a questionnaire did you use in your survey? 
• Exactly which visual stimulus did you show participants, and is it publicly available? 
• What participants did you decide to exclude? 
• What process did you adjust, during your work? 

Tip: Be sure to capture any changes to your protocols

You yourself would want to know about any adjustments, if you ever replicate the work, so you can surmise that anyone else would want to as well. Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal methods, or methodological constraints, than to conceal them, which could result in reproducibility or ethical issues downstream.

Visual aids for methods help when reading the whole paper

Consider whether a visual representation of your methods could be appropriate or aid understanding your process. A visual reference readers can easily return to, like a flow-diagram, decision-tree, or checklist, can help readers to better understand the complete article, not just the methods section.

Ethical Considerations

In addition to describing what you did, it is just as important to assure readers that you also followed all relevant ethical guidelines when conducting your research. While ethical standards and reporting guidelines are often presented in a separate section of a paper, ensure that your methods and protocols actually follow these guidelines. Read more about ethics .

Existing standards, checklists, guidelines, partners

While the level of detail contained in a methods section should be guided by the universal principles of rigorous science outlined above, various disciplines, fields, and projects have worked hard to design and develop consistent standards, guidelines, and tools to help with reporting all types of experiment. Below, you’ll find some of the key initiatives. Ensure you read the submission guidelines for the specific journal you are submitting to, in order to discover any further journal- or field-specific policies to follow, or initiatives/tools to utilize.

Tip: Keep your paper moving forward by providing the proper paperwork up front

Be sure to check the journal guidelines and provide the necessary documents with your manuscript submission. Collecting the necessary documentation can greatly slow the first round of peer review, or cause delays when you submit your revision.

Randomized Controlled Trials – CONSORT The Consolidated Standards of Reporting Trials (CONSORT) project covers various initiatives intended to prevent the problems of  inadequate reporting of randomized controlled trials. The primary initiative is an evidence-based minimum set of recommendations for reporting randomized trials known as the CONSORT Statement . 

Systematic Reviews and Meta-Analyses – PRISMA The Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) is an evidence-based minimum set of items focusing  on the reporting of  reviews evaluating randomized trials and other types of research.

Research using Animals – ARRIVE The Animal Research: Reporting of In Vivo Experiments ( ARRIVE ) guidelines encourage maximizing the information reported in research using animals thereby minimizing unnecessary studies. (Original study and proposal , and updated guidelines , in PLOS Biology .) 

Laboratory Protocols Protocols.io has developed a platform specifically for the sharing and updating of laboratory protocols , which are assigned their own DOI and can be linked from methods sections of papers to enhance reproducibility. Contextualize your protocol and improve discovery with an accompanying Lab Protocol article in PLOS ONE .

Consistent reporting of Materials, Design, and Analysis – the MDAR checklist A cross-publisher group of editors and experts have developed, tested, and rolled out a checklist to help establish and harmonize reporting standards in the Life Sciences . The checklist , which is available for use by authors to compile their methods, and editors/reviewers to check methods, establishes a minimum set of requirements in transparent reporting and is adaptable to any discipline within the Life Sciences, by covering a breadth of potentially relevant methodological items and considerations. If you are in the Life Sciences and writing up your methods section, try working through the MDAR checklist and see whether it helps you include all relevant details into your methods, and whether it reminded you of anything you might have missed otherwise.

Summary Writing tips

The main challenge you may find when writing your methods is keeping it readable AND covering all the details needed for reproducibility and replicability. While this is difficult, do not compromise on rigorous standards for credibility!

• Keep in mind future replicability, alongside understanding and readability.
• Follow checklists, and field- and journal-specific guidelines.
• Consider a commitment to rigorous and transparent science a personal responsibility, and not just adhering to journal guidelines.
• Establish whether there are persistent identifiers for any research resources you use that can be specifically cited in your methods section.
• Deposit your laboratory protocols in Protocols.io, establishing a permanent link to them. You can update your protocols later if you improve on them, as can future scientists who follow your protocols.
• Consider visual aids like flow-diagrams, lists, to help with reading other sections of the paper.
• Be specific about all decisions made during the experiments that someone reproducing your work would need to know.

Don’t

• Summarize or abbreviate methods without giving full details in a discoverable supplemental section.
• Presume you will always be able to remember how you performed the experiments, or have access to private or institutional notebooks and resources.
• Attempt to hide constraints or non-optimal decisions you had to make–transparency is the key to ensuring the credibility of your research.
• How to Write a Great Title
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What’s Included: Methodology Template

This template covers all the core components required in the research methodology chapter or section of a typical dissertation or thesis, including:

• The opening section
• Research philosophy
• Research type
• Research strategy
• Time horizon
• Sampling strategy
• Data collection methods
• Data analysis methods
• Conclusion & summary

The purpose of each section is explained in plain language, followed by an overview of the key elements that you need to cover. The template also includes practical examples to help you understand exactly what’s required, along with links to additional free resources (articles, videos, etc.) to help you along your research journey.

The cleanly-formatted Google Doc can be downloaded as a fully editable MS Word Document (DOCX format), so you can use it as-is or convert it to LaTeX.

PS – if you’d like a high-level template for the entire thesis, you can we’ve got that too .

What format is the template (DOC, PDF, PPT, etc.)?

The methodology chapter template is provided as a Google Doc. You can download it in MS Word format or make a copy to your Google Drive. You’re also welcome to convert it to whatever format works best for you, such as LaTeX or PDF.

What types of dissertations/theses can this template be used for?

The methodology template follows the standard format for academic research projects, which means it will be suitable for the vast majority of dissertations and theses (especially those within the sciences), whether they adopt a qualitative, quantitative, or mixed-methods approach. The template is loosely based on Saunders’ research onion , which is recommended as a methodological framework by many universities.

Keep in mind that the exact requirements for the methodology chapter/section will vary between universities and degree programs. These are typically minor, but it’s always a good idea to double-check your university’s requirements before you finalize your structure.

Is this template for an undergrad, Master or PhD-level thesis?

This template can be used for a dissertation, thesis or research project at any level of study. Doctoral-level projects typically require the methodology chapter to be more extensive/comprehensive, but the structure will typically remain the same.

How long should the methodology chapter be?

This can vary a fair deal, depending on the level of study (undergrad, Master or Doctoral), the field of research, as well as your university’s specific requirements. Therefore, it’s best to check with your university or review past dissertations from your program to get an accurate estimate. 

How detailed should my methodology be?

As a rule of thumb, you should provide enough detail for another researcher to replicate your study. This includes clear descriptions of procedures, tools, and techniques you used to collect and analyse your data, as well as your sampling approach.

How technical should my language be in this chapter?

In the methodology chapter, your language should be technical enough to accurately convey your research methods and processes, but also clear and precise to ensure it’s accessible to readers within your field.

Aim for a balance where the technical aspects of your methods are thoroughly explained without overusing jargon or overly complex language.

Should I include a pilot study in my methodology?

If you conducted a pilot study, you can include it in the methodology to demonstrate the feasibility and refinement of your methods. Be sure to obtain the necessary permissions from your research advisor before conducting any pilot studies, though. 

Can I share this template with my friends/colleagues?

Yes, you’re welcome to share this template in its original format (no editing allowed). If you want to post about it on your blog or social media, we kindly request that you reference this page as your source.

Do you have templates for the other chapters?

Yes, we do. We are constantly developing our collection of free resources to help students complete their dissertations and theses. You can view all of our template resources here .

Can Grad Coach help me with my methodology?

Yes, we can assist with your methodology chapter (or any other chapter) on a coaching basis. If you’re interested, feel free to get in touch to discuss our private coaching services .

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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Published: 14 May 2024

Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants

• Md Shafiqur Rahman   ORCID: orcid.org/0000-0003-4068-6775 1 , 2 ,
• Emma Harrison 2 , 3 ,
• Heather Biggs 2 , 3 ,
• Chloe Seikus 2 , 3 ,
• Paul Elliott   ORCID: orcid.org/0000-0002-7511-5684 4 ,
• Gerome Breen   ORCID: orcid.org/0000-0003-2053-1792 5 , 6 ,
• Nathalie Kingston 3 , 7 ,
• John R. Bradley   ORCID: orcid.org/0000-0002-7774-8805 3 , 8 ,
• Steven M. Hill   ORCID: orcid.org/0000-0002-5909-692X 1   nAff10 ,
• Brian D. M. Tom   ORCID: orcid.org/0000-0002-3335-9322 1 &
• Patrick F. Chinnery   ORCID: orcid.org/0000-0002-7065-6617 2 , 3 , 9  

Nature Medicine ( 2024 ) Cite this article

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A leading explanation for translational failure in neurodegenerative disease is that new drugs are evaluated late in the disease course when clinical features have become irreversible. Here, to address this gap, we cognitively profiled 21,051 people aged 17–85 years as part of the Genes and Cognition cohort within the National Institute for Health and Care Research BioResource across England. We describe the cohort, present cognitive trajectories and show the potential utility. Surprisingly, when studied at scale, the APOE genotype had negligible impact on cognitive performance. Different cognitive domains had distinct genetic architectures, with one indicating brain region-specific activation of microglia and another with glycogen metabolism. Thus, the molecular and cellular mechanisms underpinning cognition are distinct from dementia risk loci, presenting different targets to slow down age-related cognitive decline. Participants can now be recalled stratified by genotype and cognitive phenotype for natural history and interventional studies of neurodegenerative and other disorders.

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By 2050, approximately 139 million people are expected to have dementia worldwide 1 , 2 . Although there has been recent therapeutic progress (lecanemab 3 and donanemab 4 ), the vast majority of new treatments shown to be effective in animal studies do not benefit patients when evaluated in large-scale clinical trials 5 , 6 , 7 . Several explanations have been proposed for the translational failure, including a limited understanding of the pathophysiology and animal models that do not accurately reflect the human disorder. However, a compelling explanation is that the new drugs are genuinely effective but have been evaluated too late in the disease course to have clinically meaningful impact. Therefore, there is an urgent need to understand the disease mechanisms during the preclinical and prodromal stages of neurodegenerative diseases and test new treatments at an early stage 8 , maximizing the potential to enhance the quality of life and reduce the societal burden of disease. This requires large cohorts of participants willing to be recalled for clinical and experimental studies, but despite major international efforts, studies specifically focused on dementia are typically in the order of a few thousands with low recallable capability 9 , 10 , 11 .

The National Institute for Health and Care Research (NIHR) BioResource in England was established to facilitate the recall of volunteers keen to engage in experimental medicine and clinical trials across the whole of medicine 12 . Most of the participants are healthy, are extensively phenotyped and have genome-wide genetic data available. Recognizing the unmet need to develop treatments for neurodegenerative disorders, we partnered with patients and carers from the UK Alzheimer’s Society to design and deliver the Genes and Cognition (G&C) cohort as an open-ended study nested within the NIHR BioResource. Individuals undertook cognitive profiling and genetic testing mirroring UK Biobank (UKB), enabling targeted recall studies in 21,051 NIHR BioResource participants from the UK population for both discovery and experimental validation. This also offers an opportunity to study the dynamics of cognitive variability across the lifespan and its genetic underpinnings. In this Article, we report the demographic, cognitive and genetic data available for participant recall, including educational status, measures of deprivation, comorbidities and 13 cognitive phenotypes. To show the potential power of the resource, we determine the heritability of each cognitive phenotypes, show phenotypic and genetic correlation between cognitive phenotypes, and determine the genetic landscape for two novel measures of cognitive ability, discovering novel genetic loci influencing cognitive performance throughout the life course.

Participant data on demographics, cognition and genetics for recall

Eleven cognitive tests (Reaction test, RT; Stroop box, SB; Stroop ink, SI; Symbol digits, SD; Trail making: numeric, TMN; Trail making: alpha numeric, TMA; Matrices, MX; Quiz, QZ; Vocabulary, VY; Working memory, WM; Pairing 7, PR) spanning different cognitive domains were undertaken at the participants’ convenience using downloaded software (Fig. 1 and Methods ). The tests were those used in the Airwave study 13 adapted to work on a range of different devices. Data from 21,051 participants were available (Table 1 ). Self-reported clinical information is presented in Supplementary Table 1 , and a summary of 11 tests (phenotypes) is presented in Supplementary Tables 2 and 3 , and Extended Data Figs. 1 and 2 . Test scores from QZ (a measure of fluid intelligence), WM, MX, VY (a measure of crystallized intelligence) and SD were reversed so that higher scores indicate poorer performance, facilitating a direct comparison between all cognitive phenotypes. Those reporting a diagnosis known to affect cognition ( n  = 123) were excluded from subsequent analyses.

Diagnoses that affected cognition ( N  = 123) and participants with missing values in cognitive tests were excluded while measuring G4 and G6.

Common variance underlying cognitive tasks is known as general cognitive ability, general intelligence or g-factor 14 . We obtained two data-driven measures of general cognitive ability (G6 and G4) using principal component (PC) analysis across participants based on disjoint subsets of the cognitive phenotypes ( Methods and Extended Data Figs. 2 , 3 and 4 ). G6 corresponds to the first PC (explaining 66.5% of variation) derived from RT, SB, SI, SD, TMN and TMA ( Methods and Extended Data Fig. 3a–c ). G4 corresponds to the first PC (explaining 46.6% of variation) derived from MX, QZ, VY and WM ( Methods and Extended Data Fig. 4a–c ). All 13 cognitive phenotypes (11 cognitive tests, G4 and G6) were positively correlated with each other except VY, which was positively correlated with QZ, MX, WM, TMA and G4, and negatively correlated with the other cognitive phenotypes (Extended Data Fig. 5 ).

The majority of participants used iOS devices (46%), followed by Android (31%) and Windows (23%) devices to take the tests (Extended Data Fig. 6 ). With the exception of WM, there were systematic differences in test scores between the device types, which remained after adjusting for age and gender, possibly reflecting differences in input interface (touchscreen versus mouse; Extended Data Fig. 7 and Supplementary Table 4 ). The device type was thus factored into all subsequent analysis other than WM. Although there were differences in device use between different age, socioeconomic and educational groups (Supplementary Table 5 ), potentially influencing some of the cognitive phenotypes (except WM and PR). However, this should be borne in mind if participants are recalled on the basis of their cognitive profiles.

Available genome-wide genotype array data (based on UKB Axiom Array) confirmed the self-reported ethnicity (99.3%) in a subgroup of participants ( N  = 10,038) representative of the whole G&C cohort (Supplementary Tables 3 , 6 and 7 ).

Cognition, gender, education, deprivation and health

As expected, performance across all cognitive tests decreased with age, except VY, which increased with age (Bonferroni–Holm-adjusted P  < 0.05; Fig. 2 and Supplementary Table 8 ). Previous reports have shown that VY performance declines beyond age 60 years 15 , 16 , but this was not apparent across 20,777 NIHR BioResource participants. Males had, on average, higher SD, TMN, TMA and PR scores, and lower scores in other phenotypes when compared with females (Bonferroni–Holm-adjusted P  < 0.05; Fig. 2 and Supplementary Table 8 ) except for G6 where there was no clear evidence for a gender difference. A significant age-by-gender interaction effect was observed for SD, VY and G4 (Bonferroni–Holm-adjusted P  < 0.05; Supplementary Table 8 , model 1). An indication of age-by-gender interaction was observed for RT, SB and QZ. However, age and gender terms did not make a major contribution to the variance of WM (1.09%), QZ (1.16%) and G4 (2.53%). Although several previous studies reported differences in cognition between males and females, these have been inconsistent 17 , 18 , 19 , 20 , 21 , 22 . Here, we confirm that the overall pattern of cognitive change between males and females is strikingly similar, with gender only accounting for 0.1–1.33% of the variation in cognitive phenotypes. Adjusting for deprivation and ethnicity did not influence this analysis (Supplementary Table 8 , model 2).

a – l , Cognitive test scores for RT ( a ), SB ( b ), SI ( c ), SD ( d ), TMN ( e ), TMA ( f ), MX ( g ), WM ( h ), QZ ( i ), VY ( j ) and PR ( k ) and G4 and G6 scores ( l ) plotted against age. Lines of best fit with standard error are stratified by gender (indicated by line color). Response time is the average time taken per item.

Likewise, in keeping with previous studies 23 , the lowest two education groups had higher scores (worse performance) across all cognitive phenotypes when compared with the highest education group (Bonferroni–Holm-adjusted P  < 0.05; Supplementary Table 9 ), and there was a linear trend between cognitive performance and educational attainment (Bonferroni–Holm-adjusted P  < 0.05; Supplementary Table 9 ). All cognitive phenotypes except PR correlated with levels of multiple deprivation (Bonferroni–Holm-adjusted P  < 0.05; Extended Data Fig. 8 and Supplementary Table 10 ), with a significant linear trend indicating worse performance with higher levels of multiple deprivation (Bonferroni–Holm-adjusted P  < 0.05). Associations between cognitive profiles and self-reported health-related issues are presented in Supplementary Table 11 . Given the correlation between all of these parameters and cognition, these data have been made available for recall, allowing participants to be matched by potential confounders of cognition.

Cognitive trajectory and APOE genotype

APOE e4 allele status has a major impact on Alzheimer’s disease (AD) risk 24 . APOE genotype is also thought to influence cognition and brain activity in healthy individuals, but studies have been small, with inconsistent findings 25 , 26 , 27 , 28 , 29 . To show the utility of the NIHR BioResource G&C cohort, we determined whether APOE genotype influences cognitive performance throughout adult life.

APOE e4 carriers showed a subtle increase in RT, SB, SI, SD, TMA, G6, QZ and PR emerging in late middle age (45–64 years) and TMN in late old age (>65 years) when compared with e3 / e3 carriers (Extended Data Fig. 9 ), but this did not withstand adjustment for covariates (Supplementary Table 12 ). On further inspection of those nine cognitive phenotypes showing subtle increase, RT, SB, SI and G6 showed a trend toward having pointwise higher mean scores for e4 allele carriers after the age 45 when using categorized age (Extended Data Fig. 10 ). An age-by- APOE interaction was observed for SD and G6, where e4 carriers had higher scores than e3 / e3 carriers (uncorrected P  < 0.05), and an age 2 -by- APOE interaction effect was observed for SI, where e2 / e3 carriers had higher scores compared with e3 / e3 carriers (uncorrected P  < 0.05; Supplementary Table 12 ). Previous studies reported associations with APOE for specific age groups, including 60–65 years 30 , 31 , and between 47 and 56 years 32 , particularly for processing speed (similar to SD) and visual episodic memory (similar to PR). However, in our study, none of these associations survived correction for multiple testing. In conclusion, across the age range studied we saw no compelling evidence that APOE genotype influenced performance of the 11 established cognitive phenotypes in the 9,691 individuals where the genotype could be unambiguously called ( Methods ).

Stratification by AD polygenic risk scores

Given the interest in polygenic risk scores (PRS) in AD risk stratification, AD-PRS were calculated for participants to facilitate informed recall. AD-PRS obtained from Lambert et al. 33 , 34 were used to test whether AD genetic risk was associated with cognitive performance across the age range. Two PRS were created (Supplementary Table 13 ), one including APOE (AD-PRS APOE ) and the other without APOE (AD-PRS noAPOE ) to determine the value of non- APOE PRS in risk prediction. The 11 cognitive scores, G4 and G6 were compared between the top 5th percentile of AD-PRS (‘AD-PRS-high’ group) and the bottom 95th percentile of AD-PRS (‘AD-PRS-low’ group). For AD-PRS APOE , positive deviation in RT, SB, SI, SD, TMN, PR, QZ and G6 scores were observed for the AD-PRS APOE -high group starting between ages 55 and 65. A similar score deviation was observed around late adulthood (over 65 years) for TMA (Fig. 3 ). For AD-PRS noAPOE , a positive score deviation in RT, SB, TMN and VY was observed for the AD-PRS noAPOE -high group beginning in either late middle age or late adulthood (Supplementary Fig. 1 ). In the adjusted analysis, these score deviations did not differ between the AD-PRS APOE (Supplementary Table 14 ) and AD-PRS noAPOE groups (Supplementary Table 15 ). However, an age-by-AD-PRS APOE risk group interaction was observed for SB, SI and G6 (Supplementary Table 14 ), but only the SI association remained following multiple testing corrections (Bonferroni–Holm-adjusted P  = 0.039). Our exploratory analysis using categorized age showed that mean values for SB, SI, SD and G6 between AD-PRS APOE groups differed ( P  < 0.05) for the 60–64-year-old age category (Supplementary Fig. 2 ). No age-by-AD-PRS noAPOE risk group interaction effect was observed for RT, SB, TMN and VY (Supplementary Fig. 3 ). Thus, AD-PRS had a minimal impact on cognitive performance, with effects being noticeable only in later life. The use of AD-PRS had inferior discriminatory ability than the APOE genotype alone to identify early changes in cognitive ability.

a – l , Cognitive test scores for RT ( a ), SB ( b ), SI ( c ), SD ( d ), TMN ( e ), TMA ( f ), MX ( g ), WM ( h ), QZ ( i ), VY ( j ) and PR ( k ) and G4 and G6 scores ( l ) plotted against age. Lines of best fit with standard error are stratified by AD-PRS APOE group (indicated by line color); The ‘high’ group is the top 5th percentile of AD-PRS APOE and the ‘low’ group is the bottom 95th percentile of AD-PRS APOE . The response time is the average time taken per item.

Heritability, genetic and phenotypic correlation

Having annotated the cohort for recall studies based on cognition and genotype, we moved on to estimate single-nucleotide polymorphism (SNP) heritability for each cognitive phenotype, as well as the genetic and phenotypic correlations between these phenotypes. Based on individual-level genetic data, the heritability of each cognitive phenotype ranged from 0.06 to 0.28 ( Methods and Supplementary Tables 16 and 17 ), confirming published findings for QZ 35 , RT 36 , TMA 37 and general cognitive ability 38 . The correlations between genetic profiles associated with cognitive phenotypes were stronger than the correlations between the cognitive phenotypes themselves ( Methods and Supplementary Fig. 4a,b ).

Genome-wide association study of general cognitive ability

Given that G4 and G6 explained most of the variation seen in the individual tests (Extended Data Figs. 3 and 4 ), we conducted two genome-wide association studies (GWAS) to identify known or novel genetic loci determining general cognitive ability. Covariates included in the GWAS are listed in Supplementary Table 17 . G4 and G6 were associated with distinct genome-wide significant loci (Figs. 4a and 5a and Supplementary Fig. 5 ). There was no evidence of confounding due to population stratification (G4: λ GC  = 1.0466, linkage disequilibrium score regression (LDSR) 39 intercept 0.9974, and G6: λ GC  = 1.0466, LDSR intercept 1.0095), indicating that the different cognitive domains probably have different molecular bases. The strongest association for G4 spanned 75 SNPs ( P  < 5 × 10 −8 ) including the independent SNP, rs62034351 (intronic variant, P  = 9.1 × 10 −9 ), within CCDC101 ( SGF29 ) in a gene-dense region on chromosome 16 (Fig. 4b and Supplementary Tables 18 and 19 ). Rs62034351 explained 185-fold more of the variance in G4 (0.37%, analysis of variance (ANOVA) P  = 1.38 × 10 −8 ) than APOE (0.002%, ANOVA P  = 0.93). Four additional loci were suggestive of genome-wide association with G4 ( P  < 1 × 10 −6 ; Supplementary Table 20 ). For G6, the strongest association was on chromosome 3, with the independent SNP at this locus ( rs11705789 ; P  = 4.5 × 10 −8 ) near GBE1 (Fig. 5b and Supplementary Tables 18 and 21 ). Three additional loci were suggestive of an association with G6 (Supplementary Table 22 ). Rs11705789 explained 5.5-fold more variance in G6 (0.11%, ANOVA P  = 2.52 × 10 −5 ) than APOE (0.02%, ANOVA P  = 0.21). To validate these findings, we reviewed two previous meta-analyses of intelligence 40 , 41 . The G4/ rs62034351 discovery replicated in the same direction in both studies 40 , 41 , but the G6/ rs11705789 discovery did not replicate, possibly reflecting differences in the cognitive profiling and its contribution to G6 (Supplementary Table 23 ).

a , A Manhattan plot of the genome-wide association analysis of G4. The x axis shows SNP chromosome positions, and the y axis shows the corresponding −log 10 two-tailed P values from the two-sided BOLT infinitesimal model. The horizontal red line indicates the genome-wide significance threshold at P  = 5 × 10 −8 . The horizontal blue line indicates the suggestive genome-wide significance threshold at P  = 1 × 10 −6 . The nearest gene or top SNP is highlighted for loci associated at P  < 1 × 10 −6 . b , Regional association and LD plots for G4-associated genome-wide significant locus. The x axis shows the SNP position on the chromosome, and the y axis shows the −log 10 ( P value). The independent SNP is indicated by the purple diamond. The circles show other SNPs in pairwise LD with the independent SNP, with color indicating the strength of LD ( r 2 ). The strength of LD ( r 2 ) is presented in the upper left corner of the plot. The dashed horizontal line indicates genome-wide significant threshold. Estimated recombination rates are marked in light blue. Bottom: genes within ±200 kb of the independent SNP. c , A pie chart showing the proportion of the functional consequences of the G4-associated independent SNP and its proxies as annotated with ANNOVAR. d , Pathway and process enrichment analysis of genes mapped for G4 locus. The figure presents the top ten clusters along with their respective enriched terms (one per cluster). P values (−log 10 transformed) are computed using the cumulative hypergeometric distribution, and the most statistically significant term within each cluster is selected to represent it. e , Colocalization of G4-associated signals with microglia eQTLs at SULT1A1 (i), SULT1A2 (ii), TUFM (iii) and long noncoding RNA (lncRNA) (iv). Each colored point indicates the strength of LD (red, ≥0.8; orange, 0.6–0.8; green, 0.4–0.6; light blue, 0.2–0.4; dark blue, <0.2) with candidate SNP (purple diamond labeled with rsID). PPH4 values indicate PP in support of shared single causal variants between the traits. PPH3 values indicate PP in support of sharing different causal variants between traits. f , A bar graph showing evidence from SMR between G4-GWAS and GTEx (v8) Brain eQTLs ( cis ) for G4-associated locus. The x axis represents coefficients from SMR for associated brain tissues (indicated by color), and the y axis represents prioritized genes.

a , A Manhattan plot of the genome-wide association analysis of G6. The x axis shows SNP chromosome positions, and the y axis shows the corresponding −log 10 two-tailed P values from the two-sided BOLT infinitesimal model. The horizontal red line indicates the genome-wide significance threshold at P  = 5 × 10 −8 . The horizontal blue line indicates the suggestive genome-wide significance threshold at P  = 1 × 10 −6 . The nearest gene or top SNP is highlighted for loci associated at P  < 1 × 10 −6 . b , Regional association and LD plots for G6-associated genome-wide significant locus. The x axis shows SNP position on the chromosome, and the y axis shows −log 10 ( P value). Tick marks at the top of the plot indicate SNP position. The independent SNP is indicated by the purple diamond. The circles show other SNPs in pairwise LD with the independent SNP, with color indicating the strength of LD ( r 2 ). The strength of LD ( r 2 ) is presented in the upper left corner of the plot. Estimated recombination rates are marked in light blue. Bottom: genes within ±500 kb of the independent SNP. c , A pie chart showing the proportion of the functional consequences of G4-associated independent SNP and its proxies as annotated with ANNOVAR. d , A Manhattan plot for the GWGBA analysis of G6. The y axis shows the −log 10 -transformed two-tailed P value of each gene from a linear model, and the x axis shows the chromosomal position. The dotted red line indicates the Bonferroni-corrected threshold ( P  = 2.614 × 10 −6 ) for the genome-wide significance of the gene-based test. The gene with the lowest P value is highlighted. e , Bulk tissue expression of the GBE1 gene across tissue types from GTEx v8. The y axis represents transcripts per million (TPM), and the x axis represents the GTEx (V.8) tissues. Box plots feature the median, 25th and 75th percentiles. Points are displayed as outliers if they fall beyond 1.5 times the interquartile range. The figure was adapted from the GTEx Portal ( https://www.gtexportal.org/home/gene/GBE1 ). f , A circos plot displaying chromatin interactions (Ci) and eQTLs for rs11705789 . The outermost layer shows the Manhattan plot with −log 10 ( P value) for the G6-associated locus, and SNPs with P  < 0.05 are displayed. The LD relationship between rs11705789 and other SNPs is indicated with red ( r 2  > 0.8), orange ( r 2  > 0.6) and green ( r 2  > 0.4) colors. Gray SNPs show minimal LD with r 2  ≤ 0.20. The second circle represents the chromosome ring with coordinates, where the genomic risk locus is highlighted in blue. The third circle shows the same chromosome ring, but with Ci- and eQTL-mapped genes represented by orange and green lines, respectively. Genes mapped by both approaches are colored red.

Functional mapping of the G4 locus

SNPs in linkage disequilibrium (LD) with G4/ rs62034351 were annotated using ANNOVAR ( n  = 423). The majority of SNPs were intronic (44.3%) or intergenic (36.1%), but 14 lay within exons of which 7 were predicted to change the amino acid sequence (Fig. 4c and Supplementary Table 24 ). Thirteen SNPs (3.7%) were predicted to be deleterious (combined annotation-dependent depletion (CADD) 42 score >12.37), 17 (4%) were likely to regulate gene expression (Regulome DB 43 (RDB) score <2) and 385 (91.25%) had regulatory potential (minimum chromatin state <8). Genome-wide gene-based association (GWGBA) analysis identified 16 genes associated with G4 ( CLN3 was the highest ranked; Supplementary Fig. 6 ). Collectively, GWGBA, positional, expression quantitative trait loci (eQTL) and chromatin interaction mapping identified 128 genes for G4, including NUPR1 , ATXN2L , CCDC101 and SULT1A1 observed through all mapping strategies (Supplementary Table 25 and Supplementary Fig. 7 ).

To cast light on the mechanisms underpinning G4 we investigated tissue-specific expression of the mapped gene set for 53 specific GTEx (v8) 44 tissue types. Most of the implicated genes were downregulated across multiple tissues, particularly in the brain (Supplementary Fig. 8 ). The majority of the top 10 enriched terms identified by pathway and process analysis were immunological, with microglial response to γ-interferon being the highest ranked (Fig. 4d and Supplementary Table 26 ) and INTERFERON_GAMMA_RESPONSE being the top hallmark gene set ( P  = 3.68 × 10 −19 ; Supplementary Fig. 9 ). In keeping with this, SNPs associated with G4 also influenced the expression of TUFM , SULT1A1 and SULT1A2 in microglia (microglial eQTLs 45 ; Fig. 4e ). To investigate whether the effects of G4 were restricted to different anatomical locations in the brain, we performed summary-based Mendelian randomization (SMR) analysis using GTEx (v8) eQTL on G4-GWAS summary statistics on tissue from 12 brain regions. This indicated a potential causal link between SNVs in 11 genes (seven protein coding), including TUFM (seven brain regions), SULT1A1 (eight brain regions) and SULT1A2 (eight brain regions), and G4-cognitive phenotype through differential microglial gene expression (Fig. 4f ). Statistical fine mapping identified rs3743963 , rs11074904 , rs62031607 and rs2411453 as most plausible causal variants (Supplementary Fig. 10 ).

Functional mapping of the G6 locus

A total of 186 SNPs in LD were annotated for the G6/ rs11705789 locus. The majority of the SNPs were intergenic (Fig. 5c ). Nine SNPs (4.83%) were predicted to be deleterious, and 152 SNPs (81.72%) were identified with regulatory potential. GWGBA analysis identified GBE1 as the only associated gene (Fig. 5d ). The overall expression of GBE1 was lower in all bulk brain tissues than the other tissue types (Fig. 5e ). Independently, positional, eQTL and chromatin interaction mapping also prioritized CYP51A1P1 , RP11-359D24.1 and RP11-142L1.1 , none of which are protein coding. G6/ rs11705789 is an expression quantitative locus for GBE1 (Fig. 5f ). There was no instrumental variable available for GBE1 locus precluding SMR analysis. Statistical fine mapping showed rs12635671 , rs820270 and rs2691073 to be the likely causal variant regulating GBE1 expression.

Correlation of general cognitive ability and related phenotypes

To assess the life course stability of general cognitive ability, we examined the association of G4 and G6 with childhood 46 and adulthood 40 , 41 intelligence quotient using GWAS summary statistics. Childhood and adulthood intelligence quotient had a high genetic correlation (GC) with G4 and G6, and the estimate for G4 was higher than G6 (Supplementary Table 27 ), suggesting that fluid and crystallized intelligence domains might be less variable within an individual across the life course than processing speed and executive function. We assessed the relevance of G4 and G6 in educational attainment 47 using GC. G4 had a 2.4 times higher GC estimate with educational attainment than G6 (Supplementary Table 27 ), indicating that fluid and crystallized intelligence domains might predict better educational attainment than processing speed and executive function. We also looked for a GC between summary measures of cognitive abilities (G4 and G6) and AD 34 . A strong GC would imply a shared biological processes between two phenotypes 48 (in this instance, cognition in healthy people and AD). However, our analysis only revealed a very weak correlation between the genetic factors associated with normal cognition and genetic factors associated with AD (Supplementary Table 27 ), implying different underlying biological mechanisms.

Here, we report cross-sectional data for 11 cognitive tests and two summary statistics (G4 and G6) in 20,928 healthy individuals aged 17–85 years who participated in the newly established NIHR BioResource G&C cohort. Analyzing data at this scale confirmed well-established determinants of cognition, including age, socioeconomic status and educational status, and showed negligible differences in cognitive performance between males and females across the life course. Contrary to previous reports from smaller studies, genetic risk factors for dementia, including APOE genotype and AD-PRS, have a minimal impact on cognition in healthy individuals. However, a small effect of e4 and AD-PRS on cognitive performance in certain domains emerges in mid-life, potentially reflecting the presence of patients with early AD neuropathological changes or demographic characteristics of the study influencing the e4-mediated effect on cognition. On the other hand, our unbiased genome-wide approach identified novel risk factors for different cognitive parameters. Thus, the genetic and biological basis of cognition in healthy individuals appears to be distinct from the pathogenesis of neurodegenerative dementia, and characterizing the different molecular pathways has the potential to uncover new targets to prevent age-related cognitive decline.

For G4, which summarizes short-term memory, fluid and crystallized intelligence, our functional annotation implicated microglial-mediated immunological processes in the age-related cognitive trajectory, supporting previous circulating cytokine measurements 49 , 50 . Multiple lines of evidence implicated three plausible genes ( TUFM , SULT1A1 and SULT1A2 ) with G4. TUFM encodes the mitochondrial elongation factor Tu, which is involved in mitochondrial protein synthesis and has been implicated with cognitive trajectory 51 and AD pathology 52 . SULT1A1 (sulfotransferase family 1A member 1) and SULT1A2 (sulfotransferase family 1A member 2) encode sulfotransferase enzymes responsible for the metabolism of hormones, and xenobiotics 53 . While the functional roles of SULT1A1 and SUKT1A2 in the brain remain largely unexplored, both genes are expressed in the adult brain and are implicated in the local metabolism of catecholamines and toxin clearance 54 , 55 . However, the region is genetically complex, raising the possibility that other genes play a critical role through LD with the four likely causal SNVs: rs3743963 , rs11074904 , rs62031607 and rs2411453 . The locus also contains IL27 coding for interleukin 27, which can be both pro-inflammatory and anti-inflammatory 56 and influence microglial activation 57 . In addition, several proximal candidates have been implicated with brain function and cognition such as CLN3 58 , KIF22 59 , ALDOA , SEZ6L2 and TAOK2 60 , 61 . Functional studies are required to clarify whether these genes play a role in general cognition, but this will be very challenging because phenotypes in cellular or animal models are unlikely to closely reflect cognitive function in healthy humans as they age.

For G6, which summarizes reaction time, attention, processing speed and executive functioning, only one protein-coding gene was associated with cognition: GBE1 , which codes for 1,4-α-glucan-branching enzyme and plays a critical role in glycogen synthesis and glucose storage. Rare recessive mutations in GBE1 cause adult polyglucosan body disease, which often affects cognition including executive function 62 , 63 , 64 , and in a recent GWAS, GBE1 was implicated in musical beat synchronization 65 , which is closely related to attention and executive function (planning, organizing and controlling action). These independent observations support our findings indicating that GBE1 —and more broadly, glycogen metabolism—probably play a role in general cognitive ability. Glycogen’s presence in the brain has not been considered to be as important as glucose, but its role in cognition has attracted recent interest 66 , 67 , 68 , warranting further investigation.

The strengths of this resource include online cognitive assessment allowing rapid data collection of thousands of individuals, cognitive phenotyping covering various domains, and genotyping mirroring the UKB. However, unlike UKB, the NIHR BioResource is designed specifically for participant recall, which is now possible based on both cognitive and genetic profiles. Several limitations also require consideration. So far, the cognitive data are cross-sectional, and measurement error may have diluted associations. The cognitive tests were also device dependent. Although this was taken into account in our analysis, this could confound recall studies unless factored into subsequent designs. It is important to note that our choice of cognitive tests does not represent all possible cognitive domains, such as verbal episodic memory and visuospatial skills. In addition, our findings are based on an analysis of participants of white European background, with the majority having benefited from higher education. Thus, our findings cannot be generalized across all ethnicities with confidence at this stage. Finally, it is important to note that, other than genetic and cognitive characterization, we have not yet measured any biomarkers specific for neurodegenerative diseases. It is therefore possible that recalled participants will be no different from the background population for specific neurodegeneration biomarkers such as brain imaging. On the other hand, this emphasizes the potential utility of the NIHR BioResource for a wide range of studies beyond neurodegeneration, including age-related cognitive decline and other common human disorders.

Our analyses of APOE genotypes and AD-PRS and G4 and G6 were chosen to illustrate the potential use of the data generated through the NIHR G&C study. However, the potential for further analysis extends way beyond what has been explored so far. The participants of the NIHR BioResource G&C cohort have consented to be recalled for clinical studies and clinical data linkage from across England. Defining the principal demographic and genetic factors that explain why any two individuals differ allows careful matching of participants in early proof-of-concept clinical trials, thus reducing the risk of confounding variables influencing experimental studies. It is also possible to recall specific genetic subgroups to optimize the chance of observing a specific treatment effect based on known mechanisms of action. We are currently repeating the cognitive profiling of all participants to determine cognitive trajectories over time, expanding to include more diverse ethnic groups and carrying out long-read genome sequencing to enrich the recall potential for both academic and industry researchers. The data access procedure for the NIHR BioResource is described at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-bioresource-data-access/ , and the participant recall process for the NIHR BioResource is explained at https://bioresource.nihr.ac.uk/using-our-bioresource/apply-for-recall/ .

Study population and data collection

The G&C study is a prospective open cohort nested within the NIHR BioResource, which recruits participants from the general population and National Health Service organizations in England. The G&C study participants were recruited via NIHR BioResource with the objective of gaining insights into brain and cognitive function within healthy populations and facilitating early experimental studies in people at risk of neurodegenerative diseases such as dementia.

The NIHR BioResource operates under two separate set of ethics: a study for the recruitment of patients with rare disease (REC REF: 13/EE/0325) and a research tissue bank for the recruitment of all other participants (REC REF: 17/EE/0025). All participants of NIHR BioResource were invited to take part in the G&C study in two phases: (1) pilot phase (~June 2020 to ~August 2020) and (2) main phase (~November 2020 to ~November 2021). A total of 315 participants took part in the pilot study, and 20,869 participants participated in the main study. Combining both phases (excluding those who withdrew their consent or were missing vital information), 21,052 participants served as the study base. These participants were considered cognitively healthy at the time of recruitment for the G&C study. They donated their DNA via a blood sample and completed a questionnaire containing basic lifestyle and health-related information, including self-reported height and weight, ethnicity, current smoking status, alcohol consumption and diagnosis of certain diseases (for example, diabetes, stroke and mental health issues), all at recruitment to NIHR BioResource. Ethical approval for the G&C study was obtained from the North of Scotland Research Ethics Committee (REC REF: 19/NS/0118). All participants consented to be part of NIHR BioResource and to be recalled for future studies.

Cognitive tests and measures of general cognitive ability

The G&C study participants were invited to take online cognitive tests using the ‘Cognitive Test (v4.4.7-v5.6.7)’ application that was downloadable onto a compatible device. The ‘Cognitive Test’ application was composed of a short pretest questionnaire and ten cognitive tests (RT; SB; SI; SD; Trail making: TMN and TMA; MX; WM; QZ; VY; and PR). The total time to complete all these tests was approximately 30 min. We reversed some test scores to make the direction of all tests similar. In this work, a higher score across cognitive tests signifies poorer performance. The majority of these tests are similar to cognitive tests performed in UKB. The 4-week test–retest reliability of the UKB cognitive tests was moderate to high (range 0.40–0.83), with most showing a modest to good correlation with reference datasets 69 . A brief discussion of each test and measures of general cognitive ability (G4 and G6) is presented in Supplementary Note .

Other covariates

Information on age, gender, body mass index (BMI), self-reported ethnicity, smoking status, alcohol use and multiple deprivation index was collected centrally by NIHR BioResource. Age reflects the age at the time of cognitive testing. In this work, we used age as both a continuous and categorical variable. For the continuous use, age was centered by subtracting off the mean age in the G&C cohort, which was used to create a second-degree polynomial term. Self-reported gender was categorized as male, female and other. We categorized BMI into underweight (<18.5 kg m −2 ), healthy weight (18.5–24.9 kg m −2 ), overweight (25–29.9 kg m −2 ) and obese (≥30 kg m −2 ), following the criteria of the World Health Organization 70 . The multiple deprivation index is a relative measure of deprivation assigned to each participant on the basis of post codes 71 . Deprivation indices were available in deciles, where higher score correspond to lesser deprivation. In this study, we categorized deciles of multiple deprivations into three groups: (1) high (first three deciles), (2) medium (fourth to seventh deciles) and (3) low deprivation (eighth to tenth deciles). Information on education and participants’ first language was collected using the ‘Cognitive Test’ application. We categorized education into four groups, where the first category represents the lowest level of education and covers certificates of secondary education (CSEs)/equivalent/equivalent or other professional degrees/not specified. The second category covers A-level/O-level/national vocational qualification (NVQ)/higher national diploma (HND)/higher national certificate (HNC)/equivalent education, while the third category covers A-level/O-level/NVQ/HND/HNC/equivalent education with a professional degree and the fourth category covers college/university/equivalent professional degree.

Self-reported diagnosis

Several self-reported diagnoses were available for G&C study participants. Information on arthritis, diabetes, the presence of autism, attention-deficit/hyperactivity disorder, any heart condition, high blood pressure, mental health issues and stroke or related conditions was collected using a questionnaire centrally by NIHR BioResource. Information on color blindness, learning disability and conditions that participants thought would interfere with their cognition was collected via the ‘Cognitive Test’ application before cognitive testing.

Genotyping, imputation and quality control

DNA was extracted from whole blood and/or saliva. Aliquoted samples were sent to Affymetrix for genotyping and processing with the standard pipeline. Participants were genotyped using either Affymetrix v1.0 or v2.1 array by ThermoFisher Scientific 72 . Samples on the v1.0 and v2.1 chips were genotyped on the genome build hg37 and hg38, respectively. Before the imputation, we lifted variants on the genome build hg38 to hg37 using Liftover 73 and 708,654 variants common in both chips were used for pre-imputation quality control. Genotyped markers were used to infer genetic sex and determine European ancestry (EU) using the 1000 Genomes dataset. The multidimensional scaling approach incorporating the 1000 Genomes dataset was used to infer the genetic ethnicity of the samples. Plink 1.9 (ref. 74 ) was used for multidimensional scaling analysis 75 . Only genetically inferred EU participants were used for imputation. We applied the following filters before the imputation: minor allele frequency (<0.01), marker missingness (>0.01), individual missingness (>0.01), Hardy–Weinberg equilibrium ( P  < 1 × 10 −6 ), exclusion of individuals with extreme heterozygosity (±3 standard deviations (s.d.) from the mean heterozygosity rate) and exclusion of mono-morphic variants and those who had an allelic mismatch with Haplotype Reference Consortium (HRC) 76 . A total of 518,164 high-quality autosomal markers (genotyping rate 99.6%) were used for imputation using the HRC reference panel on the Michigan imputation server 77 . HRC consisted of whole-genome sequence data from cohorts of EU ancestry, providing large coverage for the common genetic variants in European ancestry population. To analyze the samples with genetic data, we excluded participants for whom there was a mismatch between genetically inferred sex and self-reported gender. To account for population stratification, 20 genetic PCs were created using post-imputation quality-controlled data, implemented on Plink 1.9 (ref. 74 ).

Identification of APOE alleles

We use rs429358 and rs7412 to determine APOE alleles 78 . Both SNPs were imputed in our data. We used the method specified at GitHub ( https://github.com/neurogenetics/APOE_genotypes ). There were 279 participants for whom the APOE allele was ambiguous or unknown, and these were therefore excluded. In the remaining sample, the proportion of e2 / e2 , e2 / e3 , e3 / e3 , e3 / e4 and e4 / e4 carriers was 0.01 ( n  = 69), 0.13 ( n  = 1,238), 0.61 ( n  = 5,931), 0.24 ( n  = 2,304) and 0.02 ( n  = 218), respectively. We combined e4 carriers into one group ( e3 / e4 and e4 / e4 , n  = 2,522) and e2 carriers into another group ( e2 / e2 and e2 / e3 , n  = 1,307).

Derivation of AD-PRS

The PRS provides an individual-level estimate of genetic liability for any given phenotype. The PRS is measured by combining weighted effect sizes (odds ratios or β ) of multiple SNPs into one score, where weights are obtained from previous GWAS performed for that phenotype of interest 79 . The most widely used PRS for AD is obtained from Lambert et al. 34 study, which included EU ancestry participants. We used previously created PRS based on Lambert et al. 34 from the polygenic score (PGS) catalog. The PGS ID PGS002289 included 23 SNPs, of which rs11218343 , rs670139 and rs8093731 were not available for G&C study participants. Of the 20 available SNPs, rs429358 and rs7412 represent APOE. We created two PRS using PGS ID PGS002289 (refs. 32 , 33 ), (1) AD-PRS APOE : including 20 SNPs (2 APOE SNPs included) and (2) AD-PRS noAPOE : including 18 SNPs (without APOE SNPs). Assuming an additive model, both PRS were computed using PRSice-2 (v2.3.3) 80 with the ‘--score std’ and ‘--missing MEAN_IMPUTE’ settings. For both PRS (AD-PRS APOE and AD-PRS noAPOE ), we categorized participants into high-risk (values >95th percentile) and low-risk (values ≤95th percentile) groups (AD-PRS-high and AD-PRS-low).

Statistical Analysis

Demographics, clinical characteristics and scores for 13 cognitive phenotypes (11 cognitive tests, G4 and G6) are presented for both the whole sample and a subset with available genetic data. Categorical data were presented as proportions, while continuous data were summarized using mean, median, s.d. or interquartile range. A small number of individuals ( n  = 123 out of 21,051) were excluded because they had a medical disorder or disability that could bias the effect estimates. The phenotypic correlation between cognitive phenotypes was measured using Pearson correlation (the whole sample and a subset with available genetic data). The association between 13 cognitive phenotypes and devices used (iOS device user served as reference category) to take cognitive tests was examined using a linear regression model with further adjustment for age and gender. Age and gender effects on cognitive phenotypes were measured, excluding those who self-identified as ‘other’ ( N  = 61). Trajectories of each cognitive phenotype (11 tests, G4 and G6) were plotted across age, stratified by gender, using the ‘geom_smooth’ function from the ggplot2 package in R with the ‘method’ argument set to ‘loess’. The associations of cognitive phenotypes were assessed in relation to age and gender. While testing associations, age (centered), age 2 , gender, an interaction term for age-by-gender and age 2 -by-gender, and devices used to take cognitive tests (except WM) were considered as covariates in a stepwise linear regression model using the ‘stepAIC’ function with both forward and backward selection implemented with MASS package in R to choose the best model for each cognitive phenotype. Henceforth, variables selected using stepwise regression (base model) remained consistent for each cognitive phenotype while testing association in relation to other factors, unless stated otherwise. Additionally, base models were adjusted for self-reported ethnicity and multiple deprivation. Since self-reported ethnicity and multiple deprivation had negligible effects on the cognitive phenotypes, none of the associations tested from this point onward included those factors. We assessed the association for cognitive phenotypes with education and multiple deprivation using linear regression model adjusting for the cognitive phenotype-specific base model. A linear trend in the association between cognitive phenotypes and both education and multiple deprivation was also examined. The association between cognitive phenotypes and self-reported diagnosis was explored using the linear regression model, which was adjusted for age terms, gender and device used to take the test. The association of cognitive phenotypes with age, gender, education, multiple deprivation and the self-reported diagnosis was corrected using the Bonferroni–Holm correction for 13 tests (considering the 13 cognitive phenotypes). The terms age 2 , age-by-gender, or age 2 -by-gender were corrected (Bonferroni–Holm) in accordance with the number of times they were subjected to testing against cognitive phenotypes.

Each cognitive phenotype was plotted against age, with the smooth line fitted and stratified by the APOE allele. Following visual inspection, nine cognitive phenotypes were selected to undergo testing for their association with age term(s) and APOE utilizing the linear mixed-effects model adjusting for sex (genetically determined), devices used for cognitive tests, genotyping batch as a random effect, genotyping array and first five genomic PCs. We used e3 / e3 carriers as a reference while assessing the association between cognitive phenotypes and APOE . The model also examined the interaction effect between age term(s) and APOE on cognitive phenotypes. The results of the linear mixed-effects model were corrected using Bonferroni–Holm correction for nine tests. Furthermore, the mean difference in all nine cognitive phenotypes across different age groups was explored using the ANOVA test.

The correlation between PRSs was measured using Pearson correlation. Cognitive phenotype trajectories across the age continuum (fitted smooth line) were inspected for an indication of score deviation in the AD-PRS-high group compared with the AD-PRS-low group. Based on the observations, the association for candidate cognitive phenotypes in relation to age term(s) and AD-PRS group (high versus low) was examined using the linear mixed-effects model adjusting for sex, devices used for cognitive tests, genotyping batch as a random effect, genotyping array and the first five genomic PCs. The model also assessed the interaction effect of age term(s) and AD-PRS group for each cognitive phenotype. The findings were presented following the Bonferroni–Holm correction. Based on the outcome of linear mixed-effects models, the mean difference in four cognitive phenotypes (for each PRS) were explored across age groups between AD-PRS-high and AD-PRS-low groups (based on AD-PRS APOE and AD-PRS noAPOE ) using t -tests.

Heritability and GC analysis

We used individual-level genetic data to estimate SNP heritability and GC for 13 cognitive phenotypes. SNP heritability for cognitive phenotypes was estimated using BOLT-REML (V.2.4) 81 , 82 . Covariates adjusted in the heritability analysis are specified in Supplementary Table 17 . GC between cognitive phenotypes was measured using Bivariate GREML analysis on GCTA (v1.94.1) 83 . Before the analysis, we removed related individuals using the ‘--grm-cutoff’ value of 0.125. For each cognitive phenotype, residuals were obtained from the separate linear regression model adjusted for covariates (except batch, genotyping chips and genetic PCs) specified in Supplementary Table 17 . These residuals were used for GC analysis, which was adjusted for batch, genotyping chips and the first ten genomic PCs as covariates. Moreover, we measured summary statistics based GC for G4 and G6 in relation to childhood 46 and adulthood 40 , 41 intelligence, educational attainment 47 and AD 34 using LDSR (v1.0.1) 39 . Precomputed LD scores based on 1000 Genomes European data restricted to HapMap release-3 SNPs ( n  = 1,217,311) were used to calculate SNP heritability and GCs. Precomputed LD scores and the list of HapMap3 SNPs were obtained from https://data.broadinstitute.org/alkesgroup/LDSCORE/eur_w_ld_chr.tar.bz2 and https://data.broadinstitute.org/alkesgroup/LDSCORE/w_hm3.snplist.bz2 .

GWAS of general cognitive ability

We performed GWAS on G6 and G4 using the linear mixed model implemented in BOLT-LMM (V.2.3.6) 81 , which accounts for population structure and cryptic relatedness. These analyses were performed assuming an additive SNP effect on both phenotypes. Covariates adjusted for in the genome-wide association analysis of G4 and G6 are specified in Supplementary Table 17 . We applied the following filters for the genome-wide association analysis of G4 and G6: minor allele frequency ≥0.05, imputation quality scores (INFO) ≥0.50, and HWE threshold P value <1 × 10 −6 . A P -value threshold of 5 × 10 −8 (for suggestive significance, P value <1 × 10 −6 ) was used to determine genome-wide significance. LDSR (v1.0.1) 39 was used to assess inflation ( λ GC ) and to distinguish confounding from polygenicity in GWAS summary statistics. SNPs with P value <1 × 10 −5 at each genome-wide significant locus were considered to identify independent SNP at r 2  ≥ 0.4 using the publicly available web-based application FUMA (functional mapping and annotation) 84 . We measured the percentage of variance explained in G4 by the rs62034351 and APOE using linear regression models that included age, age 2 , sex, age-by-sex interaction, batch, array and first five genomic PCs as covariates. Likewise, the variance explained in G6 by rs11705789 and APOE was measured using linear regression models that included age, age 2 , sex, batch, array and first five genomic PCs as covariates. Model significances were examined by comparing with the model that included all relevant covariates using ANOVA.

Replication of G&C locus in UKB

For the replication of the G4- and G6-associated locus (SNP with lowest P value considered), we used previously published GWAS studies by Sniekers et al. 41 and Savage et al. 40 . Sniekers and colleagues 41 performed a genome-wide association meta-analysis on human intelligence using 78,308 European descent individuals from 13 cohorts where phenotype was either Spearman’s g or a measure of fluid intelligence. The majority of the study participants ( N  = 54,119) were from UKB. For these participants, only fluid intelligence (either touchscreen or web-based) test score was used, which was considered to correlate highly with g (ref. 85 ). We obtained summary statistics for Sniekers et al. 41 from http://ctg.cncr.nl/software/summary_statistics . The Savage et al. 40 study performed a genome-wide association meta-analysis in 269,867 European descent individuals from 14 cohorts where various cognitive phenotypes were used to measure intelligence. Most of the study participants (72.5%) were obtained from UKB ( N  = 195,653), for which either touchscreen or web-based fluid intelligence test scores were used. Savage et al. 40 summary statistics were obtained from https://ctg.cncr.nl/ . In both intelligence GWAS studies 40 , 85 , the imputation of participating cohorts varied. However, the authors provided no details regarding the direction of test scores across participating cohorts. Given that UKB forms the large majority of their participants and fluid intelligence measures were used for UKB-GWAS, we can assume that, overall, a higher score for the phenotype in both studies meant better performance. In contrast, we performed GWAS on G4 and G6, where a higher score meant poor performance. To resolve confusion, we reported replication findings from Sniekers et al. 41 and Savage et al. 40 , harmonizing the summary statistics in line with the G&C study.

Functional annotation

FUMA 84 was used to annotate genome-wide significant loci for G4 and G6. SNP2GENE function in FUMA was used to annotate SNPs and prioritize genes at each locus using gene-based association analysis (implemented in MAGMA 86 ) and three gene mapping strategies (positional, eQTL and chromatin interaction). ANNOVAR 87 implemented in FUMA 84 annotated SNPs (minimum minor allele frequency threshold set at 0.0001) in LD with independent SNP within a 250 kb window based on the 1000 Genome Phase3 reference panel. SNPs with CADD scores >12.37 are predicted to be pathogenic, RDB scores <2 are predicted to have a regulatory function and chromatin state ≥7 indicates open chromatin region.

Gene mapping strategies

ANNOVAR 87 -annotated SNPs were used to prioritize genes on the basis of positional, eQTL and chromatin interaction mapping. Positional mapping considered a 10 kb window from the human reference assembly GRCh37/hg19 to map each SNP to genes. For eQTL mapping, SNPs were mapped to eQTL data repositories available by default to annotate SNP effect on gene expression at a false discovery rate threshold <0.05. For chromatin interaction mapping, SNPs were linked to chromatin interaction data available by default to map SNP to gene promoter regions (250 bp upstream and 500 bp downstream of the transcription start site). Also, we opted for annotating enhancer/promoter regions based on Roadmap 111 epigenomes and filtered SNPs overlapping with those regions. A false discovery rate threshold <1 × 10 −6 was used to detect significant interaction. In addition, we performed GWGBA analysis implemented with MAGMA 86 to prioritize genes for each genome-wide significant locus where all SNPs from GWAS summary data were mapped to 19,128 protein-coding genes. Genome-wide significance was defined at P value of 0.05/19,128 = 2.614 × 10 −6 .

Tissue specificity and gene expression

Genes prioritized using all mapping strategies (positional, eQTL, chromatin interaction and GWGBA) were used for tissue specificity analysis using the GENE2FUNC option on FUMA 84 . For G4, tissue specificity analysis was performed using predefined differentially expressed gene (DEG) sets for GTEx v8 54 tissue 44 . The gene set was characterized as (1) upregulated DEG, (2) downregulated DEG and (3) DEG, both sides. All FUMA-mapped genes were used as input to test each DEG using default parameters. For G6, bulk tissue gene expression for GBE1 across GTEx v8 (ref. 44 ) tissues were visualized using GTEx Portal ( https://www.gtexportal.org/home/gene/GBE1 ).

Gene-set enrichment

FUMA 84 -mapped genes for G4 were used for pathway and process enrichment analysis using ‘Metascape’ ( http://metascape.org/ ) 88 with input and analysis species set to Homo sapiens . Of the 128 genes, Metascape considered 106 genes for the enrichment analysis. The following ontology sources were used in the analysis: KEGG Pathway, GO Molecular Functions, GO Cellular Components, GO Biological Processes, Immunologic Signatures, Oncogenic Signatures, Reactome Gene Sets, Hallmark Gene Sets, Canonical Pathways, Chemical and Genetic Perturbations, BioCarta Gene Sets, CORUM and WikiPathways. We used default Metascape settings. All genes in the genome were used as background for the enrichment in Metascape 88 . Metascape findings were validated using GENE2FUNCTION option on FUMA 84 .

Colocalization

We examined evidence of shared colocalization between microglia eQTL and G4-associated significant locus at the level of individual genes within a 1 MB window around GWAS-independent SNP. Meta-analyzed (random effects) eQTL summary statistics (out_mfg_stg_svz_tha.metasoft.gz) of four microglial brain regions (medial frontal gyrus, superior temporal gyrus, thalamus and subventricular zone) with random effects were used for colocalization and downloaded from Zenodo ( https://doi.org/10.5281/zenodo.4118676 ). We used a Bayesian colocalization method (COLOC 89 ) assuming one single causal variant underlying the locus. A total of five hypotheses were tested to evaluate colocalization: H0, there is no causal variant for both traits (PP0); H1 or H2, causal variant associated with either trait 1 or trait 2 (PP1 or PP2); H3, two independent causal variants for trait 1 and trait 2 (PP3); H4, one single causal variant associated with both traits (PP4). COLOC generates a posterior probability (PP) for each hypothesis, with higher values indicating the degree to which we favor a hypothesis. A higher PP for H3 (PP3) supports the presence of two independent variants for both traits. A higher PP for H4 (PP4) supports the presence of single independent variants affecting both traits. We considered thresholds of PP H4 (PP4) ≥0.5 for suggestive, ≥0.7 for moderate and ≥0.8 for strong colocalization, respectively.

SMR and HEIDI analysis

The SMR method uses principals of Mendelian randomization to integrate summary-level data of an exposure (for example, gene expression) and outcome (that is, intelligence) to test for an association between the two due to a shared and potentially causal variant at a locus 90 . We used SMR to prioritize brain regions and genes associated with G4. We retained 2 Mb regions around GWAS independent SNPs for the analysis where cis -eQTLs from 12 GTEx (version 8) brain regions were used as the instrumental variable, gene expression of each brain region as exposure and G4 as the outcome. For each gene, heterogeneity in dependent instruments (HEIDI) 90 test was performed, which distinguishes pleiotropy (that is, gene expression and G4 are associated owing to a single shared genetic variant) from linkage (that is, two variants in LD independently affecting gene expression and G4). We performed SMR and HEIDI analysis on the Complex-Traits Genetics Virtual Lab 91 platform. Threshold levels of significance for SMR tests were adjusted for multiple comparisons by Bonferroni correction ( P SMR  < 0.05/number of genes in each eQTL analysis). Genes with P HEIDI  < 0.05 were considered as linkage and removed.

Statistical fine mapping

We performed statistical fine mapping of G4- and G6-associated locus. First, GWAS-associated regions were analyzed using GCTA-COJO (v1.94.1) 92 to identify conditionally independent lead variants. All variants within a 1 MB window of the lead variant were analyzed using FINEMAP (v1.4.2) 93 , a Bayesian fine-mapping method, to identify high-confidence putative causal SNPs for G4 and G6. We allowed for a maximum number of five causal variants for fine mapping. FINEMAP calculates PPs and assigns a Bayes factor to each variant. We considered variants with PP >0.95 and log 10 Bayes factor ≥2 as plausibly causal.

Reporting summary

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

Data availability

Summary statistics for G4 and G6 GWAS were deposited in Zenodo at https://doi.org/10.5281/zenodo.10836380 (ref. 94 ). Other data relevant to the study are included in the article or uploaded as online supplementary information. NIHR BioResource holds individual-level genetic and phenotypic data for genes and cognitive study participants that can be accessed through https://bioresource.nihr.ac.uk/using-our-bioresource/ .

Code availability

All software used in this study is publicly available. The codes used for cognitive data cleaning are available on GitHub ( https://github.com/shafiqnoa/Genes-and-Cognition-Phase-1/tree/main/Phase1_Cognitive_Data_Clean ).

Wolters, F. J. & Ikram, M. A. Epidemiology of dementia: the burden on society, the challenges for research. Methods Mol. Biol. 1750 , 3–14 (2018).

Article   CAS   PubMed   Google Scholar  

Dementia (World Health Organization, 2017).

van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388 , 9–21 (2022).

Article   PubMed   Google Scholar  

Mintun, M. A. et al. Donanemab in early Alzheimer’s disease. N. Engl. J. Med. 384 , 1691–1704 (2021).

Peters, R. et al. The epidemiology is promising, but the trial evidence is weak. Why pharmacological dementia risk reduction trials haven’t lived up to expectations, and where do we go from here? Alzheimers Dement. 18 , 507–512 (2022).

Anderson, R. M., Hadjichrysanthou, C., Evans, S. & Wong, M. M. Why do so many clinical trials of therapies for Alzheimer’s disease fail? Lancet 390 , 2327–2329 (2017).

Yiannopoulou, K. G., Anastasiou, A. I., Zachariou, V. & Pelidou, S.-H. Reasons for failed trials of disease-modifying treatments for alzheimer disease and their contribution in recent research. Biomedicines 7 , 97 (2019).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Irwin, K., Sexton, C., Daniel, T., Lawlor, B. & Naci, L. Healthy aging and dementia: two roads diverging in midlife? Front. Aging Neurosci. 10 , 275 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Ropacki, M. T., Arrighi, M., Middleton, L. & Perneczky, R. P1-320: cognitive health in ageing register: investigational, observational, and trial studies in dementia research (CHARIOT)—prospective readiness cohort study (PRO). Alzheimer’s Dement. 11 , P479 (2015).

Article   Google Scholar  

Ritchie, C. W. & Ritchie, K. The PREVENT study: a prospective cohort study to identify mid-life biomarkers of late-onset Alzheimer’s disease. BMJ Open 2 , e001893 (2012).

Sellami, L. et al. Distinct neuroanatomical correlates of neuropsychiatric symptoms in the three main forms of genetic frontotemporal dementia in the GENFI cohort. J. Alzheimer’s Dis. 65 , 147–163 (2018).

Google Scholar  

National Institute for Health and Care Research (NIHR) BioResource https://bioresource.nihr.ac.uk/

Elliott, P. et al. The Airwave Health Monitoring Study of police officers and staff in Great Britain: rationale, design and methods. Environ. Res. 134 , 280–285 (2014).

Johnson, W., Nijenhuis, J. T. & Bouchard, T. J. Still just 1 g : consistent results from five test batteries. Intelligence 36 , 81–95 (2008).

Salthouse, T. A. Trajectories of normal cognitive aging. Psychol. Aging 34 , 17–24 (2019).

Salthouse, T. A. Quantity and structure of word knowledge across adulthood. Intelligence 46 , 122–130 (2014).

Levine, D. A. et al. Sex differences in cognitive decline among US adults. JAMA Netw. Open 4 , e210169 (2021).

Majeres, R. L. Sex differences in symbol-digit substitution and speeded matching. Intelligence 7 , 313–327 (1983).

Siedlecki, K. L., Falzarano, F. B. & Salthouse, T. Examining gender differences in neurocognitive functioning across adulthood. J. Int. Neuropsychol. Soc. 25 , 1051–1060 (2019).

Jäncke, L. Sex/gender differences in cognition, neurophysiology, and neuroanatomy. F1000Res 7 , F1000 Faculty Rev-805 (2018).

Weber, D., Skirbekk, V., Freund, I. & Herlitz, A. The changing face of cognitive gender differences in Europe. Proc. Natl Acad. Sci. 111 , 11673–11678 (2014).

Bloomberg, M. et al. Sex differences and the role of education in cognitive ageing: analysis of two UK-based prospective cohort studies. Lancet Public Health 6 , e106–e115 (2021).

Deary, I. J. & Johnson, W. Intelligence and education: causal perceptions drive analytic processes and therefore conclusions. Int. J. Epidemiol. 39 , 1362–1369 (2010).

Karch, C. M., Cruchaga, C. & Goate, A. M. Alzheimer’s disease genetics: from the bench to the clinic. Neuron 83 , 11–26 (2014).

Rawle, M. J. et al. Apolipoprotein-E (Apoe) ε4 and cognitive decline over the adult life course. Transl. Psychiatry 8 , 18 (2018).

Hayden, K. M. et al. Effects of family history and apolipoprotein E ε4 status on cognitive decline in the absence of Alzheimer dementia: the Cache County Study. Arch. Neurol. 66 , 1378–1383 (2009).

Bunce, D., Fratiglioni, L., Small, B. J., Winblad, B. & Bäckman, L. APOE and cognitive decline in preclinical Alzheimer disease and non-demented aging. Neurology 63 , 816–821 (2004).

Reas, E. T. et al. Effects of APOE on cognitive aging in community-dwelling older adults. Neuropsychology 33 , 406–416 (2019).

Fernández, A., Vaquero, L., Bajo, R., Zuluaga, P. & Apolipoprotein, E. ɛ4-related effects on cognition are limited to the Alzheimer’s disease spectrum. Geroscience 44 , 195–209 (2022).

Caselli, R. J. et al. Longitudinal modeling of age-related memory decline and the APOE ε4 effect. N. Engl. J. Med. 361 , 255–263 (2009).

Vasiljevic, E. et al. Cognitive trajectories diverge by genetic risk in a preclinical longitudinal cohort. Alzheimers Dement. 19 , 3108–3118 (2023).

Zimmerman, S. C. et al. Association of genetic variants linked to late-onset alzheimer disease with cognitive test performance by midlife. JAMA Netw. Open 5 , e225491 (2022).

Lambert, S. A. et al. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. Nat. Genet. 53 , 420–425 (2021).

Lambert, J.-C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45 , 1452–1458 (2013).

Ge, T., Chen, C.-Y., Neale, B. M., Sabuncu, M. R. & Smoller, J. W. Phenome-wide heritability analysis of the UK Biobank. PLoS Genet. 13 , e1006711 (2017).

Davies, G. et al. Genome-wide association study of cognitive functions and educational attainment in UK Biobank ( N  = 112 151). Mol. Psychiatry 21 , 758–767 (2016).

Hagenaars, S. P. et al. Genetic contributions to Trail Making Test performance in UK Biobank. Mol. Psychiatry 23 , 1575–1583 (2018).

Davies, G. et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat. Commun. 9 , 2098 (2018).

Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47 , 291–295 (2015).

Savage, J. E. et al. Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence. Nat. Genet. 50 , 912–919 (2018).

Sniekers, S. et al. Genome-wide association meta-analysis of 78,308 individuals identifies new loci and genes influencing human intelligence. Nat. Genet. 49 , 1107–1112 (2017).

Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47 , D886–D894 (2019).

Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 22 , 1790–1797 (2012).

Aguet, F. et al. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318–1330 (2020).

Article   CAS   Google Scholar  

Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53 , 861–868 (2021).

Benyamin, B. et al. Childhood intelligence is heritable, highly polygenic and associated with FNBP1L. Mol. Psychiatry 19 , 253–258 (2014).

Lee, J. J. et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat. Genet. 50 , 1112–1121 (2018).

van Rheenen, W., Peyrot, W. J., Schork, A. J., Lee, S. H. & Wray, N. R. Genetic correlations of polygenic disease traits: from theory to practice. Nat. Rev. Genet. 20 , 567–581 (2019).

Keenan, A. W. et al. Systemic inflammation during midlife and cognitive change over 20 years. Neurology 92 , e1256 (2019).

Teemu, K. et al. Association of midlife inflammatory markers with cognitive performance at 10-year follow-up. Neurology 99 , e2294 (2022).

Wingo, A. P. et al. Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age. Nat. Commun. 10 , 1619 (2019).

Zhong, B.-R. et al. TUFM is involved in Alzheimer’s disease-like pathologies that are associated with ROS. FASEB J. 35 , e21445 (2021).

Glatt, H. & Meinl, W. Pharmacogenetics of soluble sulfotransferases (SULTs). Naunyn-Schmiedeberg’s Arch. Pharmacol. 369 , 55–68 (2004).

Asai, Y., Sakakibara, Y., Kondo, M., Nadai, M. & Katoh, M. Expression and activities of sulfotransferase in rat brain. Xenobiotica 49 , 270–275 (2019).

Salman, E. D., Kadlubar, S. A. & Falany, C. N. Expression and localization of cytosolic sulfotransferase (SULT) 1A1 and SULT1A3 in normal human brain. Drug Metab. Dispos. 37 , 706–709 (2009).

Tait Wojno, E. D., Hunter, C. A. & Stumhofer, J. S. The immunobiology of the interleukin-12 family: room for discovery. Immunity 50 , 851–870 (2019).

Nortey, A. N., Garces, K. N. & Hackam, A. S. Exploring the role of interleukin-27 as a regulator of neuronal survival in central nervous system diseases. Neural Regen. Res 17 , 2149–2152 (2022).

Kuper, W. F. E. et al. Timing of cognitive decline in CLN3 disease. J. Inherit. Metab. Dis. 41 , 257–261 (2018).

Park, S. M., Littleton, J. T., Park, H. R. & Lee, J. H. Drosophila homolog of human KIF22 at the autism-linked 16p11.2 loci influences synaptic connectivity at larval neuromuscular junctions. Exp. Neurobiol. 25 , 33–39 (2016).

Kumar, R. A. et al. Recurrent 16p11.2 microdeletions in autism. Hum. Mol. Genet. 17 , 628–638 (2008).

Girirajan, S. et al. 16p12.2 Recurrent Deletion. GeneReviews https://www.ncbi.nlm.nih.gov/books/NBK274565/ (2018).

Zebhauser, P. T. et al. Characterization of cognitive impairment in adult polyglucosan body disease. J. Neurol. 269 , 2854–2861 (2022).

Mochel, F. et al. Adult polyglucosan body disease: natural history and key magnetic resonance imaging findings. Ann. Neurol. 72 , 433–441 (2012).

Hellmann, M. A. et al. Frequent misdiagnosis of adult polyglucosan body disease. J. Neurol. 262 , 2346–2351 (2015).

Niarchou, M. et al. Genome-wide association study of musical beat synchronization demonstrates high polygenicity. Nat. Hum. Behav. 6 , 1292–1309 (2022).

Waitt, A. E., Reed, L., Ransom, B. R. & Brown, A. M. Emerging roles for glycogen in the CNS. Front. Mol. Neurosci. 10 , 73 (2017).

O’Dowd, B. S., Gibbs, M. E., Ng, K. T., Hertz, E. & Hertz, L. Astrocytic glycogenolysis energizes memory processes in neonate chicks. Brain Res. Dev. Brain Res. 78 , 137–141 (1994).

Rich, L. R., Harris, W. & Brown, A. M. The role of brain glycogen in supporting physiological function. Front. Neurosci. 13 , 1176 (2019).

Fawns-Ritchie, C. & Deary, I. J. Reliability and validity of the UK Biobank cognitive tests. PLoS ONE 15 , e0231627 (2020).

A Healthy Lifestyle—WHO Recommendations (World Health Organization, 2010).

Radburn, R. & Beecham, R. Mapping deprivation for each and every small area in England. Reg. Stud. Reg. Sci. 8 , 269–272 (2021).

Genetic Data from SNP Chips/Arrays and Whole Exome Sequencing (NIHR, 2023).

Hinrichs, A. S. et al. The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34 , D590–D598 (2006).

Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81 , 559–575 (2007).

Li, Q. & Yu, K. Improved correction for population stratification in genome‐wide association studies by identifying hidden population structures. Genet. Epidemiol. 32 , 215–226 (2008).

McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48 , 1279–1283 (2016).

Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48 , 1284–1287 (2016).

Husain, M. A., Laurent, B. & Plourde, M. APOE and Alzheimer’s disease: from lipid transport to physiopathology and therapeutics. Front. Neurosci. 15 , 630502 (2021).

Choi, S. W., Mak, T. S. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15 , 2759–2772 (2020).

Choi, S. W. & O’Reilly, P. F. PRSice-2: polygenic risk score software for biobank-scale data. Gigascience 8 , giz082 (2019).

Loh, P.-R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47 , 284–290 (2015).

Loh, P. R. et al. Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis. Nat. Genet. 47 , 1385–1392 (2015).

Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet 88 , 76–82 (2011).

Watanabe, K., Taskesen, E., van Bochoven, A. & Posthuma, D. Functional mapping and annotation of genetic associations with FUMA. Nat. Commun. 8 , 1826 (2017).

Deary, I. J., Penke, L. & Johnson, W. The neuroscience of human intelligence differences. Nat. Rev. Neurosci. 11 , 201–211 (2010).

de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11 , e1004219 (2015).

Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38 , e164 (2010).

Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10 , 1523 (2019).

Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10 , e1004383 (2014).

Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48 , 481–487 (2016).

Cuéllar-Partida, G. et al. Complex-Traits Genetics Virtual Lab: a community-driven web platform for post-GWAS analyses. Preprint at bioRxiv https://doi.org/10.1101/518027 (2019).

Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44 , 369–375 (2012).

Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32 , 1493–1501 (2016).

Rahman, M. S. et al. Genome-wide association summary statistics for G4 and G6. Zenodo https://doi.org/10.5281/zenodo.10836380 (2024).

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Acknowledgements

The Alzheimer’s Society UK and NIHR BioResource supported this study. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centers, NHS Trusts and staff for their contribution. We also thank B. Plumpton and colleagues for providing valuable assistance in interpretation and oversight as an Alzheimer’s Society Research Network Volunteer, and M. J. Keogh for early discussions that led to the study. M. Mangino (King’s College London) advised on the imputation of genetic data. J. Asimit (University of Cambridge) advised on the imputation of genetic data and fine mapping. D. S. Robertson (University of Cambridge) advised on the correction for multiple testing. Alzheimer’s Society, UK and NIHR BioResource supported this study. R. Elliott (Imperial College London) developed the ‘Cognitive Test’ application for online cognitive data collection in the NIHR BioResource. C. Starr (University of Cambridge) arranged access to the Cambridge high-performance computing service. We thank the National Institute for Health and Care Research, NHS Blood and Transplant, and Health Data Research UK as part of the Digital Innovation Hub Programme. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was primarily supported by the Alzheimer’s Society (AS-PG-18b-022) and the NIHR BioResource. The funders had no role in the design of the study or the interpretation of the findings. P.F.C. was a Wellcome Trust Principal Research Fellow during this study (212219/Z/18/Z) and is currently funded by a Wellcome Discovery Award (226653/Z/22/Z), a Wellcome Collaborative Award (224486/Z/21/Z), the Medical Research Council Mitochondrial Biology Unit (MC_UU_00028/7), the Biological and Biotechnology Research Council (BB/Y003209/1) and the LifeArc Centre to Treat Mitochondrial Diseases (LAC-TreatMito). B.D.M.T. is supported by the UKRI Medical Research Council (MC_UU_00002/21).

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Steven M. Hill

Present address: Cancer Research UK National Biomarker Centre, University of Manchester, Manchester, UK

Authors and Affiliations

MRC Biostatistics Unit, University of Cambridge, Cambridge, UK

Md Shafiqur Rahman, Steven M. Hill & Brian D. M. Tom

Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK

Md Shafiqur Rahman, Emma Harrison, Heather Biggs, Chloe Seikus & Patrick F. Chinnery

National Institute for Health and Care Research BioResource, Cambridge, UK

Emma Harrison, Heather Biggs, Chloe Seikus, Nathalie Kingston, John R. Bradley & Patrick F. Chinnery

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

Paul Elliott

Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Gerome Breen

UK National Institute for Health Research Biomedical Research Centre for Mental Health, South London and Maudsley Hospital, London, UK

Dept of Haematology, Cambridge University, Cambridge, UK

Nathalie Kingston

Department of Medicine, University of Cambridge, Cambridge, UK

John R. Bradley

MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK

Patrick F. Chinnery

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Contributions

P.F.C. conceived the study and secured funding with B.D.M.T. and S.M.H. The data cleaning and analysis was performed by M.S.R., supervised by P.F.C., B.D.M.T. and S.M.H. Cognitive data were collected by E.H., H.B. and C.S. working with the NIHR BioResource and Airwave teams, which were overseen by N.K., J.R.B. and P.E., respectively. M.S.R. drafted the manuscript with P.F.C. All authors provided critical comments on the draft manuscript and approved the final version.

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Extended data

Extended data fig. 1 proportion of missing values in cognitive test scores..

QZ: Quiz; WM: Working memory; MX: Matrices; SD: Symbols Digit; VY: Vocabulary; RT: Reaction Test; PR: Pairing 7; SB: Stroop Box; SI: Stroop Ink; TMN: Trail Making Numeric; TMA: Trail Making Alpha Numeric.

Extended Data Fig. 2 Distribution of cognitive test scores and two summary scores (G4 and G6).

In the x-axis subtitle, n indicates the number of non-missing values and m indicates the number of missing values. QZ: Quiz; WM: Working memory; MX: Matrices; SD: Symbols Digit; VY: Vocabulary; RT: Reaction Test; PR: Pairing 7; SB: Stroop Box; SI: Stroop Ink; TMN: Trail Making Numeric; TMA: Trail Making Alpha Numeric.

Extended Data Fig. 3

( A ) Percentage of variance explained by principal components (PCs) derived from six cognitive tests (SB: Stroop Box, SI: Stroop Ink, SD: Symbols Digit, TMN: Trail Making Numeric, TMA: Trail Making Alpha Numeric, RT: Reaction Test), ( B ) Contribution of each of the six cognitive tests to PC1. Horizontal red (dashed) line expected value of each test if the contribution where uniform, and ( C ) Generalised scree plot for PCs derived from SB, SI, SD, TMN, TMA and RT tests.

Extended Data Fig. 4

( A ) Percentage of variance explained by principal components (PCs) derived from four cognitive tests (QZ: Quiz, VY: Vocabulary, MX: Matrices, WM: Working Memory), ( B ) Contribution of each of the four cognitive tests to PC1. Horizontal red (dashed) line expected value of each test if the contribution where uniform, and ( C ) Generalised scree plot for PCs derived from QZ, VY, MX and WM tests.

Extended Data Fig. 5 Phenotypic correlation for 11 cognitive test scores and two measures of general cognitive ability (G4 and G6).

QZ: Quiz; WM: Working memory; MX: Matrices; SD: Symbols Digit; VY: Vocabulary; RT: Reaction Test; PR: Pairing 7; SB: Stroop Box; SI: Stroop Ink; TMN: Trail Making Numeric; TMA: Trail Making Alpha Numeric. Each coloured cell indicates magnitudes of phenotypic correlations. The corresponding colour scale is presented on the right side of the heatmap where dark green represents the highest genetic correlation, and darker moderate pink represents highest negative correlation. On the x- and y-axis, all cognitive phenotypes are presented maintaining the order.

Extended Data Fig. 6 Devices used to take the cognitive tests.

The x-axis represents types of devices used to take cognitive test and the y-axis represents number of observations.

Extended Data Fig. 7 (A-K) Cognitive tests Scores by devices used to take the test and (L-M) Measures of general cognitive ability (G4 and G6) by device used to complete all cognitive tests.

Across all plots, the x-axis represents types of devices used to take cognitive test and the y-axis represents score for the corresponding test or measures of general cognitive ability. Response Time is the average time taken per item. In each box plot ( A – L ), the box represents the interquartile range, with the center line denoting the median. The edges of the box indicate the first and third quartiles, while the whiskers extend to span a range of 1.5 interquartile distances from the edges. Individual data points that fall beyond the whiskers are presented as circles.

Extended Data Fig. 8 (A-K) Cognitive tests Scores by groups of multiple deprivation and (L-M) Measures of general cognitive ability (G4 and G6) by groups of multiple deprivation.

Across all plots, the x-axis represents groups of multiple deprivation, and the y-axis represents score for the corresponding test or measures of general cognitive ability. Response Time is the average time taken per item. In each box plot ( A-L ), the box represents the interquartile range, with the center line denoting the median. The edges of the box indicate the first and third quartiles, while the whiskers extend to span a range of 1.5 interquartile distances from the edges. Individual data points that fall beyond the whiskers are presented as circles.

Extended Data Fig. 9 Cognitive tests and two measures of general cognitive ability (G4 and G6) stratified by APOE alleles status.

( A – K ) Cognitive test scores and ( L ) G4 and G6 scores plotted against age. Lines of best fit with standard error are stratified by APOE allele status (indicated by line colour). Response Time is the average time taken per item.

Extended Data Fig. 10 (A-I) Mean and standard error for nine cognitive phenotypes across age categories stratified by APOE alleles.

In each of the plots, mean score differences for APOE allele carriers across different age groups were assessed using analysis of variance (ANOVA). The x-axis represents age categories and y-axis indicate scores for the corresponding cognitive phenotype. Bars are aligned based on age category and indicates standard error for the mean. The red, green, and blue lines across plots represent APOE allele status, specified in the right side of the plot with colour coded legend. Response Time is the average time taken per item.

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Rahman, M.S., Harrison, E., Biggs, H. et al. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med (2024). https://doi.org/10.1038/s41591-024-02960-5

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DOI : https://doi.org/10.1038/s41591-024-02960-5

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