methodology of a research paper sample

What Is Research Methodology? A Plain-Language Explanation & Definition (With Examples)

By Derek Jansen (MBA)  and Kerryn Warren (PhD) | June 2020 (Last updated April 2023)

If you’re new to formal academic research, it’s quite likely that you’re feeling a little overwhelmed by all the technical lingo that gets thrown around. And who could blame you – “research methodology”, “research methods”, “sampling strategies”… it all seems never-ending!

In this post, we’ll demystify the landscape with plain-language explanations and loads of examples (including easy-to-follow videos), so that you can approach your dissertation, thesis or research project with confidence. Let’s get started.

Research Methodology 101

• What exactly research methodology means
• What qualitative , quantitative and mixed methods are
• What sampling strategy is
• What data collection methods are
• What data analysis methods are
• How to choose your research methodology
• Example of a research methodology

What is research methodology?

Research methodology simply refers to the practical “how” of a research study. More specifically, it’s about how  a researcher  systematically designs a study  to ensure valid and reliable results that address the research aims, objectives and research questions . Specifically, how the researcher went about deciding:

• What type of data to collect (e.g., qualitative or quantitative data )
• Who  to collect it from (i.e., the sampling strategy )
• How to  collect  it (i.e., the data collection method )
• How to  analyse  it (i.e., the data analysis methods )

Within any formal piece of academic research (be it a dissertation, thesis or journal article), you’ll find a research methodology chapter or section which covers the aspects mentioned above. Importantly, a good methodology chapter explains not just   what methodological choices were made, but also explains  why they were made. In other words, the methodology chapter should justify  the design choices, by showing that the chosen methods and techniques are the best fit for the research aims, objectives and research questions. 

So, it’s the same as research design?

Not quite. As we mentioned, research methodology refers to the collection of practical decisions regarding what data you’ll collect, from who, how you’ll collect it and how you’ll analyse it. Research design, on the other hand, is more about the overall strategy you’ll adopt in your study. For example, whether you’ll use an experimental design in which you manipulate one variable while controlling others. You can learn more about research design and the various design types here .

Need a helping hand?

What are qualitative, quantitative and mixed-methods?

Qualitative, quantitative and mixed-methods are different types of methodological approaches, distinguished by their focus on words , numbers or both . This is a bit of an oversimplification, but its a good starting point for understanding.

Let’s take a closer look.

Qualitative research refers to research which focuses on collecting and analysing words (written or spoken) and textual or visual data, whereas quantitative research focuses on measurement and testing using numerical data . Qualitative analysis can also focus on other “softer” data points, such as body language or visual elements.

It’s quite common for a qualitative methodology to be used when the research aims and research questions are exploratory  in nature. For example, a qualitative methodology might be used to understand peoples’ perceptions about an event that took place, or a political candidate running for president. 

Contrasted to this, a quantitative methodology is typically used when the research aims and research questions are confirmatory  in nature. For example, a quantitative methodology might be used to measure the relationship between two variables (e.g. personality type and likelihood to commit a crime) or to test a set of hypotheses .

As you’ve probably guessed, the mixed-method methodology attempts to combine the best of both qualitative and quantitative methodologies to integrate perspectives and create a rich picture. If you’d like to learn more about these three methodological approaches, be sure to watch our explainer video below.

What is sampling strategy?

Simply put, sampling is about deciding who (or where) you’re going to collect your data from . Why does this matter? Well, generally it’s not possible to collect data from every single person in your group of interest (this is called the “population”), so you’ll need to engage a smaller portion of that group that’s accessible and manageable (this is called the “sample”).

How you go about selecting the sample (i.e., your sampling strategy) will have a major impact on your study.  There are many different sampling methods  you can choose from, but the two overarching categories are probability   sampling and  non-probability   sampling .

Probability sampling  involves using a completely random sample from the group of people you’re interested in. This is comparable to throwing the names all potential participants into a hat, shaking it up, and picking out the “winners”. By using a completely random sample, you’ll minimise the risk of selection bias and the results of your study will be more generalisable  to the entire population. 

Non-probability sampling , on the other hand,  doesn’t use a random sample . For example, it might involve using a convenience sample, which means you’d only interview or survey people that you have access to (perhaps your friends, family or work colleagues), rather than a truly random sample. With non-probability sampling, the results are typically not generalisable .

To learn more about sampling methods, be sure to check out the video below.

What are data collection methods?

As the name suggests, data collection methods simply refers to the way in which you go about collecting the data for your study. Some of the most common data collection methods include:

• Interviews (which can be unstructured, semi-structured or structured)
• Focus groups and group interviews
• Surveys (online or physical surveys)
• Observations (watching and recording activities)
• Biophysical measurements (e.g., blood pressure, heart rate, etc.)
• Documents and records (e.g., financial reports, court records, etc.)

The choice of which data collection method to use depends on your overall research aims and research questions , as well as practicalities and resource constraints. For example, if your research is exploratory in nature, qualitative methods such as interviews and focus groups would likely be a good fit. Conversely, if your research aims to measure specific variables or test hypotheses, large-scale surveys that produce large volumes of numerical data would likely be a better fit.

What are data analysis methods?

Data analysis methods refer to the methods and techniques that you’ll use to make sense of your data. These can be grouped according to whether the research is qualitative  (words-based) or quantitative (numbers-based).

Popular data analysis methods in qualitative research include:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Interpretative phenomenological analysis (IPA)
• Visual analysis (of photographs, videos, art, etc.)

Qualitative data analysis all begins with data coding , after which an analysis method is applied. In some cases, more than one analysis method is used, depending on the research aims and research questions . In the video below, we explore some  common qualitative analysis methods, along with practical examples.  

Moving on to the quantitative side of things, popular data analysis methods in this type of research include:

• Descriptive statistics (e.g. means, medians, modes )
• Inferential statistics (e.g. correlation, regression, structural equation modelling)

Again, the choice of which data collection method to use depends on your overall research aims and objectives , as well as practicalities and resource constraints. In the video below, we explain some core concepts central to quantitative analysis.

How do I choose a research methodology?

As you’ve probably picked up by now, your research aims and objectives have a major influence on the research methodology . So, the starting point for developing your research methodology is to take a step back and look at the big picture of your research, before you make methodology decisions. The first question you need to ask yourself is whether your research is exploratory or confirmatory in nature.

If your research aims and objectives are primarily exploratory in nature, your research will likely be qualitative and therefore you might consider qualitative data collection methods (e.g. interviews) and analysis methods (e.g. qualitative content analysis). 

Conversely, if your research aims and objective are looking to measure or test something (i.e. they’re confirmatory), then your research will quite likely be quantitative in nature, and you might consider quantitative data collection methods (e.g. surveys) and analyses (e.g. statistical analysis).

Designing your research and working out your methodology is a large topic, which we cover extensively on the blog . For now, however, the key takeaway is that you should always start with your research aims, objectives and research questions (the golden thread). Every methodological choice you make needs align with those three components. 

Example of a research methodology chapter

In the video below, we provide a detailed walkthrough of a research methodology from an actual dissertation, as well as an overview of our free methodology template .

Psst... there’s more!

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

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Thanks for your comment.

We can’t write your methodology for you. If you’re looking for samples, you should be able to find some sample methodologies on Google. Alternatively, you can download some previous dissertations from a dissertation directory and have a look at the methodology chapters therein.

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MLA Jansen, Derek, and Kerryn Warren. “What (Exactly) Is Research Methodology?” Grad Coach, June 2021, gradcoach.com/what-is-research-methodology/.

APA Jansen, D., & Warren, K. (2021, June). What (Exactly) Is Research Methodology? Grad Coach. https://gradcoach.com/what-is-research-methodology/

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

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

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

Importance of a Good Methodology Section

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

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

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

Structure and Writing Style

I.  Groups of Research Methods

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

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

II.  Content

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

The remainder of your methodology section should describe the following:

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

In addition, an effectively written methodology section should:

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

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

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

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

III.  Problems to Avoid

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

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

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

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

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

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

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

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

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

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

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

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

Yet Another Writing Tip

Methods and the Methodology

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

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

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

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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
• How to Write an Abstract
• How to Report Statistics
• How to Write Discussions and Conclusions
• How to Edit Your Work

The contents of the Peer Review Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

The contents of the Writing Center are also available as a live, interactive training session, complete with slides, talking points, and activities. …

There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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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.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

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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The methods section is a critical part of the research papers, allowing researchers to use this to understand your findings and replicate your work when pursuing their own research. However, it is usually also the most difficult section to write. This is where Paperpal can help you overcome the writer’s block and create the first draft in minutes with Paperpal Copilot, its secure generative AI feature suite.  

With Paperpal you can get research advice, write and refine your work, rephrase and verify the writing, and ensure submission readiness, all in one place. Here’s how you can use Paperpal to develop the first draft of your methods section.  

• Generate an outline: Input some details about your research to instantly generate an outline for your methods section 
• Develop the section: Use the outline and suggested sentence templates to expand your ideas and develop the first draft.  
• P araph ras e and trim : Get clear, concise academic text with paraphrasing that conveys your work effectively and word reduction to fix redundancies. 
• Choose the right words: Enhance text by choosing contextual synonyms based on how the words have been used in previously published work.  
• Check and verify text : Make sure the generated text showcases your methods correctly, has all the right citations, and is original and authentic. .   

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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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• 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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Organizing Academic Research Papers: 6. The Methodology

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The methods section of a research paper provides the information by which a study’s validity is judged. The method section answers two main questions: 1) How was the data collected or generated? 2) How was it analyzed? The writing should be direct and precise and written in the past tense.

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 choose affects the results and, by extension, how you likely interpreted those results.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and it misappropriates interpretations of findings .
• In most cases, there are a variety of different methods you can choose to investigate a research problem. Your methodology section of your paper should make clear the reasons why you chose a particular method or procedure .
• 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 questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The research method must be appropriate to the objectives of the study . For example, be sure 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 did arise, you must describe the ways in which their impact was minimized or why these problems do not affect the findings in any way that impacts your interpretation of the data.
• Often in social science research, it is useful for other researchers to adapt or replicate your methodology. Therefore, it is important to always provide sufficient information to allow others to use or replicate the study . This information is particularly important when a new method had been developed or an innovative use of an existing method has been utilized.

Bem, Daryl J. Writing the Empirical Journal Article . Psychology Writing Center. University of Washington; 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 empirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences. This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for hypotheses that need to be tested. This approach is focused on explanation .
• The interpretative group is focused on understanding phenomenon in a comprehensive, holistic way . This research method allows you to recognize your connection to the subject under study. Because the interpretative group focuses more on subjective knowledge, it requires careful interpretation of variables.

II. Content

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 should have a clear connection with 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 unsuited 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.
• 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?
• Provide background and 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 rationale 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 statisics being used? If other data sources exist, explain why the data you chose is most appropriate.
• Address 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.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but to the point. Don’t provide any background information that doesn’t directly help the reader to understand why a particular method was chosen, how the data was gathered or obtained, and how it was analyzed. 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. NOTE: An exception to this rule is if you select an unconventional approach to doing the method; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall research process. Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data. 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 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; 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; 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; 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.

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 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.

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Definition:

Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.

Introduction

The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

• Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
• Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
• Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
• Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
• Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
• Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
• Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
• Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
• Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

• Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
• Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
• Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

• How often do you use social media per day?
• Less than 30 minutes
• 30 minutes to 1 hour
• 1 to 2 hours
• 2 to 4 hours
• More than 4 hours
• Which social media platforms do you use?
• Others (Please specify)
• How often do you experience the following on social media?
• Social comparison (comparing yourself to others)
• Cyberbullying
• Fear of Missing Out (FOMO)
• Have you ever experienced any of the following mental health problems in the past month?
• Do you think social media use has a positive or negative impact on your mental health?
• Very positive
• Somewhat positive
• Somewhat negative
• Very negative
• In your opinion, which factors contribute to the negative impact of social media on mental health?
• Social comparison
• In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
• Education on healthy social media use
• Counseling for mental health problems caused by social media
• Social media detox programs
• Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

• Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
• Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
• Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
• Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
• Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

• For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
• For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
• To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
• To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

• To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
• To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
• To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
• To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

• Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
• Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
• Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
• Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
• Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
• Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

• Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
• Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
• Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
• Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
• Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
• Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

• Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
• Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
• Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
• Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
• Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
• Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Methodology
• Open access
• Published: 09 May 2024

Network meta-analysis for an ordinal outcome when outcome categorization varies across trials

• Paul Morris 1 ,
• Chong Wang   ORCID: orcid.org/0000-0003-4489-4344 1 , 2 &
• Annette O’Connor 2 , 3  

Systematic Reviews volume  13 , Article number:  128 ( 2024 ) Cite this article

111 Accesses

Metrics details

Binary outcomes are likely the most common in randomized controlled trials, but ordinal outcomes can also be of interest. For example, rather than simply collecting data on diseased versus healthy study subjects, investigators may collect information on the severity of disease, with no disease, mild, moderate, and severe disease as possible levels of the outcome. While some investigators may be interested in all levels of the ordinal variable, others may combine levels that are not of particular interest. Therefore, when research synthesizers subsequently conduct a network meta-analysis on a network of trials for which an ordinal outcome was measured, they may encounter a network in which outcome categorization varies across trials.

The standard method for network meta-analysis for an ordinal outcome based on a multinomial generalized linear model is not designed to accommodate the multiple outcome categorizations that might occur across trials. In this paper, we propose a network meta-analysis model for an ordinal outcome that allows for multiple categorizations. The proposed model incorporates the partial information provided by trials that combine levels through modification of the multinomial likelihoods of the affected arms, allowing for all available data to be considered in estimation of the comparative effect parameters. A Bayesian fixed effect model is used throughout, where the ordinality of the outcome is accounted for through the use of the adjacent-categories logit link.

We illustrate the method by analyzing a real network of trials on the use of antibiotics aimed at preventing liver abscesses in beef cattle and explore properties of the estimates of the comparative effect parameters through simulation. We find that even with the categorization of the levels varying across trials, the magnitudes of the biases are relatively small and that under a large sample size, the root mean square errors become small as well.

Conclusions

Our proposed method to conduct a network meta-analysis for an ordinal outcome when the categorization of the outcome varies across trials, which utilizes the adjacent-categories logit link, performs well in estimation. Because the method considers all available data in a single estimation, it will be particularly useful to research synthesizers when the network of interest has only a limited number of trials for each categorization of the outcome.

Peer Review reports

Network meta-analysis (NMA) is an extension of traditional pairwise meta-analysis that allows for the simultaneous comparison of multiple interventions by utilizing direct and indirect evidence from a network of randomized clinical trials [ 1 ]. When the outcome of interest is categorical with more than two categories, the NMA is typically conducted through the use of a generalized linear model (GLM) where the random component is multinomial, as described by [ 2 ]. It is common to utilize some type of logit link in a multinomial GLM [ 3 ], under which the parameters of interest in the NMA correspond to log-odds ratios of subjects belonging to a given category versus another under a particular intervention relative to the network’s baseline intervention. The type of logit employed determines the outcome categories under consideration in these comparative effect parameters. When the outcome is unordered, the baseline-category logit is often used [ 4 ], and the log-odds ratios are parameterized in terms of non-baseline categories versus a selected baseline category. When the outcome is ordered, it is often referred to as an ordinal variable with the categories referred to as levels. One possible choice of link function for analyzing an ordinal outcome is the adjacent-categories logit [ 3 ], under which the log-odds ratios are parameterized in terms of adjoining levels. Analysts can select the logit link that best matches their question of interest given the properties of the outcome.

While ordinal outcomes must be comprised of mutually exclusive and exhaustive levels, reporting of data for such an outcome can depend on the question of interest being addressed in a given trial. This often leads to networks for which the categorization of the outcome varies across trials. For example, suppose that we are interested in the effects of a set of interventions on an ordinal outcome with four mutually exclusive and exhaustive levels, call them A, B, C, and D. While some trials report event counts for each of the four levels, others may report combined values for B and C or even for B, C, and D. Data combined in this fashion has been referred to as incomplete [ 5 ]. Note that incomplete data in the sense presented here is unique to categorical outcomes and is distinct from the phenomenon of missing data for which the values for some subjects are either not measured or not reported at all [ 6 , 7 , 8 ]. Trials that report incomplete data can still provide information that contributes to our knowledge of the underlying comparative effects. For example, if we are utilizing the adjacent-categories logit link, the combined data for levels B and C can inform the estimates of the log-odds ratios involving B versus A and D versus C. To maximize the utility of the network of trials, it would be advantageous to consider all of the available data in estimation, regardless of outcome categorization. Unfortunately, the standard multinomial GLM framework cannot simultaneously incorporate data from multiple categorizations without some modification.

The problem of multiple outcome categorizations within a network has been addressed by [ 5 ] for the case of an unordered outcome. They proposed an extension to the multinomial GLM framework wherein the form of the multinomial likelihood was modified to allow for outcome categories to be combined according to a trial’s categorization. Their model incorporates random comparative effects and utilizes the baseline-category logit link. However, the modified multinomial likelihood was structured around a specific example and is therefore not provided in a general form. In addition, the authors did not evaluate the performance of the model through simulation.

In this paper, we extend the method developed by [ 5 ] on several fronts. First, we take a step toward adapting the method for the case of an ordinal outcome by proposing a model that utilizes the adjacent-categories logit link rather than the baseline-category logit. The structure of the adjacent-categories logit link takes into account the ordering of the outcome [ 3 ] and is particularly useful if we are interested in the log-odds ratios pertaining to adjoining levels, as parameters representing these comparisons are directly included in the model. Second, we provide the general form of the modified multinomial likelihood that allows for any outcome categorization, whether the outcome is ordered or unordered. Throughout, we assume that the intervention effects are fixed, although this can be extended to the random effects case in a manner similar to that presented in [ 5 ], and use a Bayesian approach by conducting estimation through Markov Chain Monte Carlo (MCMC).

The remainder of the paper is organized as follows. The “ Methods ” section details the proposed model, including the general form of the modified multinomial likelihood, and describes the approach to estimation. The “ Application ” section illustrates a use case of the proposed model through an analysis of a real network of trials comparing the effects of various regimens of antibiotics on the prevention of liver abscesses in beef cattle, where the severity of the abscesses is reported on an ordinal scale. The “ Simulation ” section presents a simulation study that assesses the estimation performance of the proposed model, and the “ Discussion ” and “ Conclusions ” sections discuss and conclude.

This section specifies the proposed NMA model for an ordinal outcome when outcome categorization varies across trials. This includes detailing the general form of the modified multinomial likelihood and the model for the response probabilities that follows from use of the adjacent-categories logit link. The proposed model is a modified version of that detailed in [ 5 ] for an unordered outcome, with some of the notation borrowed from [ 2 ]. The Bayesian estimation approach used throughout, including the specification of priors and starting values, is also presented.

Accommodating multiple outcome categorizations

Here the general form of the modified multinomial likelihood that allows for any outcome categorization is specified. While the term levels will be used to denote the outcome categories, this notation is also applicable to the unordered case. Consider K interventions compared in I trials, where trial i has \(n_i\) arms. Suppose that the outcome consists of M mutually exclusive and exhaustive levels, but that a given trial need not report data for each level separately. Rather, data for some levels may be reported together. That is, suppose that trial i collapses the M levels into \(M_i \le M\) groups denoted by \(A_{i,1}, A_{i,2}, \ldots , A_{i,M_i}\) that are mutually exclusive and exhaustive. Let \(\left( r_{i,k,1},\ldots ,r_{i,k,M}\right)\) be the vector of true, but potentially unreported, counts for the M levels under trial i and intervention k . We can denote the total count for the \(c^{th}\) combined category under trial i and intervention k as

where the separate counts \(r_{i,k,m}\) for \(m \in A_{i,c}\) are unreported if they belong to a group of combined levels. This notation for the grouping of levels is adapted from that given in [ 9 ] for the form of the collapsed and partitioned multinomial distribution. Note that this notation is applicable to both unordered and ordinal outcomes, but under the ordinal case it is reasonable to restrict groups to include only adjacent categories.

The reported data for intervention k in study i can then be modeled using a multinomial likelihood:

where \(\varvec{p}_{i,k}\) and \({\textbf {z}}_{i,k}\) are the response probability and observed count vectors for intervention k in trial i , \(p_{i,k,m}\) is the true response probability for intervention k and level m in study i , and \(N_{i,k} = \sum _{c=1}^{M_i}z_{i,k,c} = \sum _{m=1}^{M}r_{i,k,m}\) . Since the levels are mutually exclusive and exhaustive, \(\sum _{c=1}^{M_i}\sum _{m \in A_{i,c}}p_{i,k,m} = \sum _{m=1}^{M}p_{i,k,m} = 1\) for each trial i and intervention k .

Following independence, the likelihood for the entire network is then

where \(\varvec{p}\) and \({\textbf {z}}\) are the response probability and observed count vectors for the entire network and \(K_{i}\) denotes the group of \(n_i\) interventions included in trial i .

The modified likelihood accommodates the reporting of data for combined levels through the incorporation of the total outcome counts and response probabilities of the respective levels. The trials that combine levels therefore provide partial information on the underlying response probabilities [ 5 ], allowing for all data available across the network to contribute to the estimation of the model parameters.

Model for the response probabilities

When working with an ordinal outcome, it is natural to be interested in comparisons of interventions that involve the underlying order. A link function should be selected such that it allows analysts to directly make those comparisons of interest. The cumulative logit and the adjacent-categories logit links are common choices for an ordinal outcome. However, if proportional odds are not assumed, use of the cumulative logit does not necessarily result in valid estimated response probabilities [ 3 ]. In the context of NMA, proportional odds implies that the comparative effects of interventions are identical for each of the level pairings considered under the chosen link function. The proportional odds assumption can be useful, particularly if there is reason to believe that the effects of interventions are similar across each of the level pairings, because it utilizes the ordinality of the outcome to reduce the number of parameters included in the model [ 3 ]. We leave the development of a procedure for determining when it is appropriate to assume proportional odds under the given framework for future work, and opt to utilize the adjacent-categories logit link under non-proportional odds for the remainder of this paper. Under the adjacent-categories logit link, the comparative effects are log-odds ratios of a subject belonging to a given level relative to the level below it. This could be of interest, for example, if we have a network where the outcome consists of the severity of disease ranging from healthy to severe. The estimates of the comparative effects would then provide insight on how interventions affect the odds of a subject belonging to the mild relative to the healthy state, the moderate relative to the mild state, and the severe relative to the moderate state. Since the adjacent-categories logit and baseline-category logit are functionally reparameterizations of each other [ 3 ], such comparisons could be made under the baseline-category logit link indirectly. However, the use of the adjacent-categories logit puts parameters corresponding to the comparisons of interest directly in the model. This approach can be helpful when specifying priors, making inferences, and diagnosing problems with estimation.

Since each trial does not necessarily include each of the K interventions, some additional notation is necessary. An overall baseline intervention for the network, b , must be selected. This will often correspond to a placebo or standard therapy group. Each trial i also has a trial-specific baseline intervention, \(b_i\) , which will be the same as b if \(b \in K_i\) . Then the response probabilities can be modeled as

where \(I(\cdot )\) denotes the indicator function, and

The logit described in Eq. 4 can be interpreted as the log-odds of a subject belonging to level m versus \(m-1\) under trial i and intervention k . Under Eq. 4 , the \(\mu _{i,m}\) correspond to trial-specific baselines representing the log-odds of level m versus level \(m-1\) under study i ’s baseline intervention \(b_i\) . These are regarded as nuisance parameters and serve only to set up the contrast needed to include the parameters corresponding to the comparative effects. The \(d_{b_i,k,m}\) represents the log-odds ratio of a subject belonging to category m versus \(m-1\) under intervention k relative to the trial-specific baseline \(b_i\) . These are included in Eq. 4 only if \(k \ne b_i\) , as if \(k = b_i\) then \(\mu _{i,m}\) represents the corresponding logit on its own. Following from the consistency assumption (see [ 10 ] for an overview of the assumptions commonly made in NMA),

where \(d_{k,m}\) represents the log-odds ratio of a subject belonging to category m versus \(m-1\) under intervention k relative to the overall baseline intervention b . The \(d_{k,m}\) are the parameters of interest and do not vary across trials under the assumption of fixed intervention effects. Under the consistency assumption, the comparative effect between any two interventions \(k_1\) and \(k_2\) is \(d_{k_1, k_2, m} = d_{k_2,m} - d_{k_1,m}\) .

Expressions for the response probabilities can be obtained through an application of the inverse adjacent-categories logit function [ 11 ] to Eqs. 4 and 5 :

Bayesian implementation

The model parameters are estimated using MCMC via the JAGS software package [ 12 ]. The Bayesian approach is advantageous for the proposed method, as it allows for the specification of informative priors to help overcome the lack of identifiability of some of the trial-specific baseline parameters.

Prior specification

In order to implement the Bayesian approach, priors need to be assigned to the parameters in the model. Since we are assuming fixed intervention effects, we need only consider each of the \(\mu _{i,m}\) and \(d_{k,m}\) . Identifiability of the \(d_{k,m}\) requires that for each adjacent pair of levels there exists a path of comparisons that connects all of the interventions such that in each comparison the event counts for the two levels are reported separately. Assuming that this condition holds, we can assign these parameters non-informative \(\text {Normal}(0, 1,000,000)\) priors as was done in [ 5 ].

Recall that the \(\mu _{i,m}\) are trial-specific baselines representing the log-odds of a subject belonging to level m versus \(m-1\) under trial i ’s baseline intervention \(b_i\) . Since any combination of adjacent levels is allowed in the observed data, for a given trial, separate event counts for levels m and \(m-1\) may not be reported. This means that there will not be data available to estimate some of the \(\mu _{i,m}\) parameters. Stronger priors can be used to help overcome this lack of identifiability. We propose the following procedure to specify priors for the \(\mu _{i,m}\) . For the group of trials T that report separate event counts for levels m and \(m-1\) under trial-specific baseline intervention \(b_i\) :

Calculate the empirical log-odds for category m versus \(m-1\) under intervention \(b_i\) from the available data. In the specified notation, these would take the form \(y_i = log(r_{i,b_i,m}/r_{i,b_i,m-1})\) for trials \(i \in T\) . Note that if either of the event counts is 0 for trial i , we add 0.5 to each count to ensure that the empirical log-odds fall on the real line.

Fit the following Bayesian model using the \(y_i\) from the previous step as data: \(y_i {\mathop {\sim }\limits ^{iid}} \text {Normal}(\mu , \sigma ^2)\) with priors \(\mu \sim Normal(0, 1000)\) and \(\sigma \sim Uniform(0, 5)\) . This can be done using JAGS via the rjags R package [ 13 ], where 10,000 iterations are run for each of burn-in and sampling. Note that this step is adapted from the estimation of the baseline-effects model under NMA for a binary outcome presented in [ 14 ].

Letting \(\hat{\mu }\) and \(\hat{\sigma }^2\) denote the posterior means of \(\mu\) and \(\sigma ^2\) obtained in step 2, assign the prior \(\mu _{i,m} \sim Normal\left(\hat{\mu }, \hat{\sigma }^2\right)\) for each \(i \in T\) .

Selection of starting values

Selecting suitable starting values is important to ensure proper behavior of the MCMC chains. Schmid et al. [ 5 ] proposes a method for selecting dispersed starting values under the baseline-category logit link. This procedure was later implemented in the BNMA R package [ 15 ]. We modify this procedure for use with the adjacent-categories logit link by substituting the empirical adjacent-categories log-odds for the empirical baseline-category log-odds in the described regression.

Parameter estimation

Estimation was achieved through MCMC using the JAGS software, where we interfaced with JAGS via the rjags R package. Four MCMC chains were used, and proper convergence and mixing of the chains were monitored through the Gelman-Rubin diagnostic [ 16 ] and examination of the trace plots.

Application

In this section, the use of the proposed model is illustrated through the analysis of a network of studies examining the effects of several antibiotic regimens on the prevention of liver abscesses in beef cattle. Many studies in the veterinary literature have compared the efficacy of various interventions on this outcome, but synthesis of this research has proved difficult because the categorization of the outcome varies across trials. Abscess severity is often measured using an ordinal scale containing four levels: healthy (H), one or two small abscesses (A−), two to four small abscesses (A), and one or more large abscesses (A+) [ 17 , 18 ]. This ordinal scale is well established in the beef cattle industry. However, because the economic impact of liver abscesses is mainly linked to the A+ level, some investigators combined levels A− and A [ 19 , 20 ], resulting in a three-level outcome variable. This three-level scale is currently used by the well-known Elanco Liver Check Service [ 21 ]. Still other investigators report the presence of any abscess regardless of severity, combining A−, A, and A+ [ 22 , 23 ], resulting in a two-level outcome variable. To control liver abscesses, in-feed antibiotics are used. Currently, in-feed tylosin phosphate, an antibiotic in the same family as erythromycin, is the primary approach to the prevention of liver abscesses. However, over the years numerous approaches to control have been evaluated including diets, other antibiotics, non-antibiotic additives, ionophores, and other regimens of tylosin.

The data for this network are a subset of that obtained from a systematic review of interventions aimed at preventing liver abscesses in cattle (the review protocol of which is available at https://syreaf.org/protocols/ ). For the purposes of this project, only four interventions are included in the network presented here to enable focus on the methodological issue of interest. Three of the intervention groups are regimens of the antibiotics tylosin phosphate or virginiamycin while the fourth is a placebo group. An example of a trial identified by the review but excluded in this study is [ 24 ]. The purpose of this trial was to evaluate the effect of a phytogenic feed additive (Digestarom; Biomin, Getzersdorf, Austria) on multiple outcomes including liver abscesses in finishing steers. Since the trial did not examine any of the tylosin phosphate or virginiamycin regimens of interest it was excluded from our network.

For the trials included in the network, we defined a placebo arm as any trial arm that did not contain tylosin phosphate or virginiamycin. Placebo arms contained any level of monensin or diet composition. Monensin is an ionophore administered in feed that promotes the efficient use of feedstuffs and is not considered to have any impact on liver abscesses. As many trials included multiple such arms, the data from these arms were combined to create a single placebo arm per trial. To illustrate this approach we use a trial published in [ 17 ]. This trial was a 2 by 3 factorial design with one factor being diet: based on steam-flaked corn finishing diet (SFC) or SFC plus 25% (dry basis) corn wet distillers grains with solubles (WDGS). The second factor was feed additives: no added antibiotics (NONE), 300 mg of monensin daily (MON), or 300 mg of monensin + 90 mg of tylosin phosphate daily (MON+TYL). Our approach to handling such a trial was to combine the data for the NONE + SFC, NONE + WDGS , MON + SFC, and MON + WDG arms into a single placebo arm. The antibiotic arm (tylosin phosphate) was created by combing the data from the (MON + TYL) + SFC and (MON + TYL) + WDGS arms. The treatments were fed from arrival to slaughter, i.e., 150 days.

Tylosin phosphate arms were categorized based on dosing regimens as follows:

Protocols that began the feeding period without tylosin phosphate and started to feed constantly late in the feed period (latestart)

Protocols that began the feeding period with tylosin phosphate and ended the feeding period without tylosin phosphate (earlyfinish)

Protocols that did use tylosin phosphate for the entire feeding period but limited the period to less than or equal to 100 days (short)

Protocols that did use tylosin phosphate for the entire feeding period but that feeding period was more than 100 days (long)

Protocols that did use tylosin phosphate for the entire period but intermittently (intermittent).

An example of the use of this approach to categorize tylosin regimens is provided by [ 25 ], which investigated management strategies that reduce in-feed tylosin phosphate in the control of liver abscesses in feedlot cattle. A total of 7576 crossbred yearlings were allocated to the trial (approximately 253 animals/pen with 10 replicate pens per treatment) and individually randomized to one of three treatments: tylosin phosphate (11 ppm) was included in-feed (1) for the first 125 days on feed (DOF) (earlyfinish) (2) for DOF 41 to 161 (latestart) or (3) for the entire feeding period for DOF 0 to 161 (long). However, for this project, we only included arms corresponding to the “long” regimen as a means of keeping the illustrative data set simple. This is also the registered dose, while the others are exploratory.

Virginiamycin arms were categorized based on two dose levels: less than 15 mg/kg, and greater than or equal to 15 mg/kg. An example of an application of this grouping scheme follows from [ 26 ], which fed cattle a four-level range of virginiamycin (0, 10, 25, and 50 mg/kg) over multiple trials throughout a 245 day growing-finishing period. For this trial, the 0 mg/kg arm was designated as the placebo, 10 mg/kg arm was designated as being less than 15 mg/kg, and the data for the 25 mg/kg and 50 mg/kg arms were combined into a single arm with greater than 15 mg/kg virginiamycin.

Figure 1 presents a diagram of the network. Note that every trial included a placebo group, and that the tylosin phosphate regimen is included in a large number of trials relative to the virginiamycin regimens. Also note that the tylosin phosphate regimen is not directly compared with either of the virginiamycin dosing regimens. Each of the three liver abscess categorizations detailed in the first paragraph of this section is present in the network. Table 1 details the frequencies of these categorizations. While 12 of the 22 trials comparing tylosin phosphate to the placebo reported complete data (4 levels), complete data was available in only four of the eight trials including either of the virginiamycin regimens. The remaining four trials that included either of the virginiamycin regimens combined A−, A, and A+ in their reporting. There is thus substantially less data available to estimate the comparative effects involving virginiamycin compared to those for tylosin phosphate.

Diagram of the liver abscess trial network. Nodes are interventions and edges are direct comparisons. The size of the nodes and the numbers in parentheses indicate the number of trials that include each intervention, while the edge width indicates the number of direct comparisons made between each intervention

Estimation was conducted as described in the “ Bayesian implementation ” section. Here the model parameters were estimated using 50,000 iterations for each of burn-in and sampling. Table 2 displays the point estimates and 95% credible intervals for each of the basic comparative effect parameters on the log-odds ratio and odds ratio scales. A negative estimate on the log-odds ratio scale means that a subject is estimated to be relatively more likely to belong to the lower disease level than the higher one under the noted intervention compared to under the placebo. We note that the 95% credible intervals are quite wide for the comparative effects associated with virginiamycin due to the limited amount of data on that antibiotic in the network.

In this section, properties of the estimates of the comparative effect parameters are evaluated through simulation under two scenarios. In the first, we treat the point estimates obtained in the “ Application ” section as the true values for the parameters and use these values to repeatedly regenerate the data for the liver abscess network. The estimates obtained by analyzing the regenerated datasets can then be used to calculate bias, root mean square error (RMSE), and coverage probability of the credible intervals. The second scenario is similar to the first, except that here in the regeneration step data for each study is generated ten times as if it came from ten different studies. This results in simulated datasets that are ten times larger than those in the first scenario, which allows for the evaluation of large-sample estimation performance.

The simulation procedure for the first scenario is as follows:

Obtain the posterior means for each parameter from the existing NMA (values for the comparative effects are given in Table 2 , while those for the trial-specific baselines are not shown). For each trial i , intervention k and outcome level \(m = 2,\ldots , M\) that occurs in the network, let \(\hat{\mu }_{i,m}\) and \(\hat{d}_{k,m}\) denote the corresponding posterior means.

For each trial i , intervention k and outcome level \(m = 2,\ldots , M\) in the network, calculate \(\hat{\theta }_{i,k,m} = \hat{\mu }_{i,m} + \hat{d}_{b_i,k,m}\) where \(\hat{d}_{b_i,k,m}\) is obtained by using the estimated comparative effects in Eq. 6 , i.e., the consistency assumption. Then obtain values for the multinomial response probabilities, denoted by \(\hat{p}_{i,k,m}\) , using \(\hat{\theta }_{i,k,m}\) in Eqs. 7 and 8 .

Complete the following 1000 times:

Generate the complete data for the network using the calculated probabilities. For each trial i and intervention k in \(K_i\) : \((r_{i,k,1},\ldots ,r_{i,k,M}) \sim \text {Multinomial}(N_{i,k}, (\hat{p}_{i,k,1},\ldots ,\hat{p}_{i,k,M}))\) .

Combine the outcome data where necessary as indicated by the existing network. This gives the observed data in the form of the \(z_{i,k,c}\) , where \(z_{i,k,c} = \sum _{m \in A_{i,c}}r_{i,k,m}\) and \(A_{i,c}\) is the \(c^{th}\) group of outcome levels for trial i .

Conduct the analysis on the generated dataset using the proposed model as was done in the “ Application ” section. Here 25,000 iterations were used for each of burn-in and sampling to ensure reasonable computation time. Record the posterior means of the \(d_{k,m}\) , the comparative effect parameters of interest.

Calculate the bias, RMSE, and coverage probability for each of the \(d_{k,m}\) using the posterior means as the point estimates.

The simulation results for the first scenario are displayed in Table 3 . We see that the magnitude of the biases is relatively small and consistent across each of the comparative effect parameters. Given the lack of available data with which to estimate the trial-specific baseline parameters in some trials, some bias is expected as posterior draws for the baseline parameters that fall far from the true values will inevitably affect the estimation of the comparative effects. The use of more informative priors for the trial-specific baseline parameters as described in the “ Prior specification ” section helps limit the bias relative to using non-informative priors. The RMSEs are somewhat large due to the limited amount of data available to estimate many of these comparisons, particularly for those involving either of the virginiamycin regimens. As we will see in the simulation results for the second scenario, the magnitude of the RMSEs can be reduced if more data is available. Finally, it is clear that the proposed model achieves coverage probabilities close to the nominal value.

Scenario II

For the second scenario, the simulation procedure is identical to that used in the first except that in step (3), each trial in the existing network is used to generate data for ten separate trials rather than one. We thus generate datasets that are ten times as large as those under the first scenario. Results of the simulations under the second scenario are available in Table 4 . Note that the biases are of similar magnitude to those seen under the first scenario. While the datasets are ten times as large, there are also ten times as many trials, and thus we have not circumvented the issue brought on by the trial-specific baseline parameters that was noted in the first scenario. However, the magnitude of the RMSEs is substantially reduced, including those corresponding to comparative effects involving the virginiamycin interventions. It is worth noting here that the coverage probabilities are slightly lower than in the previous scenario. Since more data is available to estimate each of these comparisons, the credible intervals are narrower. In conjunction with the slight bias introduced through the trial-specific baseline parameters, the narrower intervals result in the true values falling outside of the given bounds at a higher rate.

In this paper we proposed a fixed effect multinomial NMA model for an ordinal outcome that allows for multiple outcome categorizations within a network. The proposed model is a modification of that presented in [ 5 ] for an unordered categorical outcome. These models are particularly useful when working with sparse networks, which are commonly encountered and can affect the quality of comparative effect estimates in terms of both precision and power. Because the models simultaneously accommodate trials with different outcome categorizations, they allow for the consideration of all available data in the estimation of the comparative effects. Practitioners can therefore avoid contributing to the sparsity of the network by excluding valuable information.

Furthermore, when working with an ordinal outcome, it is often desirable to compare interventions in such a way that the ordering is recognized. While it would be possible to use the method of [ 5 ] to analyze a network with an ordinal outcome, the resulting comparative effects would not directly recognize the ordinality. The adjacent-categories logit link incorporates the ordering into the comparative effect parameters and is an appropriate choice in many applications. It is possible to back out the adjacent-category comparative effect estimates from those of the baseline-category model through the relationship between the two logit functions [ 3 ], but including the adjacent-category comparative effects directly in the model makes specifying priors, conducting inference, and optimizing estimation more straightforward.

As with any method, there are some limitations that are important to keep in mind. While the simulations showed that the estimates of the comparative effects behave reasonably well, the lack of data with which to estimate some of the trial-specific baseline parameters introduces some bias that is not eliminated as sample size increases (see Tables 3 and 4 ). An empirical approach to specifying informative priors for these trial-specific baseline parameters was used in an attempt to keep the bias small. This approach limits the bias relative to using non-informative priors but could potentially be optimized even further. It is important to note that there is then a potential trade-off between limiting sparsity and introducing bias that comes with allowing for multiple outcome categorizations. It is possible that the standard multinomial NMA model could be the better choice for some networks with sizable amounts of data.

It is also important to ensure that the estimation procedure behaves as intended. Even with sensibly chosen priors and starting values, the complexity of NMA models can make estimation through MCMC difficult. For example, in the second simulation scenario, the adaptation phase run by JAGS was not completed for many of the generated datasets even after 25,000 iterations. Adaptation can affect the behavior of the samplers employed by JAGS. Care therefore needed to be taken to ensure that the chains were run long enough such that mixing and convergence were achieved and effective sample sizes were reasonably high.

Finally, additional developments not implemented here can increase the utility of the proposed method. For example, the proportional odds assumption, which states that the comparative effect parameters for a given intervention are the same across the different level pairings, could be reasonable for some applications with ordinal outcomes. The assumption exploits the ordinality of the outcome to reduce the number of parameters included in the model [ 3 ]. However, it is a strong assumption to make and the researchers would need to be sure that it is appropriate for a given level pairing before implementing it. The development of a procedure to determine if the proportional odds assumption is appropriate under the adjacent-categories logit link function would allow for its incorporation into the proposed model. In addition, the proposed model could be extended through the use of other link functions. For example, the cumulative logit link might be of interest for a given application and could be used if the proportional odds assumption was determined to be appropriate for that structure. An extension allowing for random intervention effects could also improve the fit of the proposed method for many networks.

In conclusion, we have proposed a multinomial NMA model for ordinal outcomes that can simultaneously handle multiple outcome categorizations, thereby ensuring that data from all of the trials included in a network can be used during estimation. The use of the adjacent-categories logit link incorporates the ordering of the outcome into the comparative effect parameters, and simulations showed that the model generally performs well with respect to estimation. The inclusion of the general form of the modified multinomial likelihood that allows for any combination of levels and R functions linked to below that can be used to implement the method should allow for its use in a wide range of applications. Moreover, there is substantial room for further development that can take fuller advantage of the ordinality of the outcome through the proportional odds assumption and the utilization of additional link functions.

Availability of data and materials

The dataset analyzed in the application section of this article as well as R functions that can be used to implement the proposed method are available at https://github.com/psmorris15/ordinal_NMA .

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Department of Statistics, Iowa State University, Ames, 50010, IA, USA

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PM proposed the method and conducted the data analysis and simulations. AOC provided the data and assisted with the data analysis. CW provided input on the proposed method and assisted with the simulations. The manuscript was prepared by PM and reviewed by CW and AOC. All authors read and approved the final manuscript.

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Morris, P., Wang, C. & O’Connor, A. Network meta-analysis for an ordinal outcome when outcome categorization varies across trials. Syst Rev 13 , 128 (2024). https://doi.org/10.1186/s13643-024-02537-w

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A research team from Technion – Israel Institute of Technology and Google Research has introduced SliCK, a novel framework specifically designed to examine integrating new knowledge within LLMs. This methodology stands out by categorizing knowledge into distinct levels, ranging from HighlyKnown to Unknown, providing a granular analysis of how different types of information affect model performance. This setup allows for a precise evaluation of the model’s ability to assimilate new facts while maintaining the accuracy of its existing knowledge base, highlighting the delicate balance required in model training.

In the methodology, the study leverages the PaLM model, a robust LLM developed by Google, which was fine-tuned using datasets carefully designed to include varying proportions of knowledge categories: HighlyKnown, MaybeKnown, WeaklyKnown, and Unknown. These datasets are derived from a curated subset of factual questions mapped from Wikidata relations, enabling a controlled examination of the model’s learning dynamics. The experiment meticulously quantifies the model’s performance across these categories using exact match (EM) metrics to assess how effectively the model integrates new information while avoiding the pitfalls of hallucinations. This structured approach provides a clear view of the impact of fine-tuning with both familiar and novel data on model accuracy.

The study’s findings demonstrate the effectiveness of the SliCK categorization in enhancing the fine-tuning process. Models trained using this structured approach, particularly with a 50% Known and 50% Unknown mix, showed an optimized balance, achieving a 5% higher accuracy in generating correct responses compared to models trained with predominantly Unknown data. Conversely, when the proportion of Unknown data exceeded 70%, the models’ propensity for hallucinations increased by approximately 12%. These results highlight SliCK’s critical role in quantitatively assessing and managing the risk of error as new information is integrated during the fine-tuning of LLMs.

To summarize, the research by Technion – Israel Institute of Technology and Google Research thoroughly examines fine-tuning LLMs using the SliCK framework to manage the integration of new knowledge. The study highlights the delicate balance required in model training, with the PaLM model demonstrating improved accuracy and reduced hallucinations when trained under controlled knowledge conditions. These findings underscore the importance of strategic data categorization in enhancing model reliability and performance, offering valuable insights for future developments in machine learning methodologies.

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Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.

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Impact of the use of cannabis as a medicine in pregnancy, on the unborn child: a systematic review and meta-analysis protocol

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Introduction: The use of cannabis for medicinal purposes is on the rise. As more people place their trust in the safety of prescribed alternative plant-based medicine and find it easily accessible, there is a growing concern that pregnant women may be increasingly using cannabis for medicinal purposes to manage their pregnancy symptoms and other health conditions. The aim of this review is to investigate the use of cannabis for medicinal purposes during pregnancy, describe the characteristics of the demographic population, and to measure the impact on the unborn child and up to twelve months postpartum. Methods and analyses: Research on pregnant women who use cannabis for medicinal purposes only and infants up to one year after birth who experienced in utero exposure to cannabis for medicinal purposes will be included in this review. Reviews, randomised controlled trials, case control, cross-sectional and cohort studies, that have been peer reviewed and published between 1996 and April 2024 as a primary research paper that investigates prenatal use of cannabis for medicinal purposes on foetal, perinatal, and neonatal outcomes, will be selected for review. Excluding cover editorials, letters, commentaries, protocols, conference papers and book chapters. Effects of illicit drugs use, alcohol misuse and nicotine exposure on neonate outcome will be controlled by excluding studies reporting on the concomitant use of such substances with cannabis for medicinal purposes during pregnancy. All titles and abstracts will be reviewed independently and in duplicate by at least two researchers. Records will be excluded based on title and abstract screening as well as publication type. Where initial disagreement exists between reviewers regarding the inclusion of a study, team members will review disputed articles status until consensus is gained. Selected studies will then be assessed by at least two independent researchers for risk bias assessment using validated tools. Data will be extracted and analysed following a systematic review and meta-analysis methodology. The statistical analysis will combine three or more outcomes that are reported in a consistent manner. The systematic review and meta-analysis will follow the PRISMA guidelines to facilitate transparent reporting [1].

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I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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