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  • How to Write Discussions and Conclusions

How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

  • the results of your research,
  • a discussion of related research, and
  • a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

discussion and analysis in research paper example

Questions to ask yourself:

  • Was my hypothesis correct?
  • If my hypothesis is partially correct or entirely different, what can be learned from the results? 
  • How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
  • Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
  • How can future research build on these observations? What are the key experiments that must be done? 
  • What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

discussion and analysis in research paper example

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

What to do

  • Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
  • Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
  • Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
  • State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
  • Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
  • If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
  • Be concise. Adding unnecessary detail can distract from the main findings. 

What not to do

Don’t

  • Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
  • Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
  • Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
  • Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
  • Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

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Organizing Your Social Sciences Research Paper

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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

  • Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
  • Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
  • Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
  • Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
  • Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

  • Do not be verbose or repetitive; be concise and make your points clearly
  • Avoid the use of jargon or undefined technical language
  • Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
  • Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
  • If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

  • Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
  • References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
  • Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
  • Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

  • Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
  • Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
  • Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
  • Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
  • Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
  • Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
  • The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

  • Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
  • As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
  • Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
  • Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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how to write a discussion section

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The discussion section of a research paper is where the author analyzes and explains the importance of the study's results. It presents the conclusions drawn from the study, compares them to previous research, and addresses any potential limitations or weaknesses. The discussion section should also suggest areas for future research.

Everything is not that complicated if you know where to find the required information. We’ll tell you everything there is to know about writing your discussion. Our easy guide covers all important bits, including research questions and your research results. Do you know how all enumerated events are connected? Well, you will after reading this guide we’ve prepared for you!

What Is in the Discussion Section of a Research Paper

The discussion section of a research paper can be viewed as something similar to the conclusion of your paper. But not literal, of course. It’s an ultimate section where you can talk about the findings of your study. Think about these questions when writing:

  • Did you answer all of the promised research questions?
  • Did you mention why your work matters?
  • What are your findings, and why should anyone even care?
  • Does your study have a literature review?

So, answer your questions, provide proof, and don’t forget about your promises from the introduction. 

How to Write a Discussion Section in 5 Steps

How to write the discussion section of a research paper is something everyone googles eventually. It's just life. But why not make everything easier? In brief, this section we’re talking about must include all following parts:

  • Answers for research questions
  • Literature review
  • Results of the work
  • Limitations of one’s study
  • Overall conclusion

Indeed, all those parts may confuse anyone. So by looking at our guide, you'll save yourself some hassle.  P.S. All our steps are easy and explained in detail! But if you are looking for the most efficient solution, consider using professional help. Leave your “ write my research paper for me ” order at StudyCrumb and get a customized study tailored to your requirements.

Step 1. Start Strong: Discussion Section of a Research Paper

First and foremost, how to start the discussion section of a research paper? Here’s what you should definitely consider before settling down to start writing:

  • All essays or papers must begin strong. All readers will not wait for any writer to get to the point. We advise summarizing the paper's main findings.
  • Moreover, you should relate both discussion and literature review to what you have discovered. Mentioning that would be a plus too.
  • Make sure that an introduction or start per se is clear and concise. Word count might be needed for school. But any paper should be understandable and not too diluted.

Step 2. Answer the Questions in Your Discussion Section of a Research Paper

Writing the discussion section of a research paper also involves mentioning your questions. Remember that in your introduction, you have promised your readers to answer certain questions. Well, now it’s a perfect time to finally give the awaited answer. You need to explain all possible correlations between your findings, research questions, and literature proposed. You already had hypotheses. So were they correct, or maybe you want to propose certain corrections? Section’s main goal is to avoid open ends. It’s not a story or a fairytale with an intriguing ending. If you have several questions, you must answer them. As simple as that.

Step 3. Relate Your Results in a Discussion Section

Writing a discussion section of a research paper also requires any writer to explain their results. You will undoubtedly include an impactful literature review. However, your readers should not just try and struggle with understanding what are some specific relationships behind previous studies and your results.  Your results should sound something like: “This guy in their paper discovered that apples are green. Nevertheless, I have proven via experimentation and research that apples are actually red.” Please, don’t take these results directly. It’s just an initial hypothesis. But what you should definitely remember is any practical implications of your study. Why does it matter and how can anyone use it? That’s the most crucial question.

Step 4. Describe the Limitations in Your Discussion Section

Discussion section of a research paper isn’t limitless. What does that mean? Essentially, it means that you also have to discuss any limitations of your study. Maybe you had some methodological inconsistencies. Possibly, there are no particular theories or not enough information for you to be entirely confident in one’s conclusions.  You might say that an available source of literature you have studied does not focus on one’s issue. That’s why one’s main limitation is theoretical. However, keep in mind that your limitations must possess a certain degree of relevancy. You can just say that you haven’t found enough books. Your information must be truthful to research.

Step 5. Conclude Your Discussion Section With Recommendations

Your last step when you write a discussion section in a paper is its conclusion, like in any other academic work. Writer’s conclusion must be as strong as their starting point of the overall work. Check out our brief list of things to know about the conclusion in research paper :

  • It must present its scientific relevance and importance of your work.
  • It should include different implications of your research.
  • It should not, however, discuss anything new or things that you have not mentioned before.
  • Leave no open questions and carefully complete the work without them.

Discussion Section of a Research Paper Example

All the best example discussion sections of a research paper will be written according to our brief guide. Don’t forget that you need to state your findings and underline the importance of your work. An undoubtedly big part of one’s discussion will definitely be answering and explaining the research questions. In other words, you’ll already have all the knowledge you have so carefully gathered. Our last step for you is to recollect and wrap up your paper. But we’re sure you’ll succeed!

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How to Write a Discussion Section: Final Thoughts

Today we have covered how to write a discussion section. That was quite a brief journey, wasn’t it? Just to remind you to focus on these things:

  • Importance of your study.
  • Summary of the information you have gathered.
  • Main findings and conclusions.
  • Answers to all research questions without an open end.
  • Correlation between literature review and your results.

But, wait, this guide is not the only thing we can do. Looking for how to write an abstract for a research paper  for example? We have such a blog and much more on our platform.

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Discussion Section of a Research Paper: Frequently Asked Questions

1. how long should the discussion section of a research paper be.

Our discussion section of a research paper should not be longer than other sections. So try to keep it short but as informative as possible. It usually contains around 6-7 paragraphs in length. It is enough to briefly summarize all the important data and not to drag it.

2. What's the difference between the discussion and the results?

The difference between discussion and results is very simple and easy to understand. The results only report your main findings. You stated what you have found and how you have done that. In contrast, one’s discussion mentions your findings and explains how they relate to other literature, research questions, and one’s hypothesis. Therefore, it is not only a report but an efficient as well as proper explanation.

3. What's the difference between a discussion and a conclusion?

The difference between discussion and conclusion is also quite easy. Conclusion is a brief summary of all the findings and results. Still, our favorite discussion section interprets and explains your main results. It is an important but more lengthy and wordy part. Besides, it uses extra literature for references.

4. What is the purpose of the discussion section?

The primary purpose of a discussion section is to interpret and describe all your interesting findings. Therefore, you should state what you have learned, whether your hypothesis was correct and how your results can be explained using other sources. If this section is clear to readers, our congratulations as you have succeeded.

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How To Write The Discussion Chapter

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

By: Jenna Crossley (PhD Cand). Reviewed By: Dr. Eunice Rautenbach | August 2021

If you’re reading this, chances are you’ve reached the discussion chapter of your thesis or dissertation and are looking for a bit of guidance. Well, you’ve come to the right place ! In this post, we’ll unpack and demystify the typical discussion chapter in straightforward, easy to understand language, with loads of examples .

Overview: Dissertation Discussion Chapter

  • What (exactly) the discussion chapter is
  • What to include in your discussion chapter
  • How to write up your discussion chapter
  • A few tips and tricks to help you along the way

What exactly is the discussion chapter?

The discussion chapter is where you interpret and explain your results within your thesis or dissertation. This contrasts with the results chapter, where you merely present and describe the analysis findings (whether qualitative or quantitative ). In the discussion chapter, you elaborate on and evaluate your research findings, and discuss the significance and implications of your results.

In this chapter, you’ll situate your research findings in terms of your research questions or hypotheses and tie them back to previous studies and literature (which you would have covered in your literature review chapter). You’ll also have a look at how relevant and/or significant your findings are to your field of research, and you’ll argue for the conclusions that you draw from your analysis. Simply put, the discussion chapter is there for you to interact with and explain your research findings in a thorough and coherent manner.

Discussion

What should I include in the discussion chapter?

First things first: in some studies, the results and discussion chapter are combined into one chapter .  This depends on the type of study you conducted (i.e., the nature of the study and methodology adopted), as well as the standards set by the university.  So, check in with your university regarding their norms and expectations before getting started. In this post, we’ll treat the two chapters as separate, as this is most common.

Basically, your discussion chapter should analyse , explore the meaning and identify the importance of the data you presented in your results chapter. In the discussion chapter, you’ll give your results some form of meaning by evaluating and interpreting them. This will help answer your research questions, achieve your research aims and support your overall conclusion (s). Therefore, you discussion chapter should focus on findings that are directly connected to your research aims and questions. Don’t waste precious time and word count on findings that are not central to the purpose of your research project.

As this chapter is a reflection of your results chapter, it’s vital that you don’t report any new findings . In other words, you can’t present claims here if you didn’t present the relevant data in the results chapter first.  So, make sure that for every discussion point you raise in this chapter, you’ve covered the respective data analysis in the results chapter. If you haven’t, you’ll need to go back and adjust your results chapter accordingly.

If you’re struggling to get started, try writing down a bullet point list everything you found in your results chapter. From this, you can make a list of everything you need to cover in your discussion chapter. Also, make sure you revisit your research questions or hypotheses and incorporate the relevant discussion to address these.  This will also help you to see how you can structure your chapter logically.

Need a helping hand?

discussion and analysis in research paper example

How to write the discussion chapter

Now that you’ve got a clear idea of what the discussion chapter is and what it needs to include, let’s look at how you can go about structuring this critically important chapter. Broadly speaking, there are six core components that need to be included, and these can be treated as steps in the chapter writing process.

Step 1: Restate your research problem and research questions

The first step in writing up your discussion chapter is to remind your reader of your research problem , as well as your research aim(s) and research questions . If you have hypotheses, you can also briefly mention these. This “reminder” is very important because, after reading dozens of pages, the reader may have forgotten the original point of your research or been swayed in another direction. It’s also likely that some readers skip straight to your discussion chapter from the introduction chapter , so make sure that your research aims and research questions are clear.

Step 2: Summarise your key findings

Next, you’ll want to summarise your key findings from your results chapter. This may look different for qualitative and quantitative research , where qualitative research may report on themes and relationships, whereas quantitative research may touch on correlations and causal relationships. Regardless of the methodology, in this section you need to highlight the overall key findings in relation to your research questions.

Typically, this section only requires one or two paragraphs , depending on how many research questions you have. Aim to be concise here, as you will unpack these findings in more detail later in the chapter. For now, a few lines that directly address your research questions are all that you need.

Some examples of the kind of language you’d use here include:

  • The data suggest that…
  • The data support/oppose the theory that…
  • The analysis identifies…

These are purely examples. What you present here will be completely dependent on your original research questions, so make sure that you are led by them .

It depends

Step 3: Interpret your results

Once you’ve restated your research problem and research question(s) and briefly presented your key findings, you can unpack your findings by interpreting your results. Remember: only include what you reported in your results section – don’t introduce new information.

From a structural perspective, it can be a wise approach to follow a similar structure in this chapter as you did in your results chapter. This would help improve readability and make it easier for your reader to follow your arguments. For example, if you structured you results discussion by qualitative themes, it may make sense to do the same here.

Alternatively, you may structure this chapter by research questions, or based on an overarching theoretical framework that your study revolved around. Every study is different, so you’ll need to assess what structure works best for you.

When interpreting your results, you’ll want to assess how your findings compare to those of the existing research (from your literature review chapter). Even if your findings contrast with the existing research, you need to include these in your discussion. In fact, those contrasts are often the most interesting findings . In this case, you’d want to think about why you didn’t find what you were expecting in your data and what the significance of this contrast is.

Here are a few questions to help guide your discussion:

  • How do your results relate with those of previous studies ?
  • If you get results that differ from those of previous studies, why may this be the case?
  • What do your results contribute to your field of research?
  • What other explanations could there be for your findings?

When interpreting your findings, be careful not to draw conclusions that aren’t substantiated . Every claim you make needs to be backed up with evidence or findings from the data (and that data needs to be presented in the previous chapter – results). This can look different for different studies; qualitative data may require quotes as evidence, whereas quantitative data would use statistical methods and tests. Whatever the case, every claim you make needs to be strongly backed up.

Every claim you make must be backed up

Step 4: Acknowledge the limitations of your study

The fourth step in writing up your discussion chapter is to acknowledge the limitations of the study. These limitations can cover any part of your study , from the scope or theoretical basis to the analysis method(s) or sample. For example, you may find that you collected data from a very small sample with unique characteristics, which would mean that you are unable to generalise your results to the broader population.

For some students, discussing the limitations of their work can feel a little bit self-defeating . This is a misconception, as a core indicator of high-quality research is its ability to accurately identify its weaknesses. In other words, accurately stating the limitations of your work is a strength, not a weakness . All that said, be careful not to undermine your own research. Tell the reader what limitations exist and what improvements could be made, but also remind them of the value of your study despite its limitations.

Step 5: Make recommendations for implementation and future research

Now that you’ve unpacked your findings and acknowledge the limitations thereof, the next thing you’ll need to do is reflect on your study in terms of two factors:

  • The practical application of your findings
  • Suggestions for future research

The first thing to discuss is how your findings can be used in the real world – in other words, what contribution can they make to the field or industry? Where are these contributions applicable, how and why? For example, if your research is on communication in health settings, in what ways can your findings be applied to the context of a hospital or medical clinic? Make sure that you spell this out for your reader in practical terms, but also be realistic and make sure that any applications are feasible.

The next discussion point is the opportunity for future research . In other words, how can other studies build on what you’ve found and also improve the findings by overcoming some of the limitations in your study (which you discussed a little earlier). In doing this, you’ll want to investigate whether your results fit in with findings of previous research, and if not, why this may be the case. For example, are there any factors that you didn’t consider in your study? What future research can be done to remedy this? When you write up your suggestions, make sure that you don’t just say that more research is needed on the topic, also comment on how the research can build on your study.

Step 6: Provide a concluding summary

Finally, you’ve reached your final stretch. In this section, you’ll want to provide a brief recap of the key findings – in other words, the findings that directly address your research questions . Basically, your conclusion should tell the reader what your study has found, and what they need to take away from reading your report.

When writing up your concluding summary, bear in mind that some readers may skip straight to this section from the beginning of the chapter.  So, make sure that this section flows well from and has a strong connection to the opening section of the chapter.

Tips and tricks for an A-grade discussion chapter

Now that you know what the discussion chapter is , what to include and exclude , and how to structure it , here are some tips and suggestions to help you craft a quality discussion chapter.

  • When you write up your discussion chapter, make sure that you keep it consistent with your introduction chapter , as some readers will skip from the introduction chapter directly to the discussion chapter. Your discussion should use the same tense as your introduction, and it should also make use of the same key terms.
  • Don’t make assumptions about your readers. As a writer, you have hands-on experience with the data and so it can be easy to present it in an over-simplified manner. Make sure that you spell out your findings and interpretations for the intelligent layman.
  • Have a look at other theses and dissertations from your institution, especially the discussion sections. This will help you to understand the standards and conventions of your university, and you’ll also get a good idea of how others have structured their discussion chapters. You can also check out our chapter template .
  • Avoid using absolute terms such as “These results prove that…”, rather make use of terms such as “suggest” or “indicate”, where you could say, “These results suggest that…” or “These results indicate…”. It is highly unlikely that a dissertation or thesis will scientifically prove something (due to a variety of resource constraints), so be humble in your language.
  • Use well-structured and consistently formatted headings to ensure that your reader can easily navigate between sections, and so that your chapter flows logically and coherently.

If you have any questions or thoughts regarding this post, feel free to leave a comment below. Also, if you’re looking for one-on-one help with your discussion chapter (or thesis in general), consider booking a free consultation with one of our highly experienced Grad Coaches to discuss how we can help you.

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33 Comments

Abbie

Thank you this is helpful!

Sai AKO

This is very helpful to me… Thanks a lot for sharing this with us 😊

Nts'eoane Sepanya-Molefi

This has been very helpful indeed. Thank you.

Cheryl

This is actually really helpful, I just stumbled upon it. Very happy that I found it, thank you.

Solomon

Me too! I was kinda lost on how to approach my discussion chapter. How helpful! Thanks a lot!

Wongibe Dieudonne

This is really good and explicit. Thanks

Robin MooreZaid

Thank you, this blog has been such a help.

John Amaka

Thank you. This is very helpful.

Syed Firoz Ahmad

Dear sir/madame

Thanks a lot for this helpful blog. Really, it supported me in writing my discussion chapter while I was totally unaware about its structure and method of writing.

With regards

Syed Firoz Ahmad PhD, Research Scholar

Kwasi Tonge

I agree so much. This blog was god sent. It assisted me so much while I was totally clueless about the context and the know-how. Now I am fully aware of what I am to do and how I am to do it.

Albert Mitugo

Thanks! This is helpful!

Abduljabbar Alsoudani

thanks alot for this informative website

Sudesh Chinthaka

Dear Sir/Madam,

Truly, your article was much benefited when i structured my discussion chapter.

Thank you very much!!!

Nann Yin Yin Moe

This is helpful for me in writing my research discussion component. I have to copy this text on Microsoft word cause of my weakness that I cannot be able to read the text on screen a long time. So many thanks for this articles.

Eunice Mulenga

This was helpful

Leo Simango

Thanks Jenna, well explained.

Poornima

Thank you! This is super helpful.

William M. Kapambwe

Thanks very much. I have appreciated the six steps on writing the Discussion chapter which are (i) Restating the research problem and questions (ii) Summarising the key findings (iii) Interpreting the results linked to relating to previous results in positive and negative ways; explaining whay different or same and contribution to field of research and expalnation of findings (iv) Acknowledgeing limitations (v) Recommendations for implementation and future resaerch and finally (vi) Providing a conscluding summary

My two questions are: 1. On step 1 and 2 can it be the overall or you restate and sumamrise on each findings based on the reaerch question? 2. On 4 and 5 do you do the acknowlledgement , recommendations on each research finding or overall. This is not clear from your expalanattion.

Please respond.

Ahmed

This post is very useful. I’m wondering whether practical implications must be introduced in the Discussion section or in the Conclusion section?

Lisha

Sigh, I never knew a 20 min video could have literally save my life like this. I found this at the right time!!!! Everything I need to know in one video thanks a mil ! OMGG and that 6 step!!!!!! was the cherry on top the cake!!!!!!!!!

Colbey mwenda

Thanks alot.., I have gained much

Obinna NJOKU

This piece is very helpful on how to go about my discussion section. I can always recommend GradCoach research guides for colleagues.

Mary Kulabako

Many thanks for this resource. It has been very helpful to me. I was finding it hard to even write the first sentence. Much appreciated.

vera

Thanks so much. Very helpful to know what is included in the discussion section

ahmad yassine

this was a very helpful and useful information

Md Moniruzzaman

This is very helpful. Very very helpful. Thanks for sharing this online!

Salma

it is very helpfull article, and i will recommend it to my fellow students. Thank you.

Mohammed Kwarah Tal

Superlative! More grease to your elbows.

Majani

Powerful, thank you for sharing.

Uno

Wow! Just wow! God bless the day I stumbled upon you guys’ YouTube videos! It’s been truly life changing and anxiety about my report that is due in less than a month has subsided significantly!

Joseph Nkitseng

Simplified explanation. Well done.

LE Sibeko

The presentation is enlightening. Thank you very much.

Angela

Thanks for the support and guidance

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Academic Paper: Discussion and Analysis

5 min read • march 10, 2023

Dylan Black

Dylan Black

Introduction

After presenting your data and results to readers, you have one final step before you can finally wrap up your paper and write a conclusion: analyzing your data! This is the big part of your paper that finally takes all the stuff you've been talking about - your method, the data you collected, the information presented in your literature review - and uses it to make a point!

The major question to be answered in your analysis section is simply "we have all this data, but what does it mean?" What questions does this data answer? How does it relate to your research question ? Can this data be explained by, and is it consistent with, other papers? If not, why? These are the types of questions you'll be discussing in this section.

Source: GIPHY

Writing a Discussion and Analysis

Explain what your data means.

The primary point of a discussion section is to explain to your readers, through both statistical means and thorough explanation, what your results mean for your project. In doing so, you want to be succinct, clear, and specific about how your data backs up the claims you are making. These claims should be directly tied back to the overall focus of your paper.

What is this overall focus, you may ask? Your research question ! This discussion along with your conclusion forms the final analysis of your research - what answers did we find? Was our research successful? How do the results we found tie into and relate to the current consensus by the research community? Were our results expected or unexpected? Why or why not? These are all questions you may consider in writing your discussion section.

You showing off all of the cool findings of your research! Source: GIPHY

Why Did Your Results Happen?

After presenting your results in your results section, you may also want to explain why your results actually occurred. This is integral to gaining a full understanding of your results and the conclusions you can draw from them. For example, if data you found contradicts certain data points found in other studies, one of the most important aspects of your discussion of said data is going to be theorizing as to why this disparity took place.

Note that making broad, sweeping claims based on your data is not enough! Everything, and I mean just about everything you say in your discussions section must be backed up either by your own findings that you showed in your results section or past research that has been performed in your field.

For many situations, finding these answers is not easy, and a lot of thinking must be done as to why your results actually occurred the way they did. For some fields, specifically STEM-related fields, a discussion might dive into the theoretical foundations of your research, explaining interactions between parts of your study that led to your results. For others, like social sciences and humanities, results may be open to more interpretation.

However, "open to more interpretation" does not mean you can make claims willy nilly and claim "author's interpretation". In fact, such interpretation may be harder than STEM explanations! You will have to synthesize existing analysis on your topic and incorporate that in your analysis.

Liam Neeson explains the major question of your analysis. Source: GIPHY

Discussion vs. Summary & Repetition

Quite possibly the biggest mistake made within a discussion section is simply restating your data in a different format. The role of the discussion section is to explain your data and what it means for your project. Many students, thinking they're making discussion and analysis, simply regurgitate their numbers back in full sentences with a surface-level explanation.

Phrases like "this shows" and others similar, while good building blocks and great planning tools, often lead to a relatively weak discussion that isn't very nuanced and doesn't lead to much new understanding.

Instead, your goal will be to, through this section and your conclusion, establish a new understanding and in the end, close your gap! To do this effectively, you not only will have to present the numbers and results of your study, but you'll also have to describe how such data forms a new idea that has not been found in prior research.

This, in essence, is the heart of research - finding something new that hasn't been studied before! I don't know if it's just us, but that's pretty darn cool and something that you as the researcher should be incredibly proud of yourself for accomplishing.

Rubric Points

Before we close out this guide, let's take a quick peek at our best friend: the AP Research Rubric for the Discussion and Conclusion sections.

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Source: CollegeBoard

Scores of One and Two: Nothing New, Your Standard Essay

Responses that earn a score of one or two on this section of the AP Research Academic Paper typically don't find much new and by this point may not have a fully developed method nor well-thought-out results. For the most part, these are more similar to essays you may have written in a prior English class or AP Seminar than a true Research paper. Instead of finding new ideas, they summarize already existing information about a topic.

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Score of Three: New Understanding, Not Enough Support

A score of three is the first row that establishes a new understanding! This is a great step forward from a one or a two. However, what differentiates a three from a four or a five is the explanation and support of such a new understanding. A paper that earns a three lacks in building a line of reasoning and does not present enough evidence, both from their results section and from already published research.

Scores of Four and Five: New Understanding With A Line of Reasoning

We've made it to the best of the best! With scores of four and five, successful papers describe a new understanding with an effective line of reasoning, sufficient evidence, and an all-around great presentation of how their results signify filling a gap and answering a research question .

As far as the discussions section goes, the difference between a four and a five is more on the side of complexity and nuance. Where a four hits all the marks and does it well, a five exceeds this and writes a truly exceptional analysis. Another area where these two sections differ is in the limitations described, which we discuss in the Conclusion section guide.

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You did it!!!! You have, for the most part, finished the brunt of your research paper and are over the hump! All that's left to do is tackle the conclusion, which tends to be for most the easiest section to write because all you do is summarize how your research question was answered and make some final points about how your research impacts your field. Finally, as always...

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

researchers writing the discussion section of their research paper

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

  • How formal should it be?
  • What words should you use, or not use?
  • How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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  • How to write an APA methods section

How to Write an APA Methods Section | With Examples

Published on February 5, 2021 by Pritha Bhandari . Revised on June 22, 2023.

The methods section of an APA style paper is where you report in detail how you performed your study. Research papers in the social and natural sciences often follow APA style. This article focuses on reporting quantitative research methods .

In your APA methods section, you should report enough information to understand and replicate your study, including detailed information on the sample , measures, and procedures used.

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Table of contents

Structuring an apa methods section.

Participants

Example of an APA methods section

Other interesting articles, frequently asked questions about writing an apa methods section.

The main heading of “Methods” should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles .

To structure your methods section, you can use the subheadings of “Participants,” “Materials,” and “Procedures.” These headings are not mandatory—aim to organize your methods section using subheadings that make sense for your specific study.

Note that not all of these topics will necessarily be relevant for your study. For example, if you didn’t need to consider outlier removal or ways of assigning participants to different conditions, you don’t have to report these steps.

The APA also provides specific reporting guidelines for different types of research design. These tell you exactly what you need to report for longitudinal designs , replication studies, experimental designs , and so on. If your study uses a combination design, consult APA guidelines for mixed methods studies.

Detailed descriptions of procedures that don’t fit into your main text can be placed in supplemental materials (for example, the exact instructions and tasks given to participants, the full analytical strategy including software code, or additional figures and tables).

Prevent plagiarism. Run a free check.

Begin the methods section by reporting sample characteristics, sampling procedures, and the sample size.

Participant or subject characteristics

When discussing people who participate in research, descriptive terms like “participants,” “subjects” and “respondents” can be used. For non-human animal research, “subjects” is more appropriate.

Specify all relevant demographic characteristics of your participants. This may include their age, sex, ethnic or racial group, gender identity, education level, and socioeconomic status. Depending on your study topic, other characteristics like educational or immigration status or language preference may also be relevant.

Be sure to report these characteristics as precisely as possible. This helps the reader understand how far your results may be generalized to other people.

The APA guidelines emphasize writing about participants using bias-free language , so it’s necessary to use inclusive and appropriate terms.

Sampling procedures

Outline how the participants were selected and all inclusion and exclusion criteria applied. Appropriately identify the sampling procedure used. For example, you should only label a sample as random  if you had access to every member of the relevant population.

Of all the people invited to participate in your study, note the percentage that actually did (if you have this data). Additionally, report whether participants were self-selected, either by themselves or by their institutions (e.g., schools may submit student data for research purposes).

Identify any compensation (e.g., course credits or money) that was provided to participants, and mention any institutional review board approvals and ethical standards followed.

Sample size and power

Detail the sample size (per condition) and statistical power that you hoped to achieve, as well as any analyses you performed to determine these numbers.

It’s important to show that your study had enough statistical power to find effects if there were any to be found.

Additionally, state whether your final sample differed from the intended sample. Your interpretations of the study outcomes should be based only on your final sample rather than your intended sample.

Write up the tools and techniques that you used to measure relevant variables. Be as thorough as possible for a complete picture of your techniques.

Primary and secondary measures

Define the primary and secondary outcome measures that will help you answer your primary and secondary research questions.

Specify all instruments used in gathering these measurements and the construct that they measure. These instruments may include hardware, software, or tests, scales, and inventories.

  • To cite hardware, indicate the model number and manufacturer.
  • To cite common software (e.g., Qualtrics), state the full name along with the version number or the website URL .
  • To cite tests, scales or inventories, reference its manual or the article it was published in. It’s also helpful to state the number of items and provide one or two example items.

Make sure to report the settings of (e.g., screen resolution) any specialized apparatus used.

For each instrument used, report measures of the following:

  • Reliability : how consistently the method measures something, in terms of internal consistency or test-retest reliability.
  • Validity : how precisely the method measures something, in terms of construct validity  or criterion validity .

Giving an example item or two for tests, questionnaires , and interviews is also helpful.

Describe any covariates—these are any additional variables that may explain or predict the outcomes.

Quality of measurements

Review all methods you used to assure the quality of your measurements.

These may include:

  • training researchers to collect data reliably,
  • using multiple people to assess (e.g., observe or code) the data,
  • translation and back-translation of research materials,
  • using pilot studies to test your materials on unrelated samples.

For data that’s subjectively coded (for example, classifying open-ended responses), report interrater reliability scores. This tells the reader how similarly each response was rated by multiple raters.

Report all of the procedures applied for administering the study, processing the data, and for planned data analyses.

Data collection methods and research design

Data collection methods refers to the general mode of the instruments: surveys, interviews, observations, focus groups, neuroimaging, cognitive tests, and so on. Summarize exactly how you collected the necessary data.

Describe all procedures you applied in administering surveys, tests, physical recordings, or imaging devices, with enough detail so that someone else can replicate your techniques. If your procedures are very complicated and require long descriptions (e.g., in neuroimaging studies), place these details in supplementary materials.

To report research design, note your overall framework for data collection and analysis. State whether you used an experimental, quasi-experimental, descriptive (observational), correlational, and/or longitudinal design. Also note whether a between-subjects or a within-subjects design was used.

For multi-group studies, report the following design and procedural details as well:

  • how participants were assigned to different conditions (e.g., randomization),
  • instructions given to the participants in each group,
  • interventions for each group,
  • the setting and length of each session(s).

Describe whether any masking was used to hide the condition assignment (e.g., placebo or medication condition) from participants or research administrators. Using masking in a multi-group study ensures internal validity by reducing research bias . Explain how this masking was applied and whether its effectiveness was assessed.

Participants were randomly assigned to a control or experimental condition. The survey was administered using Qualtrics (https://www.qualtrics.com). To begin, all participants were given the AAI and a demographics questionnaire to complete, followed by an unrelated filler task. In the control condition , participants completed a short general knowledge test immediately after the filler task. In the experimental condition, participants were asked to visualize themselves taking the test for 3 minutes before they actually did. For more details on the exact instructions and tasks given, see supplementary materials.

Data diagnostics

Outline all steps taken to scrutinize or process the data after collection.

This includes the following:

  • Procedures for identifying and removing outliers
  • Data transformations to normalize distributions
  • Compensation strategies for overcoming missing values

To ensure high validity, you should provide enough detail for your reader to understand how and why you processed or transformed your raw data in these specific ways.

Analytic strategies

The methods section is also where you describe your statistical analysis procedures, but not their outcomes. Their outcomes are reported in the results section.

These procedures should be stated for all primary, secondary, and exploratory hypotheses. While primary and secondary hypotheses are based on a theoretical framework or past studies, exploratory hypotheses are guided by the data you’ve just collected.

This annotated example reports methods for a descriptive correlational survey on the relationship between religiosity and trust in science in the US. Hover over each part for explanation of what is included.

The sample included 879 adults aged between 18 and 28. More than half of the participants were women (56%), and all participants had completed at least 12 years of education. Ethics approval was obtained from the university board before recruitment began. Participants were recruited online through Amazon Mechanical Turk (MTurk; www.mturk.com). We selected for a geographically diverse sample within the Midwest of the US through an initial screening survey. Participants were paid USD $5 upon completion of the study.

A sample size of at least 783 was deemed necessary for detecting a correlation coefficient of ±.1, with a power level of 80% and a significance level of .05, using a sample size calculator (www.sample-size.net/correlation-sample-size/).

The primary outcome measures were the levels of religiosity and trust in science. Religiosity refers to involvement and belief in religious traditions, while trust in science represents confidence in scientists and scientific research outcomes. The secondary outcome measures were gender and parental education levels of participants and whether these characteristics predicted religiosity levels.

Religiosity

Religiosity was measured using the Centrality of Religiosity scale (Huber, 2003). The Likert scale is made up of 15 questions with five subscales of ideology, experience, intellect, public practice, and private practice. An example item is “How often do you experience situations in which you have the feeling that God or something divine intervenes in your life?” Participants were asked to indicate frequency of occurrence by selecting a response ranging from 1 (very often) to 5 (never). The internal consistency of the instrument is .83 (Huber & Huber, 2012).

Trust in Science

Trust in science was assessed using the General Trust in Science index (McCright, Dentzman, Charters & Dietz, 2013). Four Likert scale items were assessed on a scale from 1 (completely distrust) to 5 (completely trust). An example question asks “How much do you distrust or trust scientists to create knowledge that is unbiased and accurate?” Internal consistency was .8.

Potential participants were invited to participate in the survey online using Qualtrics (www.qualtrics.com). The survey consisted of multiple choice questions regarding demographic characteristics, the Centrality of Religiosity scale, an unrelated filler anagram task, and finally the General Trust in Science index. The filler task was included to avoid priming or demand characteristics, and an attention check was embedded within the religiosity scale. For full instructions and details of tasks, see supplementary materials.

For this correlational study , we assessed our primary hypothesis of a relationship between religiosity and trust in science using Pearson moment correlation coefficient. The statistical significance of the correlation coefficient was assessed using a t test. To test our secondary hypothesis of parental education levels and gender as predictors of religiosity, multiple linear regression analysis was used.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles

Methodology

  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias
  • Social desirability bias

In your APA methods section , you should report detailed information on the participants, materials, and procedures used.

  • Describe all relevant participant or subject characteristics, the sampling procedures used and the sample size and power .
  • Define all primary and secondary measures and discuss the quality of measurements.
  • Specify the data collection methods, the research design and data analysis strategy, including any steps taken to transform the data and statistical analyses.

You should report methods using the past tense , even if you haven’t completed your study at the time of writing. That’s because the methods section is intended to describe completed actions or research.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

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  • Charlesworth Author Services
  • 11 November, 2021

While it is more common for Science, Technology, Engineering and Mathematics (STEM) researchers to write separate, distinct chapters for their data/ results and analysis/ discussion , the same sections can feel less clearly defined for a researcher in Social Sciences, Arts and Humanities (SSAH). This article will look specifically at some useful approaches to writing the analysis and discussion chapters in qualitative/SSAH research.

Note : Most of the differences in approaches to research, writing, analysis and discussion come down, ultimately, to differences in epistemology – how we approach, create and work with knowledge in our respective fields. However, this is a vast topic that deserves a separate discussion.

Look for emerging themes and patterns

The ‘results’ of qualitative research can sometimes be harder to pinpoint than in quantitative research. You’re not dealing with definitive numbers and results in the same way as, say, a scientist conducting experiments that produce measurable data. Instead, most qualitative researchers explore prominent, interesting themes and patterns emerging from their data – that could comprise interviews, textual material or participant observation, for example. 

You may find that your data presents a huge number of themes, issues and topics, all of which you might find equally significant and interesting. In fact, you might find yourself overwhelmed by the many directions that your research could take, depending on which themes you choose to study in further depth. You may even discover issues and patterns that you had not expected , that may necessitate having to change or expand the research focus you initially started off with.

It is crucial at this point not to panic. Instead, try to enjoy the many possibilities that your data is offering you. It can be useful to remind yourself at each stage of exactly what you are trying to find out through this research.

What exactly do you want to know? What knowledge do you want to generate and share within your field?

Then, spend some time reflecting upon each of the themes that seem most interesting and significant, and consider whether they are immediately relevant to your main, overarching research objectives and goals.

Suggestion: Don’t worry too much about structure and flow at the early stages of writing your discussion . It would be a more valuable use of your time to fully explore the themes and issues arising from your data first, while also reading widely alongside your writing (more on this below). As you work more intimately with the data and develop your ideas, the overarching narrative and connections between those ideas will begin to emerge. Trust that you’ll be able to draw those links and craft the structure organically as you write.

Let your data guide you

A key characteristic of qualitative research is that the researchers allow their data to ‘speak’ and guide their research and their writing. Instead of insisting too strongly upon the prominence of specific themes and issues and imposing their opinions and beliefs upon the data, a good qualitative researcher ‘listens’ to what the data has to tell them.

Again, you might find yourself having to address unexpected issues or your data may reveal things that seem completely contradictory to the ideas and theories you have worked with so far. Although this might seem worrying, discovering these unexpected new elements can actually make your research much richer and more interesting. 

Suggestion: Allow yourself to follow those leads and ask new questions as you work through your data. These new directions could help you to answer your research questions in more depth and with greater complexity; or they could even open up other avenues for further study, either in this or future research.

Work closely with the literature

As you analyse and discuss the prominent themes, arguments and findings arising from your data, it is very helpful to maintain a regular and consistent reading practice alongside your writing. Return to the literature that you’ve already been reading so far or begin to check out new texts, studies and theories that might be more appropriate for working with any new ideas and themes arising from your data.

Reading and incorporating relevant literature into your writing as you work through your analysis and discussion will help you to consistently contextualise your research within the larger body of knowledge. It will be easier to stay focused on what you are trying to say through your research if you can simultaneously show what has already been said on the subject and how your research and data supports, challenges or extends those debates. By drawing from existing literature , you are setting up a dialogue between your research and prior work, and highlighting what this research has to add to the conversation.

Suggestion : Although it might sometimes feel tedious to have to blend others’ writing in with yours, this is ultimately the best way to showcase the specialness of your own data, findings and research . Remember that it is more difficult to highlight the significance and relevance of your original work without first showing how that work fits into or responds to existing studies. 

In conclusion

The discussion chapters form the heart of your thesis and this is where your unique contribution comes to the forefront. This is where your data takes centre-stage and where you get to showcase your original arguments, perspectives and knowledge. To do this effectively needs you to explore the original themes and issues arising from and within the data, while simultaneously contextualising these findings within the larger, existing body of knowledge of your specialising field. By striking this balance, you prove the two most important qualities of excellent qualitative research : keen awareness of your field and a firm understanding of your place in it.

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  • Open access
  • Published: 17 April 2018

Writing a discussion section: how to integrate substantive and statistical expertise

  • Michael Höfler   ORCID: orcid.org/0000-0001-7646-8265 1 , 5 ,
  • John Venz 1 , 2 ,
  • Sebastian Trautmann 1 , 2 &
  • Robert Miller 3 , 4  

BMC Medical Research Methodology volume  18 , Article number:  34 ( 2018 ) Cite this article

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When discussing results medical research articles often tear substantive and statistical (methodical) contributions apart, just as if both were independent. Consequently, reasoning on bias tends to be vague, unclear and superficial. This can lead to over-generalized, too narrow and misleading conclusions, especially for causal research questions.

To get the best possible conclusion, substantive and statistical expertise have to be integrated on the basis of reasonable assumptions. While statistics should raise questions on the mechanisms that have presumably created the data, substantive knowledge should answer them. Building on the related principle of Bayesian thinking, we make seven specific and four general proposals on writing a discussion section.

Misinterpretation could be reduced if authors explicitly discussed what can be concluded under which assumptions. Informed on the resulting conditional conclusions other researchers may, according to their knowledge and beliefs, follow a particular conclusion or, based on other conditions, arrive at another one. This could foster both an improved debate and a better understanding of the mechanisms behind the data and should therefore enable researchers to better address bias in future studies.

Peer Review reports

After a research article has presented the substantive background, the methods and the results, the discussion section assesses the validity of results and draws conclusions by interpreting them. The discussion puts the results into a broader context and reflects their implications for theoretical (e.g. etiological) and practical (e.g. interventional) purposes. As such, the discussion contains an article’s last words the reader is left with.

Common recommendations for the discussion section include general proposals for writing [ 1 ] and structuring (e.g. with a paragraph on a study’s strengths and weaknesses) [ 2 ], to avoid common statistical pitfalls (like misinterpreting non-significant findings as true null results) [ 3 ] and to “go beyond the data” when interpreting results [ 4 ]. Note that the latter includes much more than comparing an article’s results with the literature. If results and literature are consistent, this might be due to shared bias only. If they are not consistent, the question arises why inconsistency occurs – maybe because of bias acting differently across studies [ 5 , 6 , 7 ]. Recommendations like the CONSORT checklist do well in demanding all quantitative information on design, participation, compliance etc. to be reported in the methods and results section and “addressing sources of potential bias”, “limitations” and “considering other relevant evidence” in the discussion [ 8 , 9 ]. Similarly, the STROBE checklist for epidemiological research demands “a cautious overall interpretation of results” and "discussing the generalizability (external validity)" [ 10 , 11 ]. However, these guidelines do not clarify how to deal with the complex bias issue, and how to get to and report conclusions.

Consequently, suggestions on writing a discussion often remain vague by hardly addressing the role of the assumptions that have (often implicitly) been made when designing a study, analyzing the data and interpreting the results. Such assumptions involve mechanisms that have created the data and are related to sampling, measurement and treatment assignment (in observational studies common causes of factor and outcome) and, as a consequence, the bias this may produce [ 5 , 6 ]. They determine whether a result allows only an associational or a causal conclusion. Causal conclusions, if true, are of much higher relevance for etiology, prevention and intervention. However, they require much stronger assumptions. These have to be fully explicit and, therewith, essential part of the debate since they always involve subjectivity. Subjectivity is unavoidable because the mechanisms behind the data can never be fully estimated from the data themselves [ 12 ].

In this article, we argue that the conjunction of substantive and statistical (methodical) knowledge in the verbal integration of results and beliefs on mechanisms can be greatly improved in (medical) research papers. We illustrate this through the personal roles that a statistician (i.e. methods expert) and a substantive researcher should take. Doing so, we neither claim that usually just two people write a discussion, nor that one person lacks the knowledge of the other, nor that there were truly no researchers that have both kinds of expertise. As a metaphor, the division of these two roles into two persons describes the necessary integration of knowledge via the mode of a dialogue. Verbally, it addresses the finding of increased specialization of different study contributors in biomedical research. This has teared apart the two processes of statistical compilation of results and their verbal integration [ 13 ]. When this happens a statistician alone is limited to a study’s conditions (sampled population, experimental settings etc.), because he or she is unaware of the conditions’ generalizability. On the other hand, a A substantive expert alone is prone to over-generalize because he or she is not aware of the (mathematical) prerequisites for an interpretation.

The article addresses both (medical) researchers educated in basic statistics and research methods and statisticians who cooperate with them. Throughout the paper we exemplify our arguments with the finding of an association in a cross-tabulation between a binary X (factor) and a binary Y (outcome): those who are exposed to or treated with X have a statistically significantly elevated risk for Y as compared to the non-exposed or not (or otherwise) treated (for instance via the chi-squared independence test or logistic regression). Findings like this are frequent and raise the question which more profound conclusion is valid under what assumptions. Until some decades ago, statistics has largely avoided the related topic of causality and instead limited itself on describing observed distributions (here a two-by-two table between D = depression and LC = lung cancer) with well-fitting models.

We illustrate our arguments with the concrete example of the association found between the factor depression (D) and the outcome lung cancer (LC) [ 14 ]. Yet very different mechanisms could have produced such an association [ 7 ], and assumptions on these lead to the following fundamentally different conclusions (Fig. 1 ):

D causes LC (e.g. because smoking might constitute “self-medication” of depression symptoms)

LC causes D (e.g. because LC patients are demoralized by their diagnosis)

D and LC cause each other (e.g. because the arguments in both a. and b. apply)

D and LC are the causal consequence of the same factor(s) (e.g. poor health behaviors - HB)

D and LC only share measurement error (e.g. because a fraction of individuals that has either depression or lung cancer denies both in self-report measures).

Different conclusions about an association between D and LC. a D causes LC, b LC causes B, c D and LC cause each other, d D and LC are associated because of a shared factor (HB), e D and LC are associated because they have correlated errors

Note that we use the example purely for illustrative purposes. We do not make substantive claims on what of a. through e. is true but show how one should reflect on mechanisms in order to find the right answer. Besides, we do not consider research on the D-LC relation apart from the finding of association [ 14 ].

Assessing which of a. through e. truly applies requires substantive assumptions on mechanisms: the temporal order of D and LC (a causal effect requires that the cause occurs before the effect), shared factors, selection processes and measurement error. Questions on related mechanisms have to be brought up by statistical consideration, while substantive reasoning has to address them. Together this yields provisional assumptions for inferring that are subject to readers’ substantive consideration and refinement. In general, the integration of prior beliefs (anything beyond the data a conclusion depends on) and the results from the data themselves is formalized by Bayesian statistics [ 15 , 16 ]. This is beyond the scope of this article, still we argue that Bayesian thinking should govern the process of drawing conclusions.

Building on this idea, we provide seven specific and four general recommendations for the cooperative process of writing a discussion. The recommendations are intended to be suggestions rather than rules. They should be subject to further refinement and adjustment to specific requirements in different fields of medical and other research. Note that the order of the points is not meant to structure a discussion’s writing (besides 1.).

Recommendations for writing a discussion section

Specific recommendations.

Start the discussion with the conclusion your design and results unambiguously allow

Consider the example on the association between D and LC. Rather than starting with an in-depth (causal) interpretation a finding should firstly be taken as what it allows inferring without doubt: Under the usual assumptions that a statistical model makes (e.g. random sampling, independence or certain correlation structure between observations [ 17 ]), the association indicates that D (strictly speaking: measuring D) predicts an elevated LC risk (strictly speaking: measuring LC) in the population that one has managed to sample (source population). Assume that the sample has been randomly drawn from primary care settings. In this case the association is useful to recommend medical doctors to better look at an individual’s LC risk in case of D. If the association has been adjusted for age and gender (conveniently through a regression model), the conclusion modifies to: If the doctor knows a patient’s age and gender (what should always be the case) D has additional value in predicting an elevated LC risk.

Mention the conclusion(s) that researchers would like to draw

In the above example, a substantive researcher might want to conclude that D and LC are associated in a general population instead of just inferring to patients in primary care settings (a.). Another researcher might even take the finding as evidence for D being a causal factor in the etiology of LC, meaning that prevention of D could reduce the incidence rate of LC (in whatever target population) (b.). In both cases, the substantive researcher should insist on assessing the desired interpretation that goes beyond the data [ 4 ], but the statistician immediately needs to bring up the next point.

Specify all assumptions to interprete the observed result in the desired (causal) way

The explanation of all the assumptions that lead from a data result to a conclusion enables a reader to assess whether he or she agrees with the authors’ inference or not. These conditions, however, often remain incomplete or unclear, in which case the reader can hardly assess whether he or she follows a path of argumentation and, thus, shares the conclusion this path leads to.

Consider conclusion a. and suppose that, instead of representative sampling in a general population (e.g. all U.S. citizens aged 18 or above), the investigators were only able to sample in primary care settings. Extrapolating the results to another population than the source population requires what is called “external validity”, “transportability” or the absence of “selection bias” [ 18 , 19 ]. No such bias occurs if the parameter of interest is equal in the source and the target population. Note that this is a weaker condition than the common belief that the sample must represent the target population in everything . If the parameter of interest is the difference in risk for LC between cases and non-cases of D, the condition translates into: the risk difference must be equal in target and source population.

For the causal conclusion b., however, sufficient assumptions are very strict. In an RCT, the conclusion is valid under random sampling from the target population, random allocation of X, perfect compliance in X, complete participation and no measurement error in outcome (for details see [ 20 ]). In practice, on the other hand, the derivations from such conditions might sometimes be modest what may produce little bias only. For instance, non-compliance in a specific drug intake (treatment) might occur only in a few individuals to little extent through a random process (e.g. sickness of a nurse being responsible for drug dispense) and yield just small (downward) bias [ 5 ]. The conclusion of downward bias might also be justified if non-compliance does not cause anything that has a larger effect on a Y than the drug itself. Another researcher, however, could believe that non-compliance leads to taking a more effective, alternative treatment. He or she could infer upward bias instead if well-informed on the line of argument.

Otherwise avoid causal language

In practice, researchers frequently use causal language yet without mentioning any assumptions. This does not imply that they truly have a causal effect in mind, often causal and associational wordings are carelessly used in synonymous way. For example, concluding “depression increases the risk of lung cancer” constitutes already causal wording because it implies that a change in the depression status would change the cancer risk. Associational language like “lung cancer risk is elevated if depression occurs”, however, would allow for an elevated lung cancer risk in depression cases just because LC and D share some causes (“inducing” or “removing” depression would not change the cancer risk here).

Reflect critically on how deviations from the assumptions would have influenced the results

Often, it is unclear where the path of argumentation from assumptions to a conclusion leads when alternative assumptions are made. Consider again bias due to selection. A different effect in target and source population occurs if effect-modifying variables distribute differently in both populations. Accordingly, the statistician should ask which variables influence the effect of interest, and whether these can be assumed to distribute equally in the source population and the target population. The substantive researcher might answer that the causal risk difference between D and LC likely increases with age. Given that this is true, and if elder individuals have been oversampled (e.g. because elderly are over-represented in primary care settings), both together would conclude that sampling has led to over-estimation (despite other factors, Fig. 2 ).

If higher age is related to a larger effect (risk difference) of D on LC, a larger effect estimate is expected in an elder sample

However, the statistician might add, if effect modification is weak, or the difference in the age distributions is modest (e.g. mean 54 vs. 52 years), selection is unlikely to have produced large (here: upward) bias. In turn, another substantive researcher, who reads the resulting discussion, might instead assume a decrease of effect with increasing age and thus infer downward bias.

In practice, researchers should be extremely sensitive for bias due to selection if a sample has been drawn conditionally on a common consequence of factor and outcome or a variable associated with such a consequence [19 and references therein]. For instance, hospitalization might be influenced by both D and LC, and thus sampling from hospitals might introduce a false association or change an association’s sign; particularly D and LC may appear to be negatively associated although the association is positive in the general population (Fig. 3 ).

If hospitalization (H) is a common cause of D and LC, sampling conditionally on H can introduce a spurious association between D and LC ("conditioning on a collider")

Comment on all main types of bias and the inferential consequences they putatively have

Usually, only some kinds of bias are discussed, while the consequences of others are ignored [ 5 ]. Besides selection the main sources of bias are often measurement and confounding. If one is only interested in association, confounding is irrelevant. For causal conclusions, however, assumptions on all three kinds of bias are necessary.

Measurement error means that the measurement of a factor and/or outcome deviates from the true value, at least in some individuals. Bias due to measurement is known under many other terms that describe the reasons why such error occurs (e.g. “recall bias” and “reporting bias”). In contrast to conventional wisdom, measurement error does not always bias association and effect estimates downwards [ 5 , 6 ]. It does, for instance, if only the factor (e.g. depression) is measured with error and the errors occur independently from the outcome (e.g. lung cancer), or vice versa (“non-differential misclassification”) [22 and references therein]. However, many lung cancer cases might falsely report depression symptoms (e.g. to express need for care). Such false positives (non-cases of depression classified as cases) may also occur in non-cases of lung cancer but to a lesser extent (a special case of “differential misclassification”). Here, bias might be upward as well. Importantly, false positives cause larger bias than false negatives (non-cases of depression falsely classified as depression cases) as long as the relative frequency of a factor is lower than 50% [ 21 ]. Therefore, they should receive more attention in discussion. If measurement error occurs in depression and lung cancer, the direction of bias also depends on the correlation between both errors [ 21 ].

Note that what is in line with common standards of “good” measurement (e.g. a Kappa value measuring validity or reliability of 0.7) might anyway produce large bias. This applies to estimates of prevalence, association and effect. The reason is that while indices of measurement are one-dimensional, bias depends on two parameters (sensitivity and specificity) [ 21 , 22 ]. Moreover, estimates of such indices are often extrapolated to different kinds of populations (typically from a clinical to general population), what may be inadequate. Note that the different kinds of bias often interact, e.g. bias due to measurement might depend on selection (e.g. measurement error might differ between a clinical and a general population) [ 5 , 6 ].

Assessment of bias due to confounding variables (roughly speaking: common causes of factor and outcome) requires assumptions on the entire system of variables that affect both factor and outcome. For example, D and LC might share several causes such as stressful life events or socioeconomic status. If these influence D and LC with the same effect direction, this leads to overestimation, otherwise (different effect directions) the causal effect is underestimated. In the medical field, many unfavorable conditions may be positively related. If this holds true for all common factors of D and LC, upward bias can be assumed. However, not all confounders have to be taken into account. Within the framework of “causal graphs”, the “backdoor criterion” [ 7 ] provides a graphical rule for sets of confounders to be sufficient when adjusted for. Practically, such a causal graph must include all factors that directly or indirectly affect both D and LC. Then, adjustment for a set of confounders that meets the “backdoor criterion” in the graph completely removes bias due to confounding. In the example of Fig. 4 it is sufficient to adjust for Z 1 and Z 2 because this “blocks” all paths that otherwise lead backwards from D to LC. Note that fully eliminating bias due to confounding also requires that the confounders have been collected without measurement error [ 5 , 6 , 23 ]. Therefore, the advice is always to concede at least some “residual” bias and reflect on the direction this might have (could be downward if such error is not stronger related to D and LC than a confounder itself).

Whereas the statistician should pinpoint to the mathematical insight of the backdoor criterion, its application requires profound substantive input and literature review. Of course, there are numerous relevant factors in the medical field. Hence, one should practically focus on those with the highest prevalence (a very seldom factor can hardly cause large bias) and large assumed effects on both X and Y.

Causal graph for the effect of D on LC and confounders Z 1 , Z 2 and Z 3

If knowledge on any of the three kinds of bias is poor or very uncertain, researchers should admit that this adds uncertainty in a conclusion: systematic error on top of random error. In the Bayesian framework, quantitative bias analysis formalizes this through the result of larger variance in an estimate. Technically, this additional variance is introduced via the variances of distributions assigned to “bias parameters”; for instance a misclassification probability (e.g. classifying a true depression case as non-case) or the prevalence of a binary confounder and its effects on X and Y. Of course, bias analysis also changes point estimates (hopefully reducing bias considerably). Note that conventional frequentist analysis, as regarded from the Bayesian perspective, assumes that all bias parameters were zero with a probability of one [ 5 , 6 , 23 ]. The only exceptions (bias addressed in conventional analyses) are adjustment on variables to hopefully reduce bias due to confounding and weighting the individuals (according to variables related to participation) to take into account bias due to selection.

If the substantive investigator understands the processes of selection, measurement and confounding only poorly, such strict analysis numerically reveals that little to nothing is known on the effect of X on Y, no matter how large an observed association and a sample (providing small random error) may be [ 5 , 6 , 23 ]). This insight has to be brought up by the statistician. Although such an analysis is complicated, itself very sensitive to how it is conducted [ 5 , 6 ] and rarely done, the Bayesian thinking behind it forces researchers to better understand the processes behind the data. Otherwise, he or she cannot make any assumptions and, in turn, no conclusion on causality.

Propose a specific study design that requires less and weaker assumptions for a conclusion

Usually articles end with statements that only go little further than the always true but never informative statement “more research is needed”. Moreover, larger samples and better measurements are frequently proposed. If an association has been found, a RCT or other interventional study is usually proposed to investigate causality. In our example, this recommendation disregards that: (1) onset of D might have a different effect on LC risk than an intervention against D (the effect of onset cannot be investigated in any interventional study), (2) the effects of onset and intervention concern different populations (those without vs. those with depression), (3) an intervention effect depends on the mode of intervention [ 24 ], and (4) (applying the backdoor criterion) a well-designed observational study may approximatively yield the same result as a randomized study would [ 25 , 26 , 27 ]. If the effect of “removing” depression is actually of interest, one could propose an RCT that investigates the effect of treating depression in a strictly defined way and in a strictly defined population (desirably in all who meet the criteria of depression). Ideally, this population is sampled randomly, and non-participants and dropouts are investigated with respect to assumed effect-modifiers (differences in their distributions between participants and non-participants can then be addressed e.g. by weighting [ 27 ]). In a non-randomized study, one should collect variables supposed to meet the backdoor-criterion with the best instruments possible.

General recommendations

Yet when considering 1) through 7); i.e. carefully reflecting on the mechanisms that have created the data, discussions on statistical results can be very misleading, because the basic statistical methods are mis-interpreted or inadequately worded.

Don’t mistake the absence of evidence as evidence for absence

A common pitfall is to consider the lack of evidence for the alternative hypothesis (e.g. association between D and LC) as evidence for the null hypothesis (no association). In fact, such inference requires an a-priori calculated sample-size to ensure that the type-two error probability does not exceed a pre-specified limit (typically 20% or 10%, given the other necessary assumptions, e.g. on the true magnitude of association). Otherwise, the type-two error is unknown and in practice often large. This may put a “false negative result” into the scientific public that turns out to be “unreplicable” – what would be falsely interpreted as part of the “replication crisis”. Such results are neither positive nor negative but uninformative . In this case, the wording “there is no evidence for an association” is adequate because it does not claim that there is no association.

Strictly distinguish between discussing pre-specified hypotheses and newly proposed hypotheses from post-hoc analyses

Frequently, it remains unclear which hypotheses have been a-priori specified and which have been brought up only after some data analysis. This, of course, is scientific malpractice because it does not enable the readership to assess the random error emerging from explorative data analysis. Accordingly, the variance of results across statistical methods is often misused to filter out the analysis that yields a significant result (“ p -hacking”, [ 28 ]). Pre-planned tests (via writing a grant) leave at least less room for p-hacking because they specify a-priori which analysis is to be conducted.

On the other hand, post-hoc analyses can be extremely useful for identifying unexpected phenomena and creating new hypotheses. Verbalization in the discussion section should therefore sharply separate between conclusions from hypothesis testing and new hypotheses created from data exploration. The distinction is profound, since a newly proposed hypothesis just makes a new claim. Suggesting new hypotheses cannot be wrong, this can only be inefficient if many hypotheses turn out to be wrong. Therefore, we suggest proposing only a limited number of new hypotheses that appear promising to stimulate further research and scientific progress. They are to be confirmed or falsified with future studies. A present discussion, however, should yet explicate the testable predictions a new hypothesis entails, and how a future study should be designed to keep bias in related analyses as small as possible.

Confidence intervals address the problem of reducing results to the dichotomy of significant and non-significant through providing a range of values that are compatible with the data at the given confidence level, usually 95% [ 29 ].

This is also addressed by Bayesian statistics that allows calculating what frequentist p -values are often misinterpreted to be: the probability that the alternative (or null) hypothesis is true [ 17 ]. Moreover, one can calculate how likely it is that the parameter lies within any specified range (e.g. the risk difference being greater than .05, a lower boundary for practical significance) [ 15 , 16 ]. To gain these benefits, one needs to specify how the parameter of interest (e.g. causal risk difference between D and LC) is distributed before inspecting the data. In Bayesian statistics (unlike frequentist statistics) a parameter is a random number that expresses prior beliefs via a “prior distribution”. Such a “prior” is combined with the data result to a “posterior distribution”. This integrates both sources of information.

Note that confidence intervals also can be interpreted from the Bayesian perspective (then called “credibility interval”). This assumes that all parameter values were equally likely (uniformly distributed, strictly speaking) before analyzing the data [ 5 , 6 , 20 ].

Do not over-interpret small findings. Statistical significance should not be mis-interpreted as practical significance

Testing just for a non-zero association can only yield evidence for an association deviating from zero. A better indicator for the true impact of an effect/association for clinical, economic, political, or research purposes is its magnitude. If an association between D and LC after adjusting for age and gender has been discovered, then the knowledge of D has additional value in predicting an elevated LC probability beyond age and gender. However, there may be many other factors that stronger predict LC and thus should receive higher priority in a doctor’s assessment. Besides, if an association is small, it may yet be explained by modest (upward) bias. Especially large samples often yield significant results with little practical value. The p -value does not measure strength of association [ 17 ]. For instance, in a large sample, a Pearson correlation between two dimensional variables could equal 0.1 only but with a p -value <.001. A further problem arises if the significance threshold of .05 is weakened post-hoc to allow for “statistical trends” ( p between .05 and .10) because a result has “failed to reach significance” (this wording claims that there is truly an association. If this was known, no research would be necessary).

It is usually the statistician’s job to insist not only on removing the attention from pure statistical significance to confidence intervals or even Bayesian interpretation, but also to point out the necessity of a meaningful cutoff for practical significance. The substantive researcher then has to provide this cutoff.

Avoid claims that are not statistically well-founded

Researchers should not draw conclusions that have not been explicitly tested for. For example, one may have found a positive association between D and LC (e.g. p  = .049), but this association is not significant (e.g. p  = .051), when adjusting for “health behavior”. This does not imply that “health behavior” “explains” the association (yet fully). The difference in magnitude of association in both analyses compared here (without and with adjustment on HB) may be very small and the difference in p -values (“borderline significance” after adjustment) likely to emerge from random error. This often applies to larger differences in p as well.

Investigators, however, might find patterns in their results that they consider worth mentioning for creating hypotheses. In the example above, adding the words “in the sample”, would clarify that they refer just to the difference of two point estimates . By default, “association” in hypotheses testing should mean “statistically significant association” (explorative analyses should instead refer to “suggestive associations”).

Conclusions

Some issues of discussing results not mentioned yet appear to require only substantive reasoning. For instance, Bradford Hill’s consideration on “plausibility” claims that a causal effect is more likely, if it is in line with biological (substantive) knowledge, or if a dose-response relation has been found [ 30 ]. However, the application of these considerations itself depends on the trueness of assumptions. For instance, bias might act differently across the dose of exposure (e.g. larger measurement error in outcome among those with higher dosage). As a consequence, a pattern observed across dose may mask a true or pretend a wrong dose-response relation [ 30 ]. This again has to be brought up by statistical expertise.

There are, however, some practical issues that hinder the cooperation we suggest. First, substantive researchers often feel discomfort when urged to make assumptions on the mechanisms behind the data, presumably because they fear to be wrong. Here, the statistician needs to insist: “If you are unable to make any assumptions, you cannot conclude anything!” And: “As a scientist you have to understand the processes that create your data.” See [ 31 ] for practical advice on how to arrive at meaningful assumptions.

Second, statisticians have long been skeptical against causal inference. Still, most of them focus solely on describing observed data with distributional models, probably because estimating causal effects has long been regarded as unfeasible with scientific methods. Training in causality remains rather new, since strict mathematical methods have been developed only in the last decades [ 7 ].

The cooperation could be improved if education in both fields focused on the insight that one cannot succeed without the other. Academic education should demonstrate that in-depth conclusions from data unavoidably involve prior beliefs. Such education should say: Data do not “speak for themselves”, because they “speak” only ambiguously and little, since they have been filtered through various biases [ 32 ]. The subjectivity introduced by addressing bias, however, unsettles many researchers. On the other hand, conventional frequentist statistics just pretends to be objective. Instead of accepting the variety of possible assumptions, it makes the absurd assumption of “no bias with probability of one”. Or it avoids causal conclusions at all if no randomized study is possible. This limits science to investigating just associations for all factors that can never be randomized (e.g. onset of depression). However, the alternative of Bayesian statistics and thinking are themselves prone to fundamental cognitive biases which should as well be subject of interdisciplinary teaching [ 33 ].

Readers may take this article as an invitation to read further papers’ discussions differently while evaluating our claims. Rather than sharing a provided conclusion (or not) they could ask themselves whether a discussion enables them to clearly specify why they share it (or not). If the result is uncertainty, this might motivate them to write their next discussion differently. The proposals made in this article could help shifting scientific debates to where they belong. Rather than arguing on misunderstandings caused by ambiguity in a conclusion’s assumptions one should argue on the assumptions themselves.

Abbreviations

health behavior

lung cancer

randomized clinical trial

factor variable

outcome variable

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Acknowledgements

We acknowledge support by the German Research Foundation and the Open Access Publication Funds of the TU Dresden. We wish to thank Pia Grabbe and Helen Steiner for language editing and the cited authors for their outstanding work that our proposals build on.

John Venz is funded by the German Federal Ministry of Education and Research (BMBF) project no. 01ER1303 and 01ER1703. He has contributed to this manuscript outside of time funded by these projects.

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MH and RM had the initial idea on the article. MH has taken the lead in writing. JV has contributed to the statistical parts, especially the Bayesian aspects. RM has refined the paragraphs on statistical inference. ST joined later and has added many clarifications related to the perspective of the substantive researcher. All authors have contributed to the final wording of all sections and the article’s revision. All authors read and approved the final manuscript.

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Höfler, M., Venz, J., Trautmann, S. et al. Writing a discussion section: how to integrate substantive and statistical expertise. BMC Med Res Methodol 18 , 34 (2018). https://doi.org/10.1186/s12874-018-0490-1

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Discussion Section Examples and Writing Tips

Abstract | Introduction | Literature Review | Research question | Materials & Methods | Results | Discussion | Conclusion

In this blog, we look at how to write the discussion section of a research paper. We will go through plenty of discussion examples and understand how to construct a great discussion section for your research paper.

1. What is the purpose of the discussion section?

Discussion example

The discussion section is one of the most important sections of your research paper. This is where you interpret your results, highlight your contributions, and explain the value of your work to your readers.  This is one of the challenging parts to write because the author must clearly explain the significance of their results and tie everything back to the research questions.

2. How should I structure my discussion section?

Generally, the discussion section of a research paper typically contains the following parts.

Research summary It is a good idea to start this section with an overall summary of your work and highlight the main findings of your research.

Interpretation of findings You must interpret your findings clearly to your readers one by one.

Comparison with literature You must talk about how your results fit into existing research in the literature.

Implications of your work You should talk about the implications and possible benefits of your research.

Limitations You should talk about the possible limitations and shortcomings of your research

Future work And finally, you can talk about the possible future directions of your work.

3. Discussion Examples

Let’s look at some examples of the discussion section.  We will be looking at discussion examples from different fields and of different formats. We have split this section into multiple components so that it is easy for you to digest and understand.

3.1. An example of research summary in discussion

It is a good idea to start your discussion section with the summary of your work. The best way to do this will be to restate your research question, and then reminding your readers about your methods, and finally providing an overall summary of your results.

Our aims were to compare the effectiveness and user-friendliness of different storm detection software for storm tracking. On the basis of these aims, we ran multiple experiments with the same conditions using different storm detection software. Our results showed that in both speed and accuracy of data, ‘software A’ performed better than ‘software B’. _  Aims summary  _  Methodology summary  _  Results summary

This discussion example is from an engineering research paper. The authors are restating their aims first, which is to compare different types of storm-tracking software. Then, they are providing a brief summary of the methods. Here, they are testing different storm-tracking software under different conditions to see which performs the best. Then, they are finally providing their main finding which is that they found ‘software A’ better than ‘software B’.  This is a very good example of how to start the discussion section by presenting a summary of your work.

3.2. An example of result interpretation in discussion

The next step is to interpret your results. You have to explain your results clearly to your readers. Here is a discussion example that shows how to interpret your results.

The results of this study indicate significant differences between classical music and pop music in terms of their effects on memory recall and cognition. This implies that as the complexity of the music increases, so does its ability to facilitate cognitive processing. This finding aligns with the well-known “Mozart effect,” which suggests that listening to classical music can enhance cognitive function. _  Result  _  Interpretation  _   Additional evidence

The authors are saying that their results show that there is a significant difference between pop music and classical music in terms of memory recall and cognition. Now they are providing their interpretation of the findings. They think it is because there is a link between the complexity of music and cognitive processing. They are also making a reference to a well-known theory called the ‘Mozart effect’ to back up their findings. It is a nicely written passage and the author’s interpretation sounds very convincing and credible.

3.3. An example of literature comparison in discussion

The next step is to compare your results to the literature. You have to explain clearly how your findings compare with similar findings made by other researchers. Here is a discussion example where authors are providing details of papers in the literature that both support and oppose their findings.

Our analysis predicts that climate change will have a significant impact on wheat yield. This finding undermines one of the central pieces of evidence in some previous simulation studies [1-3] that suggest a negative effect of climate change on wheat yield, but the result is entirely consistent with the predictions of other research [4-5] that suggests the overall change in climate could result in increases in wheat yield. _  Result  _  Comparison with literature

The authors are saying that their results show that climate change will have a significant effect on wheat production. Then, they are saying that there are some papers in the literature that are in agreement with their findings. However, there are also many papers in the literature that disagree with their findings. This is very important. Your discussion should be two-sided, not one-sided. You should not ignore the literature that doesn’t corroborate your findings.

3.4. An example of research implications in discussion

The next step is to explain to your readers how your findings will benefit society and the research community. You have to clearly explain the value of your work to your readers. Here is a discussion example where authors explain the implications of their research.

The results contribute insights with regard to the management of wildfire events using artificial intelligence. One could easily argue that the obvious practical implication of this study is that it proposes utilizing cloud-based machine vision to detect wildfires in real-time, even before the first responders receive emergency calls. _  Your finding  _  Implications of your finding

In this paper, the authors are saying that their findings indicate that Artificial intelligence can be used to effectively manage wildfire events. Then, they are talking about the practical implications of their study. They are saying that their work has proven that machine learning can be used to detect wildfires in real-time. This is a great practical application and can save thousands of lives. As you can see, after reading this passage, you can immediately understand the value and significance of the work.

3.5. An example of limitations in discussion

It is very important that you discuss the limitations of your study. Limitations are flaws and shortcomings of your study. You have to tell your readers how your limitations might influence the outcomes and conclusions of your research. Most studies will have some form of limitation. So be honest and don’t hide your limitations. In reality, your readers and reviewers will be impressed with your paper if you are upfront about your limitations. 

Study design and small sample size are important limitations. This could have led to an overestimation of the effect. Future research should reconfirm these findings by conducting larger-scale studies. _  Limitation  _  How it might affect the results?  _   How to fix the limitation?

Here is a discussion example where the author talks about study limitations. The authors are saying that the main limitations of the study are the small sample size and weak study design. Then they explain how this might have affected their results. They are saying that it is possible that they are overestimating the actual effect they are measuring. Then finally they are telling the readers that more studies with larger sample sizes should be conducted to reconfirm the findings.

As you can see, the authors are clearly explaining three things here:

3.6. An example of future work in discussion

It is important to remember not to end your paper with limitations. Finish your paper on a positive note by telling your readers about the benefits of your research and possible future directions. Here is a discussion example where the author talks about future work.

Our study highlights useful insights about the potential of biomass as a renewable energy source. Future research can extend this research in several ways, including research on how to tackle challenges that hinder the sustainability of renewable energy sources towards climate change mitigation, such as market failures, lack of information and access to raw materials.   _  Benefits of your work  _   Future work

The authors are starting the final paragraph of the discussion section by highlighting the benefit of their work which is the use of biomass as a renewable source of energy. Then they talk about future research. They are saying that future research can focus on how to improve the sustainability of biomass production. This is a very good example of how to finish the discussion section of your paper on a positive note.

4. Frequently Asked Questions

Sometimes you will have negative or unexpected results in your paper. You have to talk about it in your discussion section. A lot of students find it difficult to write this part. The best way to handle this situation is not to look at results as either positive or negative. A result is a result, and you will always have something important and interesting to say about your findings. Just spend some time investigating what might have caused this result and tell your readers about it.

You must talk about the limitations of your work in the discussion section of the paper. One of the important qualities that the scientific community expects from a researcher is honesty and admitting when they have made a mistake. The important trick you have to learn while presenting your limitations is to present them in a constructive way rather than being too negative about them.  You must try to use positive language even when you are talking about major limitations of your work. 

If you have something exciting to say about your results or found something new that nobody else has found before, then, don’t be modest and use flat language when presenting this in the discussion. Use words like ‘break through’, ‘indisputable evidence’, ‘exciting proposition’ to increase the impact of your findings.

Important thing to remember is not to overstate your findings. If you found something really interesting but are not 100% sure, you must not mislead your readers. The best way to do this will be to use words like ‘it appears’ and ‘it seems’. This will tell the readers that there is a slight possibility that you might be wrong.

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Research Paper – Structure, Examples and Writing Guide

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Research Paper

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.

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07 Easy Steps for Writing Discussion Section of a Research Paper

Discussion Section of Research Paper

Introduction

I. focus on the relevance.

  • II. Highlight  the Limitations 
  • III. Introduce  New Discoveries

IV. Highlight the Observations

V. compare and relate with other research works.

  • VI. Provide  Alternate View Points

A. Future Directions

B. conclusion, how to validate the claims i made in the discussion section of my research paper, phrases that can be used in the discussion section of a research paper, phrases that can be used in the analysis part of the discussion section of a research paper.

  • Your Next Move...

Whether charts and graphs are allowed in discussion section of my Research Paper?

Can i add citations in discussion section of my research paper, can i combine results and discussion section in my research paper, what is the weightage of discussion section in a research paper in terms of selection to a journal, whether literature survey paper has a discussion section.

The Discussion section of a research paper is where authors interpret their findings, contextualize their research, and propose future directions. It is a crucial section that provides the reader with insights into the significance and implications of the study.

Writing an effective discussion section is a crucial aspect of any research paper, as it allows researchers to delve into the significance of their findings and explore their implications. A well-crafted discussion section not only summarizes the key observations and limitations of the study but also establishes connections with existing research and opens avenues for future exploration. In this article, we will present a comprehensive guide to help you structure your discussion section in seven simple steps.

By following these steps, you’ll be able to write a compelling Discussion section that enhances the reader’s understanding of your research and contributes to the broader scientific community.

Please note, the discussion section usually follows after the Results Section. I have written a comprehensive article on ” How to Write Results Section of your Research Paper “. Please visit the article to enhance your write-up on the results section.

Which are these 07 steps for writing an Effective Discussion Section of a Research Paper?

Step 1: Focus on the Relevance : In the first step, we will discuss the importance of emphasizing the relevance of your research findings to the broader scientific context. By clearly articulating the significance of your study, you can help readers understand how your work contributes to the existing body of knowledge and why it matters.

Step 2: Highlight the Limitations : Every research study has its limitations, and it is essential to address them honestly and transparently. We will explore how to identify and describe the limitations of your study, demonstrating a thorough understanding of potential weaknesses and areas for improvement.

Step 3: Highlight the Observations : In this step, we will delve into the core findings of your study. We will discuss the key observations and results, focusing on their relevance to your research objectives. By providing a concise summary of your findings, you can guide readers through the main outcomes of your study.

Step 4: Compare and Relate with Other Research Works : Research is a collaborative and cumulative process, and it is vital to establish connections between your study and previous research. We will explore strategies to compare and relate your findings to existing literature, highlighting similarities, differences, and gaps in knowledge.

Step 5: Provide Alternate Viewpoints: Science thrives on the diversity of perspectives. Acknowledging different viewpoints and interpretations of your results fosters a more comprehensive understanding of the research topic. We will discuss how to incorporate alternative viewpoints into your discussion, encouraging a balanced and nuanced analysis.

Step 6: Show Future Directions : A well-crafted discussion section not only summarizes the present but also points towards the future. We will explore techniques to suggest future research directions based on the implications of your study, providing a roadmap for further investigations in the field.

Step 7: Concluding Thoughts : In the final step, we will wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers.

By following these seven steps, you can craft a comprehensive and insightful discussion section that not only synthesizes your findings but also engages readers in a thought-provoking dialogue about the broader implications and future directions of your research. Let’s delve into each step in detail to enhance the quality and impact of your discussion section.

The purpose of every research is to implement the results for the positive development of the relevant subject. In research, it is crucial to emphasize the relevance of your study to the field and its potential impact. Before delving into the details of how the research was conceived and the sequence of developments that took place, consider highlighting the following factors to establish the relevance of your work:

  • Identifying a pressing problem or research gap: Example: “This research addresses the critical problem of network security in wireless communication systems. With the widespread adoption of wireless networks, the vulnerability to security threats has increased significantly. Existing security mechanisms have limitations in effectively mitigating these threats. Therefore, there is a pressing need to develop novel approaches that enhance the security of wireless communication systems.”
  • Explaining the significance and potential impact of the research: Example: “By developing an intelligent intrusion detection system using machine learning algorithms, this research aims to significantly enhance the security of wireless networks. The successful implementation of such a system would not only protect sensitive data and communication but also ensure the reliability and integrity of wireless networks in various applications, including Internet of Things (IoT), smart cities, and critical infrastructure.”
  • Establishing connections with previous research and advancements in the field: Example: “This study builds upon previous research on intrusion detection systems and machine learning techniques. By leveraging recent advancements in deep learning algorithms and anomaly detection methods, we aim to overcome the limitations of traditional rule-based intrusion detection systems and achieve higher detection accuracy and efficiency.”

By emphasizing the relevance of your research and articulating its potential impact, you set the stage for readers to understand the significance of your work in the broader context. This approach ensures that readers grasp the motivations behind your research and the need for further exploration in the field.

II. Highlight  the Limitations 

Many times the research is on a subject that might have legal limitations or restrictions. This limitation might have caused certain imperfections in carrying out research or in results. This issue should be acknowledged by the researcher before the work is criticized by others later in his/her discussion section.

In computer science research, it is important to identify and openly acknowledge the limitations of your study. By doing so, you demonstrate transparency and a thorough understanding of potential weaknesses, allowing readers to interpret the findings in a more informed manner. Here’s an example:

Example: “It is crucial to acknowledge certain limitations and constraints that have affected the outcomes of this research. In the context of privacy-sensitive applications such as facial recognition systems, there are legal limitations and ethical concerns that can impact the accuracy and performance of the developed algorithm. These limitations stem from regulations and policies that impose restrictions on data collection, access, and usage to protect individuals’ privacy rights. As a result, the algorithm developed in this study operates under these legal constraints, which may have introduced certain imperfections.”

In this example, the researcher is working on a facial recognition system and acknowledges the legal limitations and ethical concerns associated with privacy-sensitive applications. By openly addressing these limitations, the researcher demonstrates an understanding of the challenges imposed by regulations and policies. This acknowledgement sets the stage for a more nuanced discussion and prevents others from solely criticizing the work based on these limitations without considering the broader legal context.

By highlighting the limitations, researchers can also offer potential solutions or future directions to mitigate the impact of these constraints. For instance, the researcher may suggest exploring advanced privacy-preserving techniques or collaborating with legal experts to find a balance between privacy protection and system performance.

By acknowledging and addressing the limitations, researchers demonstrate their awareness of potential weaknesses in their study, maintaining credibility, and fostering a more constructive discussion of their findings within the context of legal and ethical considerations.

III. Introduce  New Discoveries

Begin the discussion section by stating all the major findings in the course of the research. The first paragraph should have the findings mentioned, which is expected to be synoptic, naming and briefly describing the analysis of results.

Example: “In this study, several significant discoveries emerged from the analysis of the collected data. The findings revealed compelling insights into the performance of parallel computing architectures for large-scale data processing. Through comprehensive experimentation and analysis, the following key discoveries were made:

  • Discovery 1: The proposed parallel computing architecture demonstrated a 30% improvement in processing speed compared to traditional sequential computing methods. This finding highlights the potential of parallel computing for accelerating data-intensive tasks.
  • Discovery 2: A direct relationship between the number of processing cores and the overall system throughput was observed. As the number of cores increased, the system exhibited a near-linear scalability, enabling efficient utilization of available computational resources.
  • Discovery 3: The analysis revealed a trade-off between processing speed and energy consumption. While parallel computing achieved faster processing times, it also resulted in higher energy consumption. This finding emphasizes the importance of optimizing energy efficiency in parallel computing systems.

These discoveries shed light on the performance characteristics and trade-offs associated with parallel computing architectures for large-scale data processing tasks. The following sections will delve into the implications of these findings, discussing their significance, limitations, and potential applications.”

In this example, the researcher presents a concise overview of the major discoveries made during the research. Each discovery is briefly described, highlighting the key insights obtained from the analysis. By summarizing the findings in a synoptic manner, the reader gains an immediate understanding of the notable contributions and can anticipate the subsequent detailed discussion.

This approach allows the discussion section to begin with a clear and impactful introduction of the major discoveries, capturing the reader’s interest and setting the stage for a comprehensive exploration of each finding in subsequent paragraphs.

Coming to the major part of the findings, the discussion section should interpret the key observations, the analysis of charts, and the analysis of tables. In the field of computer science, presenting and explaining the results in a clear and accessible manner is essential for readers to grasp the significance of the findings. Here are some examples of how to effectively highlight observations in computer science research:

Begin with explaining the objective of the research, followed by what inspired you as a researcher to study the subject:

In a study on machine learning algorithms for sentiment analysis, start by stating the goal of developing an accurate and efficient sentiment analysis model. Share your motivation for choosing this research topic, such as the increasing importance of sentiment analysis in various domains like social media, customer feedback analysis, and market research.

Example: The objective of this research was to develop a sentiment analysis model using machine learning algorithms. As sentiment analysis plays a vital role in understanding public opinion and customer feedback, we were motivated by the need for an accurate and efficient model that could be applied in various domains such as social media analysis, customer reviews, and market research.

Explain the meaning of the findings, as every reader might not understand the analysis of graphs and charts as easily as people who are in the same field as you:

If your research involves analyzing performance metrics of different algorithms, consider presenting the results in a visually intuitive manner, such as line graphs or bar charts. In the discussion section, explain the significance of the trends observed in the graphs. For instance, if a particular algorithm consistently outperforms others in terms of accuracy, explain why this finding is noteworthy and how it aligns with existing knowledge in the field.

Example: To present the performance evaluation of the algorithms, we analyzed multiple metrics, including precision, recall, and F1 score. The line graph in Figure 1 demonstrates the trends observed. It is noteworthy that Algorithm A consistently outperformed the other algorithms across all metrics. This finding indicates that Algorithm A has a higher ability to accurately classify sentiment in comparison to its counterparts. This aligns with previous studies that have also highlighted the robustness of Algorithm A in sentiment analysis tasks.

Ensure the reader can understand the key observations without being forced to go through the whole paper:

In computer science research, it is crucial to present concise summaries of your key observations to facilitate understanding for readers who may not have the time or expertise to go through the entire paper. For example, if your study compares the runtime performance of two programming languages for a specific task, clearly state the observed differences and their implications. Highlight any unexpected or notable findings that may challenge conventional wisdom or open up new avenues for future exploration.

Example: In this study comparing the runtime performance of Python and Java for a specific computational task, we observed notable differences. Python consistently showed faster execution times, averaging 20% less time than Java across varying input sizes. These results challenge the common perception that Java is the superior choice for computationally intensive tasks. The observed performance advantage of Python in this context suggests the need for further investigation into the underlying factors contributing to this discrepancy, such as differences in language design and optimization strategies.

By employing these strategies, researchers can effectively highlight their observations in the discussion section. This enables readers to gain a clear understanding of the significance of the findings and their implications without having to delve into complex technical details.

No one is ever the only person researching a particular subject. A researcher always has companions and competitors. The discussion section should have a detailed comparison of the research. It should present the facts that relate the research to studies done on the same subject.

Example: The table below compares some of the well-known prediction techniques with our fuzzy predictor with MOM defuzzification for response time, relative error and Environmental constraints. Based on the results obtained it can be concluded that the Fuzzy predictor with MOM defuzzification has a less relative error and quick response time as compared to other prediction techniques.  The proposed predictor is more flexible, simple to implement and deals with noisy and uncertain data from real-life situations. The relative error of 5-10% is acceptable for our system as the predicted fuzzy region and the fuzzy region of the actual position remains the same.

Table 1 : Comparison of well-known Robot Motion prediction Techniques

VI. Provide  Alternate View Points

Almost every time, it has been noticed that analysis of charts and graphs shows results that tend to have more than one explanation. The researcher must consider every possible explanation and potential enhancement of the study from alternative viewpoints. It is critically important that this is clearly put out to the readers in the discussion section.

In the discussion section of a research paper, it is important to acknowledge that data analysis often yields results that can be interpreted in multiple ways. By considering different viewpoints and potential enhancements, researchers can provide a more comprehensive and nuanced analysis of their findings. Here are some examples:

Example 1: “The analysis of our experimental data showed a decrease in system performance following the implementation of the proposed optimization technique. While our initial interpretation suggested that the optimization failed to achieve the desired outcome, an alternate viewpoint could be that the decrease in performance was influenced by an external factor, such as the configuration of the hardware setup. Further investigation into the hardware settings and benchmarking protocols is necessary to fully understand the observed results and identify potential enhancements.”

Example 2: “The analysis of user feedback revealed a mixed response to the redesigned user interface. While some participants reported improved usability and satisfaction, others expressed confusion and dissatisfaction. An alternate viewpoint could be that the diverse range of user backgrounds and preferences might have influenced these varied responses. Further research should focus on conducting user studies with a larger and more diverse sample to gain a deeper understanding of the underlying factors contributing to the contrasting user experiences.”

Example 3: “Our study found a positive correlation between the implementation of agile methodologies and project success rates. However, an alternate viewpoint suggests that other factors, such as team dynamics and project complexity, could have influenced the observed correlation. Future research should explore the interactions between agile methodologies and these potential confounding factors to gain a more comprehensive understanding of their impact on project success.”

In these examples, researchers present alternative viewpoints that offer different interpretations or explanations for the observed results. By acknowledging these alternate viewpoints, researchers demonstrate a balanced and comprehensive analysis of their findings. It is crucial to clearly communicate these alternative perspectives to readers in the discussion section, as it encourages critical thinking and highlights the complexity and potential limitations of the research.

By presenting alternate viewpoints, researchers invite further exploration and discussion, fostering a more comprehensive understanding of the research topic. This approach enriches the scientific discourse and promotes a deeper analysis of the findings, contributing to the overall advancement of knowledge in the field.

VII. Future Directions and Conclusion

The section must have suggestions for research that should be done to unanswered questions. These should be suggested at the beginning of the discussion section to avoid questions being asked by critics. Emphasizing the importance of following future directions can lead to new research as well.

Example: ” While this study provides valuable insights into the performance of the proposed algorithm, there are several unanswered questions and avenues for future research that merit attention. By identifying these areas, we aim to stimulate further exploration and contribute to the continuous advancement of the field. The following future directions are suggested:

  • Future Direction 1: Investigating the algorithm’s performance under different dataset characteristics and distributions. The current study focused on a specific dataset, but it would be valuable to evaluate the algorithm’s robustness and generalizability across a broader range of datasets, including real-world scenarios and diverse data sources.
  • Future Direction 2: Exploring the potential integration of additional machine learning techniques or ensemble methods to further enhance the algorithm’s accuracy and reliability. By combining the strengths of multiple models, it is possible to achieve better performance and handle complex patterns and outliers more effectively.
  • Future Direction 3: Extending the evaluation to consider the algorithm’s scalability in large-scale deployment scenarios. As the volume of data continues to grow exponentially, it is crucial to assess the algorithm’s efficiency and scalability in handling big data processing requirements.

By suggesting these future directions, we hope to inspire researchers to explore new avenues and build upon the foundation laid by this study. Addressing these unanswered questions will contribute to a more comprehensive understanding of the algorithm’s capabilities and limitations, paving the way for further advancements in the field.”

In this example, the researcher presents specific future directions that can guide further research. Each future direction is described concisely, highlighting the specific area of investigation and the potential benefits of pursuing those directions. By suggesting these future directions early in the discussion section, the researcher proactively addresses potential questions or criticisms and demonstrates a proactive approach to knowledge expansion.

By emphasizing the importance of following future directions, researchers not only inspire others to continue the research trajectory but also contribute to the collective growth of the field. This approach encourages ongoing exploration, innovation, and collaboration, ensuring the continuous development and improvement of computer science research.

In the final step, wrap up the discussion section by summarizing the key points and emphasizing the overall implications of your research. We will discuss the significance of your study’s contributions and offer some closing thoughts to leave a lasting impression on your readers. This section serves as a crucial opportunity to reinforce the main findings and highlight the broader impact of your work. Here are some examples:

Example 1: “In conclusion, this research has made significant contributions to the field of natural language processing. By proposing a novel neural network architecture for language generation, we have demonstrated the effectiveness and versatility of the model in generating coherent and contextually relevant sentences. The experimental results indicate a significant improvement in language generation quality compared to existing approaches. The implications of this research extend beyond traditional applications, opening up new possibilities for automated content creation, chatbot systems, and dialogue generation in artificial intelligence.”

Example 2: “In summary, this study has provided valuable insights into the optimization of network routing protocols for wireless sensor networks. By proposing a novel hybrid routing algorithm that combines the advantages of both reactive and proactive protocols, we have demonstrated enhanced network performance in terms of latency, energy efficiency, and scalability. The experimental results validate the effectiveness of the proposed algorithm in dynamic and resource-constrained environments. These findings have implications for various applications, including environmental monitoring, industrial automation, and smart city infrastructure.”

Example 3: “In closing, this research sheds light on the security vulnerabilities of blockchain-based smart contracts. By conducting an extensive analysis of existing smart contract platforms and identifying potential attack vectors, we have highlighted the need for robust security measures to mitigate risks and protect user assets. The insights gained from this study can guide the development of more secure and reliable smart contract frameworks, ensuring the integrity and trustworthiness of blockchain-based applications across industries such as finance, supply chain, and decentralized applications.”

In these examples, the concluding thoughts summarize the main contributions and findings of the research. They emphasize the significance of the study’s implications and highlight the potential impact on various domains within computer science. By providing a succinct and impactful summary, the researcher leaves a lasting impression on readers, reinforcing the value and relevance of the research in the field.

Validating claims in the discussion section of a research paper is essential to ensure the credibility and reliability of your findings. Here are some strategies to validate the claims made in the discussion section:

  • Referencing supporting evidence: Cite relevant sources from the existing literature that provide evidence or support for your claims. These sources can include peer-reviewed studies, research articles, and authoritative sources in your field. By referencing credible and reputable sources, you establish the validity of your claims and demonstrate that your interpretations are grounded in existing knowledge.
  • Relating to the results: Connect your claims to the results presented in the earlier sections of your research paper. Clearly demonstrate how the findings support your claims and provide evidence for your interpretations. Refer to specific data, measurements, statistical analyses, or other evidence from your results section to substantiate your claims.
  • Comparing with previous research: Discuss how your findings align with or diverge from previous research in the field. Reference relevant studies and explain how your results compare to or build upon existing knowledge. By contextualizing your claims within the broader research landscape, you provide further validation for your interpretations.
  • Addressing limitations and alternative explanations: Acknowledge the limitations of your study and consider alternative explanations for your findings. By addressing potential counterarguments and alternative viewpoints, you demonstrate a thorough evaluation of your claims and increase the robustness of your conclusions.
  • Seeking peer feedback: Prior to submitting your research paper, consider seeking feedback from colleagues or experts in your field. They can provide valuable insights and suggestions for further validating your claims or improving the clarity of your arguments.
  • Inviting replication and further research: Encourage other researchers to replicate your study or conduct further investigations. By promoting replication and future research, you contribute to the ongoing validation and refinement of your claims.

Remember, the validation of claims in the discussion section is a critical aspect of scientific research. By employing rigorous methods and logical reasoning, you can strengthen the credibility and impact of your findings and contribute to the advancement of knowledge in your field.

Here are some common phrases that can be used in the discussion section of a paper or research article. I’ve included a table with examples to illustrate how these phrases might be used:

Here are some common academic phrases that can be used in the analysis section of a paper or research article. I have included a table with examples to illustrate how these phrases might be used:

Your Next Move…

I believe you will proceed to write conclusion section of your research paper. Conclusion section is the most neglected part of the research paper as many authors feel it is unnecessary but write in a hurry to submit the article to some reputed journal.

Please note, once your paper gets published , the readers decide to read your full paper based only on abstract and conclusion. They decide the relevance of the paper based on only these two sections. If they don’t read then they don’t cite and this in turn affects your citation score. So my sincere advice to you is not to neglect this section.

Visit my article on “How to Write Conclusion Section of Research Paper” for further details.

Please visit my article on “ Importance and Improving of Citation Score for Your Research Paper ” for increasing your visibility in research community and on Google Scholar Citation Score.

The Discussion section of a research paper is an essential part of any study, as it allows the author to interpret their results and contextualize their findings. To write an effective Discussion section, authors should focus on the relevance of their research, highlight the limitations, introduce new discoveries, highlight their observations, compare and relate their findings to other research works, provide alternate viewpoints, and show future directions.

By following these 7 steps, authors can ensure that their Discussion section is comprehensive, informative, and thought-provoking. A well-written Discussion section not only helps the author interpret their results but also provides insights into the implications and applications of their research.

In conclusion, the Discussion section is an integral part of any research paper, and by following these 7 steps, authors can write a compelling and informative discussion section that contributes to the broader scientific community.

Frequently Asked Questions

Yes, charts and graphs are generally allowed in the discussion section of a research paper. While the discussion section is primarily focused on interpreting and discussing the findings, incorporating visual aids such as charts and graphs can be helpful in presenting and supporting the analysis.

Yes, you can add citations in the discussion section of your research paper. In fact, it is highly recommended to support your statements, interpretations, and claims with relevant and credible sources. Citations in the discussion section help to strengthen the validity and reliability of your arguments and demonstrate that your findings are grounded in existing literature.

Combining the results and discussion sections in a research paper is a common practice in certain disciplines, particularly in shorter research papers or those with specific formatting requirements. This approach can help streamline the presentation of your findings and provide a more cohesive narrative. However, it is important to note that the decision to combine these sections should be based on the guidelines of the target journal or publication and the specific requirements of your field.

The weightage of the discussion section in terms of the selection of a research paper for publication in a journal can vary depending on the specific requirements and criteria of the journal. However, it is important to note that the discussion section is a critical component of a research paper as it allows researchers to interpret their findings, contextualize them within the existing literature, and discuss their implications.

In general, literature survey papers typically do not have a separate section explicitly labeled as “Discussion.” However, the content of a literature survey paper often incorporates elements of discussion throughout the paper. The focus of a literature survey paper is to review and summarize existing literature on a specific topic or research question, rather than presenting original research findings.

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  • How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

Discussion section flow chart

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

  • Summary: A brief recap of your key results
  • Interpretations: What do your results mean?
  • Implications: Why do your results matter?
  • Limitations: What can’t your results tell us?
  • Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

  • Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
  • Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
  • Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

  • The results indicate that …
  • The study demonstrates a correlation between …
  • This analysis supports the theory that …
  • The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

  • Identifying correlations , patterns, and relationships among the data
  • Discussing whether the results met your expectations or supported your hypotheses
  • Contextualising your findings within previous research and theory
  • Explaining unexpected results and evaluating their significance
  • Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

  • In line with the hypothesis …
  • Contrary to the hypothesised association …
  • The results contradict the claims of Smith (2007) that …
  • The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

  • Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
  • Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

  • These results build on existing evidence of …
  • The results do not fit with the theory that …
  • The experiment provides a new insight into the relationship between …
  • These results should be taken into account when considering how to …
  • The data contribute a clearer understanding of …
  • While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

  • If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
  • If you encountered problems when gathering or analysing data, explain how these influenced the results.
  • If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

  • The generalisability of the results is limited by …
  • The reliability of these data is impacted by …
  • Due to the lack of data on x , the results cannot confirm …
  • The methodological choices were constrained by …
  • It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

  • Further research is needed to establish …
  • Future studies should take into account …
  • Avenues for future research include …

Discussion section example

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Research Paper Analysis: How to Analyze a Research Article + Example

Why might you need to analyze research? First of all, when you analyze a research article, you begin to understand your assigned reading better. It is also the first step toward learning how to write your own research articles and literature reviews. However, if you have never written a research paper before, it may be difficult for you to analyze one. After all, you may not know what criteria to use to evaluate it. But don’t panic! We will help you figure it out!

In this article, our team has explained how to analyze research papers quickly and effectively. At the end, you will also find a research analysis paper example to see how everything works in practice.

  • 🔤 Research Analysis Definition

📊 How to Analyze a Research Article

✍️ how to write a research analysis.

  • 📝 Analysis Example
  • 🔎 More Examples

🔗 References

🔤 research paper analysis: what is it.

A research paper analysis is an academic writing assignment in which you analyze a scholarly article’s methodology, data, and findings. In essence, “to analyze” means to break something down into components and assess each of them individually and in relation to each other. The goal of an analysis is to gain a deeper understanding of a subject. So, when you analyze a research article, you dissect it into elements like data sources , research methods, and results and evaluate how they contribute to the study’s strengths and weaknesses.

📋 Research Analysis Format

A research analysis paper has a pretty straightforward structure. Check it out below!

Research articles usually include the following sections: introduction, methods, results, and discussion. In the following paragraphs, we will discuss how to analyze a scientific article with a focus on each of its parts.

This image shows the main sections of a research article.

How to Analyze a Research Paper: Purpose

The purpose of the study is usually outlined in the introductory section of the article. Analyzing the research paper’s objectives is critical to establish the context for the rest of your analysis.

When analyzing the research aim, you should evaluate whether it was justified for the researchers to conduct the study. In other words, you should assess whether their research question was significant and whether it arose from existing literature on the topic.

Here are some questions that may help you analyze a research paper’s purpose:

  • Why was the research carried out?
  • What gaps does it try to fill, or what controversies to settle?
  • How does the study contribute to its field?
  • Do you agree with the author’s justification for approaching this particular question in this way?

How to Analyze a Paper: Methods

When analyzing the methodology section , you should indicate the study’s research design (qualitative, quantitative, or mixed) and methods used (for example, experiment, case study, correlational research, survey, etc.). After that, you should assess whether these methods suit the research purpose. In other words, do the chosen methods allow scholars to answer their research questions within the scope of their study?

For example, if scholars wanted to study US students’ average satisfaction with their higher education experience, they could conduct a quantitative survey . However, if they wanted to gain an in-depth understanding of the factors influencing US students’ satisfaction with higher education, qualitative interviews would be more appropriate.

When analyzing methods, you should also look at the research sample . Did the scholars use randomization to select study participants? Was the sample big enough for the results to be generalizable to a larger population?

You can also answer the following questions in your methodology analysis:

  • Is the methodology valid? In other words, did the researchers use methods that accurately measure the variables of interest?
  • Is the research methodology reliable? A research method is reliable if it can produce stable and consistent results under the same circumstances.
  • Is the study biased in any way?
  • What are the limitations of the chosen methodology?

How to Analyze Research Articles’ Results

You should start the analysis of the article results by carefully reading the tables, figures, and text. Check whether the findings correspond to the initial research purpose. See whether the results answered the author’s research questions or supported the hypotheses stated in the introduction.

To analyze the results section effectively, answer the following questions:

  • What are the major findings of the study?
  • Did the author present the results clearly and unambiguously?
  • Are the findings statistically significant ?
  • Does the author provide sufficient information on the validity and reliability of the results?
  • Have you noticed any trends or patterns in the data that the author did not mention?

How to Analyze Research: Discussion

Finally, you should analyze the authors’ interpretation of results and its connection with research objectives. Examine what conclusions the authors drew from their study and whether these conclusions answer the original question.

You should also pay attention to how the authors used findings to support their conclusions. For example, you can reflect on why their findings support that particular inference and not another one. Moreover, more than one conclusion can sometimes be made based on the same set of results. If that’s the case with your article, you should analyze whether the authors addressed other interpretations of their findings .

Here are some useful questions you can use to analyze the discussion section:

  • What findings did the authors use to support their conclusions?
  • How do the researchers’ conclusions compare to other studies’ findings?
  • How does this study contribute to its field?
  • What future research directions do the authors suggest?
  • What additional insights can you share regarding this article? For example, do you agree with the results? What other questions could the researchers have answered?

This image shows how to analyze a research article.

Now, you know how to analyze an article that presents research findings. However, it’s just a part of the work you have to do to complete your paper. So, it’s time to learn how to write research analysis! Check out the steps below!

1. Introduce the Article

As with most academic assignments, you should start your research article analysis with an introduction. Here’s what it should include:

  • The article’s publication details . Specify the title of the scholarly work you are analyzing, its authors, and publication date. Remember to enclose the article’s title in quotation marks and write it in title case .
  • The article’s main point . State what the paper is about. What did the authors study, and what was their major finding?
  • Your thesis statement . End your introduction with a strong claim summarizing your evaluation of the article. Consider briefly outlining the research paper’s strengths, weaknesses, and significance in your thesis.

Keep your introduction brief. Save the word count for the “meat” of your paper — that is, for the analysis.

2. Summarize the Article

Now, you should write a brief and focused summary of the scientific article. It should be shorter than your analysis section and contain all the relevant details about the research paper.

Here’s what you should include in your summary:

  • The research purpose . Briefly explain why the research was done. Identify the authors’ purpose and research questions or hypotheses .
  • Methods and results . Summarize what happened in the study. State only facts, without the authors’ interpretations of them. Avoid using too many numbers and details; instead, include only the information that will help readers understand what happened.
  • The authors’ conclusions . Outline what conclusions the researchers made from their study. In other words, describe how the authors explained the meaning of their findings.

If you need help summarizing an article, you can use our free summary generator .

3. Write Your Research Analysis

The analysis of the study is the most crucial part of this assignment type. Its key goal is to evaluate the article critically and demonstrate your understanding of it.

We’ve already covered how to analyze a research article in the section above. Here’s a quick recap:

  • Analyze whether the study’s purpose is significant and relevant.
  • Examine whether the chosen methodology allows for answering the research questions.
  • Evaluate how the authors presented the results.
  • Assess whether the authors’ conclusions are grounded in findings and answer the original research questions.

Although you should analyze the article critically, it doesn’t mean you only should criticize it. If the authors did a good job designing and conducting their study, be sure to explain why you think their work is well done. Also, it is a great idea to provide examples from the article to support your analysis.

4. Conclude Your Analysis of Research Paper

A conclusion is your chance to reflect on the study’s relevance and importance. Explain how the analyzed paper can contribute to the existing knowledge or lead to future research. Also, you need to summarize your thoughts on the article as a whole. Avoid making value judgments — saying that the paper is “good” or “bad.” Instead, use more descriptive words and phrases such as “This paper effectively showed…”

Need help writing a compelling conclusion? Try our free essay conclusion generator !

5. Revise and Proofread

Last but not least, you should carefully proofread your paper to find any punctuation, grammar, and spelling mistakes. Start by reading your work out loud to ensure that your sentences fit together and sound cohesive. Also, it can be helpful to ask your professor or peer to read your work and highlight possible weaknesses or typos.

This image shows how to write a research analysis.

📝 Research Paper Analysis Example

We have prepared an analysis of a research paper example to show how everything works in practice.

No Homework Policy: Research Article Analysis Example

This paper aims to analyze the research article entitled “No Assignment: A Boon or a Bane?” by Cordova, Pagtulon-an, and Tan (2019). This study examined the effects of having and not having assignments on weekends on high school students’ performance and transmuted mean scores. This article effectively shows the value of homework for students, but larger studies are needed to support its findings.

Cordova et al. (2019) conducted a descriptive quantitative study using a sample of 115 Grade 11 students of the Central Mindanao University Laboratory High School in the Philippines. The sample was divided into two groups: the first received homework on weekends, while the second didn’t. The researchers compared students’ performance records made by teachers and found that students who received assignments performed better than their counterparts without homework.

The purpose of this study is highly relevant and justified as this research was conducted in response to the debates about the “No Homework Policy” in the Philippines. Although the descriptive research design used by the authors allows to answer the research question, the study could benefit from an experimental design. This way, the authors would have firm control over variables. Additionally, the study’s sample size was not large enough for the findings to be generalized to a larger population.

The study results are presented clearly, logically, and comprehensively and correspond to the research objectives. The researchers found that students’ mean grades decreased in the group without homework and increased in the group with homework. Based on these findings, the authors concluded that homework positively affected students’ performance. This conclusion is logical and grounded in data.

This research effectively showed the importance of homework for students’ performance. Yet, since the sample size was relatively small, larger studies are needed to ensure the authors’ conclusions can be generalized to a larger population.

🔎 More Research Analysis Paper Examples

Do you want another research analysis example? Check out the best analysis research paper samples below:

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  • Journal Article Review: Correlates of Physical Violence at School
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  • Codependence, Narcissism, and Childhood Trauma: Analysis of the Article
  • Relationship Between Work Intensity, Workaholism, Burnout, and MSC: Article Review

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How technology is reinventing education

Stanford Graduate School of Education Dean Dan Schwartz and other education scholars weigh in on what's next for some of the technology trends taking center stage in the classroom.

discussion and analysis in research paper example

Image credit: Claire Scully

New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Effect of exercise for depression: systematic review and network meta-analysis of randomised controlled trials

Linked editorial.

Exercise for the treatment of depression

  • Related content
  • Peer review
  • Michael Noetel , senior lecturer 1 ,
  • Taren Sanders , senior research fellow 2 ,
  • Daniel Gallardo-Gómez , doctoral student 3 ,
  • Paul Taylor , deputy head of school 4 ,
  • Borja del Pozo Cruz , associate professor 5 6 ,
  • Daniel van den Hoek , senior lecturer 7 ,
  • Jordan J Smith , senior lecturer 8 ,
  • John Mahoney , senior lecturer 9 ,
  • Jemima Spathis , senior lecturer 9 ,
  • Mark Moresi , lecturer 4 ,
  • Rebecca Pagano , senior lecturer 10 ,
  • Lisa Pagano , postdoctoral fellow 11 ,
  • Roberta Vasconcellos , doctoral student 2 ,
  • Hugh Arnott , masters student 2 ,
  • Benjamin Varley , doctoral student 12 ,
  • Philip Parker , pro vice chancellor research 13 ,
  • Stuart Biddle , professor 14 15 ,
  • Chris Lonsdale , deputy provost 13
  • 1 School of Psychology, University of Queensland, St Lucia, QLD 4072, Australia
  • 2 Institute for Positive Psychology and Education, Australian Catholic University, North Sydney, NSW, Australia
  • 3 Department of Physical Education and Sport, University of Seville, Seville, Spain
  • 4 School of Health and Behavioural Sciences, Australian Catholic University, Strathfield, NSW, Australia
  • 5 Department of Clinical Biomechanics and Sports Science, University of Southern Denmark, Odense, Denmark
  • 6 Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, University of Cádiz, Spain
  • 7 School of Health and Behavioural Sciences, University of the Sunshine Coast, Petrie, QLD, Australia
  • 8 School of Education, University of Newcastle, Callaghan, NSW, Australia
  • 9 School of Health and Behavioural Sciences, Australian Catholic University, Banyo, QLD, Australia
  • 10 School of Education, Australian Catholic University, Strathfield, NSW, Australia
  • 11 Australian Institute of Health Innovation, Macquarie University, Macquarie Park, NSW, Australia
  • 12 Children’s Hospital Westmead Clinical School, University of Sydney, Westmead, NSW, Australia
  • 13 Australian Catholic University, North Sydney, NSW, Australia
  • 14 Centre for Health Research, University of Southern Queensland, Springfield, QLD, Australia
  • 15 Faculty of Sport and Health Science, University of Jyvaskyla, Jyvaskyla, Finland
  • Correspondence to: M Noetel m.noetel{at}uq.edu.au (or @mnoetel on Twitter)
  • Accepted 15 January 2024

Objective To identify the optimal dose and modality of exercise for treating major depressive disorder, compared with psychotherapy, antidepressants, and control conditions.

Design Systematic review and network meta-analysis.

Methods Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Bayesian arm based, multilevel network meta-analyses were performed for the primary analyses. Quality of the evidence for each arm was graded using the confidence in network meta-analysis (CINeMA) online tool.

Data sources Cochrane Library, Medline, Embase, SPORTDiscus, and PsycINFO databases.

Eligibility criteria for selecting studies Any randomised trial with exercise arms for participants meeting clinical cut-offs for major depression.

Results 218 unique studies with a total of 495 arms and 14 170 participants were included. Compared with active controls (eg, usual care, placebo tablet), moderate reductions in depression were found for walking or jogging (n=1210, κ=51, Hedges’ g −0.62, 95% credible interval −0.80 to −0.45), yoga (n=1047, κ=33, g −0.55, −0.73 to −0.36), strength training (n=643, κ=22, g −0.49, −0.69 to −0.29), mixed aerobic exercises (n=1286, κ=51, g −0.43, −0.61 to −0.24), and tai chi or qigong (n=343, κ=12, g −0.42, −0.65 to −0.21). The effects of exercise were proportional to the intensity prescribed. Strength training and yoga appeared to be the most acceptable modalities. Results appeared robust to publication bias, but only one study met the Cochrane criteria for low risk of bias. As a result, confidence in accordance with CINeMA was low for walking or jogging and very low for other treatments.

Conclusions Exercise is an effective treatment for depression, with walking or jogging, yoga, and strength training more effective than other exercises, particularly when intense. Yoga and strength training were well tolerated compared with other treatments. Exercise appeared equally effective for people with and without comorbidities and with different baseline levels of depression. To mitigate expectancy effects, future studies could aim to blind participants and staff. These forms of exercise could be considered alongside psychotherapy and antidepressants as core treatments for depression.

Systematic review registration PROSPERO CRD42018118040.

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Introduction

Major depressive disorder is a leading cause of disability worldwide 1 and has been found to lower life satisfaction more than debt, divorce, and diabetes 2 and to exacerbate comorbidities, including heart disease, 3 anxiety, 4 and cancer. 5 Although people with major depressive disorder often respond well to drug treatments and psychotherapy, 6 7 many are resistant to treatment. 8 In addition, access to treatment for many people with depression is limited, with only 51% treatment coverage for high income countries and 20% for low and lower-middle income countries. 9 More evidence based treatments are therefore needed.

Exercise may be an effective complement or alternative to drugs and psychotherapy. 10 11 12 13 14 In addition to mental health benefits, exercise also improves a range of physical and cognitive outcomes. 15 16 17 Clinical practice guidelines in the US, UK, and Australia recommend physical activity as part of treatment for depression. 18 19 20 21 But these guidelines do not provide clear, consistent recommendations about dose or exercise modality. British guidelines recommend group exercise programmes 20 21 and offer general recommendations to increase any form of physical activity, 21 the American Psychiatric Association recommends any dose of aerobic exercise or resistance training, 20 and Australian and New Zealand guidelines suggest a combination of strength and vigorous aerobic exercises, with at least two or three bouts weekly. 19

Authors of guidelines may find it hard to provide consistent recommendations on the basis of existing mainly pairwise meta-analyses—that is, assessing a specific modality versus a specific comparator in a distinct group of participants. 12 13 22 These meta-analyses have come under scrutiny for pooling heterogeneous treatments and heterogenous comparisons leading to ambiguous effect estimates. 23 Reviews also face the opposite problem, excluding exercise treatments such as yoga, tai chi, and qigong because grouping them with strength training might be inappropriate. 23 Overviews of reviews have tried to deal with this problem by combining pairwise meta-analyses on individual treatments. A recent such overview found no differences between exercise modalities. 13 Comparing effect sizes between different pairwise meta-analyses can also lead to confusion because of differences in analytical methods used between meta-analysis, such as choice of a control to use as the referent. Network meta-analyses are a better way to precisely quantify differences between interventions as they simultaneously model the direct and indirect comparisons between interventions. 24

Network meta-analyses have been used to compare different types of psychotherapy and pharmacotherapy for depression. 6 25 26 For exercise, they have shown that dose and modality influence outcomes for cognition, 16 back pain, 15 and blood pressure. 17 Two network meta-analyses explored the effects of exercise on depression: one among older adults 27 and the other for mental health conditions. 28 Because of the inclusion criteria and search strategies used, these reviews might have been under-powered to explore moderators such as dose and modality (κ=15 and κ=71, respectively). To resolve conflicting findings in existing reviews, we comprehensively searched randomised trials on exercise for depression to ensure our review was adequately powered to identify the optimal dose and modality of exercise. For example, a large overview of reviews found effects on depression to be proportional to intensity, with vigorous exercise appearing to be better, 13 but a later meta-analysis found no such effects. 22 We explored whether recommendations differ based on participants’ sex, age, and baseline level of depression.

Given the challenges presented by behaviour change in people with depression, 29 we also identified autonomy support or behaviour change techniques that might improve the effects of intervention. 30 Behaviour change techniques such as self-monitoring and action planning have been shown to influence the effects of physical activity interventions in adults (>18 years) 31 and older adults (>60 years) 32 with differing effectiveness of techniques in different populations. We therefore tested whether any intervention components from the behaviour change technique taxonomy were associated with higher or lower intervention effects. 30 Other meta-analyses found that physical activity interventions work better when they provide people with autonomy (eg, choices, invitational language). 33 Autonomy is not well captured in the taxonomy for behaviour change technique. We therefore tested whether effects were stronger in studies that provided more autonomy support to patients. Finally, to understand the mechanism of intervention effects, such as self-confidence, affect, and physical fitness, we collated all studies that conducted formal mediation analyses.

Our findings are presented according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Network Meta-analyses (PRISMA-NMA) guidelines (see supplementary file, section S0; all supplementary files, data, and code are also available at https://osf.io/nzw6u/ ). 34 We amended our analysis strategy after registering our review; these changes were to better align with new norms established by the Cochrane Comparing Multiple Interventions Methods Group. 35 These norms were introduced between the publication of our protocol and the preparation of this manuscript. The largest change was using the confidence in network meta-analysis (CINeMA) 35 online tool instead of the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) guidelines and adopting methods to facilitate assessments—for example, instead of using an omnibus test for all treatments, we assessed publication bias for each treatment compared with active controls. We also modelled acceptability (through dropout rate), which was not predefined but was adopted in response to a reviewer’s comment.

Eligibility criteria

To be eligible for inclusion, studies had to be randomised controlled trials that included exercise as a treatment for depression and included participants who met the criteria for major depressive disorder, either clinician diagnosed or identified through participant self-report as exceeding established clinical thresholds (eg, scored >13 on the Beck depression inventory-II). 36 Studies could meet these criteria when all the participants had depression or when the study reported depression outcomes for a subgroup of participants with depression at the start of the study.

We defined exercise as “planned, structured and repetitive bodily movement done to improve or maintain one or more components of physical fitness.” 37 Unlike recent reviews, 12 22 we included studies with more than one exercise arm and multifaceted interventions (eg, health and exercise counselling) as long as they contained a substantial exercise component. These trials could be included because network meta-analysis methods allows for the grouping of those interventions into homogenous nodes. Unlike the most recent Cochrane review, 12 we also included participants with physical comorbidities such as arthritis and participants with postpartum depression because the Diagnostic Statistical Manual of Mental Health Disorders , fifth edition, removed the postpartum onset specifier after that analysis was completed. 23 Studies were excluded if interventions were shorter than one week, depression was not reported as an outcome, and data were insufficient to calculate an effect size for each arm. Any comparison condition was included, allowing us to quantify the effects against established treatments (eg, selective serotonin reuptake inhibitors (SSRIs), cognitive behavioural therapy), active control conditions (usual care, placebo tablet, stretching, educational control, and social support), or waitlist control conditions. Published and unpublished studies were included, with no restrictions on language applied.

Information sources

We adapted the search strategy from the most recent Cochrane review, 12 adding keywords for yoga, tai chi, and qigong, as they met our definition for exercise. We conducted database searches, without filters or date limits, in The Cochrane Library via CENTRAL, SPORTDiscus via Embase, and Medline, Embase, and PsycINFO via Ovid. Searches of the databases were conducted on 17 December 2018 and 7 August 2020 and last updated on 3 June 2023 (see supplementary file section S1 for full search strategies). We assessed full texts of all included studies from two systematic reviews of exercise for depression. 12 22

Study selection and data collection

To select studies, we removed duplicate records in Covidence 38 and then screened each title and abstract independently and in duplicate. Conflicts were resolved through discussion or consultation with a third reviewer. The same methods were used for full text screening.

We used the Extraction 1.0 randomised controlled trial data extraction forms in Covidence. 38 Data were extracted independently and in duplicate, with conflicts resolved through discussion with a third reviewer.

For each study, we extracted a description of the interventions, including frequency, intensity, and type and time of each exercise intervention. Using the Compendium of Physical Activities, 39 we calculated the energy expenditure dose of exercise for each arm as metabolic equivalents of task (METs) min/week. Two authors evaluated each exercise intervention using the Behaviour Change Taxonomy version 1 30 for behaviour change techniques explicitly described in each exercise arm. They also rated the level of autonomy offered to participants, on a scale from 1 (no choice) to 10 (full autonomy). We also extracted descriptions of the other arms within the randomised trials, including other treatment or control conditions; participants’ age, sex, comorbidities, and baseline severity of depressive symptoms; and each trial’s location and whether or not the trial was funded.

Risk of bias in individual studies

We used Cochrane’s risk of bias tool for randomised controlled trials. 40 Risk of bias was rated independently and in duplicate, with conflicts resolved through discussion with a third reviewer.

Summary measures and synthesis

For main and moderation analyses, we used bayesian arm based multilevel network meta-analysis models. 41 All network meta-analytical approaches allow users to assess the effects of treatments against a range of comparisons. The bayesian arm based models allowed us to also assess the influence of hypothesised moderators, such as intensity, dose, age, and sex. Many network meta-analyses use contrast based methods, comparing post-test scores between study arms. 41 Arm based meta-analyses instead describe the population-averaged absolute effect size for each treatment arm (ie, each arm’s change score). 41 As a result, the summary measure we used was the standardised mean change from baseline, calculated as standardised mean differences with correction for small studies (Hedges’ g). In keeping with the norms from the included studies, effect sizes describe treatment effects on depression, such that larger negative numbers represent stronger effects on symptoms. Using National Institute for Health and Care Excellence guidelines, 42 we standardised change scores for different depression scales (eg, Beck depression inventory, Hamilton depression rating scale) using an internal reference standard for each scale (for each scale, the average of pooled standard deviations at baseline) reported in our meta-analysis. Because depression scores generally show regression to the mean, even in control conditions, we present effect sizes as improvements beyond active control conditions. This convention makes our results comparable to existing, contrast based meta-analyses.

Active control conditions (usual care, placebo tablet, stretching, educational control, and social support) were grouped to increase power for moderation analyses, for parsimony in the network graph, and because they all showed similar arm based pooled effect sizes (Hedges’ g between −0.93 and −1.00 for all, with no statistically significant differences). We separated waitlist control from these active control conditions because it typically shows poorer effects in treatment for depression. 43

Bayesian meta-analyses were conducted in R 44 using the brms package. 45 We preregistered informative priors based on the distributional parameters of our meta-analytical model. 46 We nested effects within arms to manage dependency between multiple effect sizes from the same participants. 46 For example, if one study reported two self-reported measures of depression, or reported both self-report and clinician rated depression, we nested these effect sizes within the arm to account for both pieces of information while controlling for dependency between effects. 46 Finally, we compared absolute effect sizes against a standardised minimum clinically important difference, 0.5 standard deviations of the change score. 47 From our data, this corresponded to a large change in before and after scores (Hedges’ g −1.16), a moderate change compared with waitlist control (g −0.55), or a small benefit when compared with active controls (g −0.20). For credibility assessments comparing exercise modalities, we used the netmeta package 48 and CINeMA. 49 We also used netmeta to model acceptability, comparing the odds ratio for drop-out rate in each arm.

Additional analyses

All prespecified moderation and sensitivity analyses were performed. We moderated for participant characteristics, including participants’ sex, age, baseline symptom severity, and presence or absence of comorbidities; duration of the intervention (weeks); weekly dose of the intervention; duration between completion of treatment and measurement, to test robustness to remission (in response to a reviewer’s suggestion); amount of autonomy provided in the exercise prescription; and presence of each behaviour change technique. As preregistered, we moderated for behaviour change techniques in three ways: through meta-regression, including all behaviour change techniques simultaneously for primary analysis; including one behaviour change technique at a time (using 99% credible intervals to somewhat control for multiple comparisons) in exploratory analyses; and through meta-analytical classification and regression trees (metaCART), which allowed for interactions between moderating variables (eg, if goal setting combined with feedback had synergistic effects). 50 We conducted sensitivity analyses for risk of bias, assessing whether studies with low versus unclear or high risk of bias on each domain showed statistically significant differences in effect sizes.

Credibility assessment

To assess the credibility of each comparison against active control, we used CINeMA. 35 49 This online tool was designed by the Cochrane Comparing Multiple Interventions Methods Group as an adaptation of GRADE for network meta-analyses. 35 In line with recommended guidelines, for each comparison we made judgements for within study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. Similar to GRADE, we considered the evidence for comparisons to show high confidence then downgraded on the basis of concerns in each domain, as follows:

Within study bias —Comparisons were downgraded when most of the studies providing direct evidence for comparisons were unclear or high risk.

Reporting bias —Publication bias was assessed in three ways. For each comparison with at least 10 studies 51 we created funnel plots, including estimates of effect sizes after removing studies with statistically significant findings (ie, worst case estimates) 52 ; calculated an s value, representing how strong publication bias would need to be to nullify meta-analytical effects 52 ; and conducted a multilevel Egger’s regression test, indicative of small study bias. Given these tests are not recommended for comparisons with fewer than 10 studies, 51 those comparisons were considered to show “some concerns.”

Indirectness — Our primary population of interest was adults with major depression. Studies were considered to be indirect if they focused on one sex only (>90% male or female), participants with comorbidities (eg, heart disease), adolescents and young adults (14-20 years), or older adults (>60 years). We flagged these studies as showing some concerns if one of these factors was present, and as “major concerns” if two of these factors were present. Evidence from comparisons was classified as some concerns or major concerns using majority rating for studies directly informing the comparison.

Imprecision — As per CINeMA, we used the clinically important difference of Hedges’ g=0.2 to ascribe a zone of equivalence, where differences were not considered clinically significant (−0.2<g<0.2). Studies were flagged as some concerns for imprecision if the bounds of the 95% credible interval extended across that zone, and they were flagged as major concerns if the bounds extended to the other side of the zone of equivalence (such that effects could be harmful).

Heterogeneity — Prediction intervals account for heterogeneity differently from credible intervals. 35 As a result, CINeMA accounts for heterogeneity by assessing whether the prediction intervals and the credible intervals lead to different conclusions about clinical significance (using the same zone of equivalence from imprecision). Comparisons are flagged as some concerns if the prediction interval crosses into, or out of, the zone of equivalence once (eg, from helpful to no meaningful effect), and as major concerns if the prediction interval crosses the zone twice (eg, from helpful and harmful).

Incoherence — Incoherence assesses whether the network meta-analysis provides similar estimates when using direct evidence (eg, randomised controlled trials on strength training versus SSRI) compared with indirect evidence (eg, randomised controlled trials where either strength training or SSRI uses waitlist control). Incoherence provides some evidence the network may violate the assumption of transitivity: that the only systematic difference between arms is the treatment, not other confounders. We assessed incoherence using two methods: Firstly, a global design-by-treatment interaction to assess for incoherence across the whole network, 35 49 and, secondly, separating indirect and direct evidence (SIDE method) for each comparison through netsplitting to see whether differences between those effect estimates were statistically significant. We flagged comparisons as some concerns if either no direct comparisons were available or direct and indirect evidence gave different conclusions about clinical significance (eg, from helpful to no meaningful effect, as per imprecision and heterogeneity). Again, we classified comparisons as major concerns if the direct and indirect evidence changed the sign of the effect or changed both limits of the credible interval. 35 49

Patient and public involvement

We discussed the aims and design of this study with members of the public, including those who had experienced depression. Several of our authors have experienced major depressive episodes, but beyond that we did not include patients in the conduct of this review.

Study selection

The PRISMA flow diagram outlines the study selection process ( fig 1 ). We used two previous reviews to identify potentially eligible studies for inclusion. 12 22 Database searches identified 18 658 possible studies. After 5505 duplicates had been removed, two reviewers independently screened 13 115 titles and abstracts. After screening, two reviewers independently reviewed 1738 full text articles. Supplementary file section S2 shows the consensus reasons for exclusion. A total of 218 unique studies described in 246 reports were included, totalling 495 arms and 14 170 participants. Supplementary file section S3 lists the references and characteristics of the included studies.

Fig 1

Flow of studies through review

Network geometry

As preregistered, we removed nodes with fewer than 100 participants. Using this filter, most interventions contained comparisons with at least four other nodes in the network geometry ( fig 2 ). The results of the global test design-by-treatment interaction model were not statistically significant, supporting the assumption of transitivity (χ 2 =94.92, df=75, P=0.06). When net-splitting was used on all possible combinations in the network, for two out of the 120 comparisons we found statistically significant incoherence between direct and indirect evidence (SSRI v waitlist control; cognitive behavioural therapy v tai chi or qigong). Overall, we found little statistical evidence that the model violated the assumption of transitivity. Qualitative differences were, however, found for participant characteristics between different arms (see supplementary file, section S4). For example, some interventions appeared to be prescribed more frequently among people with severe depression (eg, 7/16 studies using SSRIs) compared with other interventions (eg, 1/15 studies using aerobic exercise combined with therapy). Similarly, some interventions appeared more likely to be prescribed for older adults (eg, mean age, tai chi=59 v dance=31) or women (eg, per cent female: dance=88% v cycling=53%). Given that plausible mechanisms exist for these systematic differences (eg, the popularity of tai chi among older adults), 53 there are reasons to believe that allocation to treatment arms would be less than perfectly random. We have factored these biases in our certainty estimates through indirectness ratings.

Fig 2

Network geometry indicating number of participants in each arm (size of points) and number of comparisons between arms (thickness of lines). SSRI=selective serotonin reuptake inhibitor

Risk of bias within studies

Supplementary file section S5 provides the risk of bias ratings for each study. Few studies explicitly blinded participants and staff ( fig 3 ). As a result, overall risk of bias for most studies was unclear or high, and effect sizes could include expectancy effects, among other biases. However, sensitivity analyses suggested that effect sizes were not influenced by any risk of bias criteria owing to wide credible intervals (see supplementary file, section S6). Nevertheless, certainty ratings for all treatments arms were downgraded owing to high risk of bias in the studies informing the comparison.

Fig 3

Risk of bias summary plot showing percentage of included studies judged to be low, unclear, or high risk across Cochrane criteria for randomised trials

Synthesis of results

Supplementary file section S7 presents a forest plot of Hedges’ g values for each study. Figure 4 shows the predicted effects of each treatment compared with active controls. Compared with active controls, large reductions in depression were found for dance (n=107, κ=5, Hedges’ g −0.96, 95% credible interval −1.36 to −0.56) and moderate reductions for walking or jogging (n=1210, κ=51, g −0.63, −0.80 to −0.46), yoga (n=1047, κ=33, g=−0.55, −0.73 to −0.36), strength training (n=643, κ=22, g=−0.49, −0.69 to −0.29), mixed aerobic exercises (n=1286, κ=51, g=−0.43, −0.61 to −0.25), and tai chi or qigong (n=343, κ=12, g=−0.42, −0.65 to −0.21). Moderate, clinically meaningful effects were also present when exercise was combined with SSRIs (n=268, κ=11, g=−0.55, −0.86 to −0.23) or aerobic exercise was combined with psychotherapy (n=404, κ=15, g=−0.54, −0.76 to −0.32). All these treatments were significantly stronger than the standardised minimum clinically important difference compared with active control (g=−0.20), equating to an absolute g value of −1.16. Dance, exercise combined with SSRIs, and walking or jogging were the treatments most likely to perform best when modelling the surface under the cumulative ranking curve ( fig 4 ). For acceptability, the odds of participants dropping out of the study were lower for strength training (n=247, direct evidence κ=6, odds ratio 0.55, 95% credible interval 0.31 to 0.99) and yoga (n=264, κ=5, 0.57, 0.35 to 0.94) than for active control. The rate of dropouts was not significantly different from active control in any other arms (see supplementary file, section S8).

Fig 4

Predicted effects of different exercise modalities on major depression compared with active controls (eg, usual care), with 95% credible intervals. The estimate of effects for the active control condition was a before and after change of Hedges’ g of −0.95 (95% credible interval −1.10 to −0.79), n=3554, κ =113. Colour represents SUCRA from most likely to be helpful (dark purple) to least likely to be helpful (light purple). SSRI=selective serotonin reuptake inhibitor; SUCRA=surface under the cumulative ranking curve

Consistent with other meta-analyses, effects were moderate for cognitive behaviour therapy alone (n=712, κ=20, g=−0.55, −0.75 to −0.37) and small for SSRIs (n=432, κ=16, g=−0.26, −0.50 to −0.01) compared with active controls ( fig 4 ). These estimates are comparable to those of reviews that focused directly on psychotherapy (g=−0.67, −0.79 to −0.56) 7 or pharmacotherapy (g=−0.30, –0.34 to −0.26). 25 However, our review was not designed to find all studies of these treatments, so these estimates should not usurp these directly focused systematic reviews.

Despite the large number of studies in the network, confidence in the effects were low ( fig 5 ). This was largely due to the high within study bias described in the risk of bias summary plot. Reporting bias was also difficult to robustly assess because direct comparison with active control was often only provided in fewer than 10 studies. Many studies focused on one sex only, older adults, or those with comorbidities, so most arms had some concerns about indirect comparisons. Credible intervals were seldom wide enough to change decision making, so concerns about imprecision were few. Heterogeneity did plausibly change some conclusions around clinical significance. Few studies showed problematic incoherence, meaning direct and indirect evidence usually agreed. Overall, walking or jogging had low confidence, with other modalities being very low.

Fig 5

Summary table for credibility assessment using confidence in network meta-analysis (CINeMA). SSRI=selective serotonin reuptake inhibitor

Moderation by participant characteristics

The optimal modality appeared to be moderated by age and sex. Compared with models that only included exercise modality (R 2 =0.65), R 2 was higher for models that included interactions with sex (R 2 =0.71) and age (R 2 =0.69). R 2 showed no substantial increase for models including baseline depression (R 2 =0.67) or comorbidities (R 2 =0.66; see supplementary file, section S9).

Effects appeared larger for women than men for strength training and cycling ( fig 6 ). Effects appeared to be larger for men than women when prescribing yoga, tai chi, and aerobic exercise alongside psychotherapy. Yoga and aerobic exercise alongside psychotherapy appeared more effective for older participants than younger people ( fig 7 ). Strength training appeared more effective when prescribed to younger participants than older participants. Some estimates were associated with substantial uncertainty because some modalities were not well studied in some groups (eg, tai chi for younger adults), and mean age of the sample was only available for 71% of the studies.

Fig 6

Effects of interventions versus active control on depression (lower is better) by sex. Shading represents 95% credible intervals

Fig 7

Effects of interventions versus active control on depression (lower is better) by age. Shading represents 95% credible intervals

Moderation by intervention and design characteristics

Across modalities, a clear dose-response curve was observed for intensity of exercise prescribed ( fig 8 ). Although light physical activity (eg, walking, hatha yoga) still provided clinically meaningful effects (g=−0.58, −0.82 to −0.33), expected effects were stronger for vigorous exercise (eg, running, interval training; g=−0.74, −1.10 to −0.38). This finding did not appear to be due to increased weekly energy expenditure: credible intervals were wide, which meant that the dose-response curve for METs/min prescribed per week was unclear (see supplementary file, section S10). Weak evidence suggested that shorter interventions (eg, 10 weeks: g=−0.53, −0.71 to −0.35) worked somewhat better than longer ones (eg, 30 weeks: g=−0.37, −0.79 to 0.03), with wide credible intervals again indicating high uncertainty (see supplementary file, section S11). We also moderated for the lag between the end of treatment and the measurement of the outcome. We found no indication that participants were likely to relapse within the measurement period (see supplementary file, section S12); effects remained steady when measured either directly after the intervention (g=−0.59, −0.80 to −0.39) or up to six months later (g=−0.63, −0.87 to −0.40).

Fig 8

Dose-response curve for intensity (METs) across exercise modalities compared with active control. METs=metabolic equivalents of task

Supplementary file section S13 provides coding for the behaviour change techniques and autonomy for each exercise arm. None of the behaviour change techniques significantly moderated overall effects. Contrary to expectations, studies describing a level of participant autonomy (ie, choice over frequency, intensity, type, or time) tended to show weaker effects (g=−0.28, −0.78 to 0.23) than those that did not (g=−0.75, −1.17 to −0.33; see supplementary file, section S14). This effect was consistent whether or not we included studies that used physical activity counselling (usually high autonomy).

Use of group exercise appeared to moderate the effects: although the overall effects were similar for individual (g=−1.10, −1.57 to −0.64) and group exercise (g=−1.16, −1.61 to −0.73), some interventions were better delivered in groups (yoga) and some were better delivered individually (strength training, mixed aerobic exercise; see supplementary file, section S15).

As preregistered, we tested whether study funding moderated effects. Models that included whether a study was funded did explain more variance (R 2 =0.70) compared with models that included treatment alone (R 2 =0.65). Funded studies showed stronger effects (g=−1.01, −1.19 to −0.82) than unfunded studies (g=−0.77, −1.09 to −0.46). We also moderated for the type of measure (self-report v clinician report). This did not explain a substantial amount of variance in the outcome (R 2 =0.66).

Sensitivity analyses

Evidence of publication bias was found for overall estimates of exercise on depression compared with active controls, although not enough to nullify effects. The multilevel Egger’s test showed significance (F 1,98 =23.93, P<0.001). Funnel plots showed asymmetry, but the result of pooled effects remained statistically significant when only including non-significant studies (see supplementary file, section S16). No amount of publication bias would be sufficient to shrink effects to zero (s value=not possible). To reduce effects below clinical significance thresholds, studies with statistically significant results would need to be reported 58 times more frequently than studies with non-significant results.

Qualitative synthesis of mediation effects

Only a few of the studies used explicit mediation analyses to test hypothesised mechanisms of action. 54 55 56 57 58 59 One study found that both aerobic exercise and yoga led to decreased depression because participants ruminated less. 54 The study found that the effects of aerobic exercise (but not yoga) were mediated by increased acceptance. 54 “Perceived hassles” and awareness were not statistically significant mediators. 54 Another study found that the effects of yoga were mediated by increased self-compassion, but not rumination, self-criticism, tolerance of uncertainty, body awareness, body trust, mindfulness, and attentional biases. 55 One study found that the effects from an aerobic exercise intervention were not mediated by long term physical activity, but instead were mediated by exercise specific affect regulation (eg, self-control for exercise). 57 Another study found that neither exercise self-efficacy nor depression coping self-efficacy mediated effects of aerobic exercise. 56 Effects of aerobic exercise were not mediated by the N2 amplitude from electroencephalography, hypothesised as a neuro-correlate of cognitive control deficits. 58 Increased physical activity did not appear to mediate the effects of physical activity counselling on depression. 59 It is difficult to infer strong conclusions about mechanisms on the basis of this small number of studies with low power.

Summary of evidence

In this systematic review and meta-analysis of randomised controlled trials, exercise showed moderate effects on depression compared with active controls, either alone or in combination with other established treatments such as cognitive behaviour therapy. In isolation, the most effective exercise modalities were walking or jogging, yoga, strength training, and dancing. Although walking or jogging were effective for both men and women, strength training was more effective for women, and yoga or qigong was more effective for men. Yoga was somewhat more effective among older adults, and strength training was more effective among younger people. The benefits from exercise tended to be proportional to the intensity prescribed, with vigorous activity being better. Benefits were equally effective for different weekly doses, for people with different comorbidities, or for different baseline levels of depression. Although confidence in many of the results was low, treatment guidelines may be overly conservative by conditionally recommending exercise as complementary or alternative treatment for patients in whom psychotherapy or pharmacotherapy is either ineffective or unacceptable. 60 Instead, guidelines for depression ought to include prescriptions for exercise and consider adapting the modality to participants’ characteristics and recommending more vigorous intensity exercises.

Our review did not uncover clear causal mechanisms, but the trends in the data are useful for generating hypotheses. It is unlikely that any single causal mechanism explains all the findings in the review. Instead, we hypothesise that a combination of social interaction, 61 mindfulness or experiential acceptance, 62 increased self-efficacy, 33 immersion in green spaces, 63 neurobiological mechanisms, 64 and acute positive affect 65 combine to generate outcomes. Meta-analyses have found each of these factors to be associated with decreases in depressive symptoms, but no single treatment covers all mechanisms. Some may more directly promote mindfulness (eg, yoga), be more social (eg, group exercise), be conducted in green spaces (eg, walking), provide a more positive affect (eg, “runner’s high”’), or be more conducive to acute adaptations that may increase self-efficacy (eg, strength). 66 Exercise modalities such as running may satisfy many of the mechanisms, but they are unlikely to directly promote the mindful self-awareness provided by yoga and qigong. Both these forms of exercise are often practised in groups with explicit mindfulness but seldom have fast and objective feedback loops that improve self-efficacy. Adequately powered studies testing multiple mediators may help to focus more on understanding why exercise helps depression and less on whether exercise helps. We argue that understanding these mechanisms of action is important for personalising prescriptions and better understanding effective treatments.

Our review included more studies than many existing reviews on exercise for depression. 13 22 27 28 As a result, we were able to combine the strengths of various approaches to exercise and to make more nuanced and precise conclusions. For example, even taking conservative estimates (ie, the least favourable end of the credible interval), practitioners can expect patients to experience clinically significant effects from walking, running, yoga, qigong, strength training, and mixed aerobic exercise. Because we simultaneously assessed more than 200 studies, credible intervals were narrower than those in most existing meta-analyses. 13 We were also able to explore non-linear relationships between outcomes and moderators, such as frequency, intensity, and time. These analyses supported some existing findings—for example, our study and the study by Heissel et al 22 found that shorter interventions had stronger effects, at least for six months; our study and the study by Singh et al 13 both found that effects were stronger with vigorous intensity exercise compared with light and moderate exercise. However, most existing reviews found various treatment modalities to be equally effective. 13 27 In our review, some types of exercise had stronger effect sizes than others. We attribute this to the study level data available in a network meta-analysis compared with an overview of reviews 24 and higher power compared with meta-analyses with smaller numbers of included studies. 22 28 Overviews of reviews have the ability to more easily cover a wider range of participants, interventions, and outcomes, but also risk double counting randomised trials that are included in separate meta-analyses. They often include heterogeneous studies without having as much control over moderation analyses (eg, Singh et al included studies covering both prevention and treatment 13 ). Some of those reviews grouped interventions such as yoga with heterogeneous interventions such as stretching and qigong. 13 This practise of combining different interventions makes it harder to interpret meta-analytical estimates. We used methods that enabled us to separately analyse the effects of these treatment modalities. In so doing, we found that these interventions do have different effects, with yoga being an intervention with strong effects and stretching being better described as an active control condition. Network meta-analyses revealed the same phenomenon with psychotherapy: researchers once concluded there was a dodo bird verdict, whereby “everybody has won, and all must have prizes,” 67 until network meta-analyses showed some interventions were robustly more effective than others. 6 26

Predictors of acceptability and outcomes

We found evidence to suggest good acceptability of yoga and strength training; although the measurement of study drop-out is an imperfect proxy of adherence. Participants may complete the study without doing any exercise or may continue exercising and drop out of the study for other reasons. Nevertheless, these are useful data when considering adherence.

Behaviour change techniques, which are designed to increase adherence, did not meaningfully moderate the effect sizes from exercise. This may be due to several factors. It may be that the modality explains most of the variance between effects, such that behaviour change techniques (eg, presence or absence of feedback) did not provide a meaningful contribution. Many forms of exercise potentially contain therapeutic benefits beyond just energy expenditure. These characteristics of a modality may be more influential than coexisting behaviour change techniques. Alternatively, researchers may have used behaviour change techniques such as feedback or goal setting without explicitly reporting them in the study methods. Given the inherent challenges of behaviour change among people with depression, 29 and the difficulty in forecasting which strategies are likely to be effective, 68 we see the identification of effective techniques as important.

We did find that autonomy, as provided in the methods of included studies, predicted effects, but in the opposite direction to our hypotheses: more autonomy was associated with weaker effects. Physical activity counselling, which usually provides a great deal of patient autonomy, was among the lowest effect sizes in our meta-analysis. Higher autonomy judgements were associated with weaker outcomes regardless of whether physical activity counselling was included in the model. One explanation for these data is that people with depression benefit from the clear direction and accountability of a standardised prescription. When provided with more freedom, the low self-efficacy that is symptomatic of depression may stop patients from setting an appropriate level of challenge (eg, they may be less likely to choose vigorous exercise). Alternatively, participants were likely autonomous when self-selecting into trials with exercise modalities they enjoyed, or those that fit their social circumstances. After choosing something value aligned, autonomy within the trial may not have helpful. Either way, data should be interpreted with caution. Our judgement of the autonomy provided in the methods may not reflect how much autonomy support patients actually felt. The patient’s perceived autonomy is likely determined by a range of factors not described in the methods (eg, the social environment created by those delivering the programme, or their social identity), so other studies that rely on patient reports of the motivational climate are likely to be more reliable. 33 Our findings reiterate the importance of considering these patient reports in future research of exercise for depression.

Our findings suggest that practitioners could advocate for most patients to engage in exercise. Those patients may benefit from guidance on intensity (ie, vigorous) and types of exercise that appear to work well (eg, walking, running, mixed aerobic exercise, strength training, yoga, tai chi, qigong) and be well tolerated (eg, strength training and yoga). If social determinants permit, 66 engaging in group exercise or structured programmes could provide support and guidance to achieve better outcomes. Health services may consider offering these programmes as an alternative or adjuvant treatment for major depression. Specifically, although the confidence in the evidence for exercise is less strong than for cognitive behavioural therapy, the effect sizes seem comparable, so it may be an alternative for patients who prefer not to engage in psychotherapy. Previous reviews on those with mild-moderate depression have found similar effects for exercise or SSRIs, or the two combined. 13 14 In contrast, we found some forms of exercise to have stronger effects than SSRIs alone. Our findings are likely related to the larger power in our review (n=14 170) compared with previous reviews (eg, n=2551), 14 and our ability to better account for heterogeneity in exercise prescriptions. Exercise may therefore be considered a viable alternative to drug treatment. We also found evidence that exercise increases the effects of SSRIs, so offering exercise may act as an adjuvant for those already taking drugs. We agree with consensus statements that professionals should still account for patients’ values, preferences, and constraints, ensuring there is shared decision making around what best suits the patient. 66 Our review provides data to help inform that decision.

Strengths, limitations, and future directions

Based on our findings, dance appears to be a promising treatment for depression, with large effects found compared with other interventions in our review. But the small number of studies, low number of participants, and biases in the study designs prohibits us from recommending dance more strongly. Given most research for the intervention has been in young women (88% female participants, mean age 31 years), it is also important for future research to assess the generalisability of the effects to different populations, using robust experimental designs.

The studies we found may be subject to a range of experimental biases. In particular, researchers seldom blinded participants or staff delivering the intervention to the study’s hypotheses. Blinding for exercise interventions may be harder than for drugs 23 ; however, future studies could attempt to blind participants and staff to the study’s hypotheses to avoid expectancy effects. 69 Some of our ratings are for studies published before the proliferation of reporting checklists, so the ratings might be too critical. 23 For example, before CONSORT, few authors explicitly described how they generated a random sequence. 23 Therefore, our risk of bias judgements may be too conservative. Similarly, we planned to use the Cochrane risk of bias (RoB) 1 tool 40 so we could use the most recent Cochrane review of exercise and depression 12 to calibrate our raters, and because RoB 2 had not yet been published. 70 Although assessments of bias between the two tools are generally comparable, 71 the RoB 1 tool can be more conservative when assessing open label studies with subjective assessments (eg, unblinded studies with self-reported measures for depression). 71 As a result, future reviews should consider using the latest risk of bias tool, which may lead to different assessments of bias in included studies.

Most of the main findings in this review appear robust to risks from publication bias. Specifically, pooled effect sizes decreased when accounting for risk of publication bias, but no degree of publication bias could nullify effects. We did not exclude grey literature, but our search strategy was not designed to systematically search grey literature or trial registries. Doing so can detect additional eligible studies 72 and reveal the numbers of completed studies that remain unpublished. 73 Future reviews should consider more systematic searches for this kind of literature to better quantify and mitigate risk of publication bias.

Similarly, our review was able to integrate evidence that directly compared exercise with other treatment modalities such as SSRIs or psychotherapy, while also informing estimates using indirect evidence (eg, comparing the relative effects of strength training and SSRIs when tested against a waitlist control). Our review did not, however, include all possible sources of indirect evidence. Network meta-analyses exist that directly focus on psychotherapy 7 and pharmacotherapy, 25 and these combined for treating depression. 6 Those reviews include more than 500 studies comparing psychological or drug interventions with controls. Harmonising the findings of those reviews with ours would provide stronger data on indirect effects.

Our review found some interesting moderators by age and sex, but these were at the study level rather than individual level—that is, rather than being able to determine whether women engaging in a strength intervention benefit more than men, we could only conclude that studies with more women showed larger effects than studies with fewer women. These studies may have been tailored towards women, so effects may be subject to confounding, as both sex and intervention may have changed. The same finding applied to age, where studies on older adults were likely adapted specifically to this age group. These between study differences may explain the heterogeneity in the effects of interventions, and confounding means our moderators for age and sex should be interpreted cautiously. Future reviews should consider individual patient meta-analyses to allow for more detailed assessments of participant level moderators.

Finally, for many modalities, the evidence is derived from small trials (eg, the median number of walking or jogging arms was 17). In addition to reducing risks from bias, primary research may benefit from deconstruction designs or from larger, head-to-head analyses of exercise modalities to better identify what works best for each candidate.

Clinical and policy implications

Our findings support the inclusion of exercise as part of clinical practice guidelines for depression, particularly vigorous intensity exercise. Doing so may help bridge the gap in treatment coverage by increasing the range of first line options for patients and health systems. 9 Globally there has been an attempt to reduce stigma associated with seeking treatment for depression. 74 Exercise may support this effort by providing patients with treatment options that carry less stigma. In low resource or funding constrained settings, group exercise interventions may provide relatively low cost alternatives for patients with depression and for health systems. When possible, ideal treatment may involve individualised care with a multidisciplinary team, where exercise professionals could take responsibility for ensuring the prescription is safe, personalised, challenging, and supported. In addition, those delivering psychotherapy may want to direct some time towards tackling cognitive and behavioural barriers to exercise. Exercise professionals might need to be trained in the management of depression (eg, managing risk) and to be mindful of the scope of their practice while providing support to deal with this major cause of disability.

Conclusions

Depression imposes a considerable global burden. Many exercise modalities appear to be effective treatments, particularly walking or jogging, strength training, and yoga, but confidence in many of the findings was low. We found preliminary data that may help practitioners tailor interventions to individuals (eg, yoga for older men, strength training for younger women). The World Health Organization recommends physical activity for everyone, including those with chronic conditions and disabilities, 75 but not everyone can access treatment easily. Many patients may have physical, psychological, or social barriers to participation. Still, some interventions with few costs, side effects, or pragmatic barriers, such as walking and jogging, are effective across people with different personal characteristics, severity of depression, and comorbidities. Those who are able may want to choose more intense exercise in a structured environment to further decrease depression symptoms. Health systems may want to provide these treatments as alternatives or adjuvants to other established interventions (cognitive behaviour therapy, SSRIs), while also attenuating risks to physical health associated with depression. 3 Therefore, effective exercise modalities could be considered alongside those intervention as core treatments for depression.

What is already known on this topic

Depression is a leading cause of disability, and exercise is often recommended alongside first line treatments such as pharmacotherapy and psychotherapy

Treatment guidelines and previous reviews disagree on how to prescribe exercise to best treat depression

What this study adds

Various exercise modalities are effective (walking, jogging, mixed aerobic exercise, strength training, yoga, tai chi, qigong) and well tolerated (especially strength training and yoga)

Effects appeared proportional to the intensity of exercise prescribed and were stronger for group exercise and interventions with clear prescriptions

Preliminary evidence suggests interactions between types of exercise and patients’ personal characteristics

Ethics statements

Ethical approval.

Not required.

Acknowledgments

We thank Lachlan McKee for his assistance with data extraction. We also thank Juliette Grosvenor and another librarian (anonymous) for their review of our search strategy.

Contributors: MN led the project, drafted the manuscript, and is the guarantor. MN, TS, PT, MM, BdPC, PP, SB, and CL drafted the initial study protocol. MN, TS, PT, BdPC, DvdH, JS, MM, RP, LP, RV, HA, and BV conducted screening, extraction, and risk of bias assessment. MN, JS, and JM coded methods for behaviour change techniques. MN and DGG conducted statistical analyses. PP, SB, and CL provided supervision and mentorship. All authors reviewed and approved the final manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: None received.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Data sharing Data and code for reproducing analyses are available on the Open Science Framework ( https://osf.io/nzw6u/ ).

The lead author (MN) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: We plan to disseminate the findings of this study to lay audiences through mainstream and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

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discussion and analysis in research paper example

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OpenAI teases an amazing new generative video model called Sora

The firm is sharing Sora with a small group of safety testers but the rest of us will have to wait to learn more.

  • Will Douglas Heaven archive page

OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long.

Based on four sample videos that OpenAI shared with MIT Technology Review ahead of today’s announcement, the San Francisco–based firm has pushed the envelope of what’s possible with text-to-video generation (a hot new research direction that we flagged as a trend to watch in 2024 ).

“We think building models that can understand video, and understand all these very complex interactions of our world, is an important step for all future AI systems,” says Tim Brooks, a scientist at OpenAI.

But there’s a disclaimer. OpenAI gave us a preview of Sora (which means sky in Japanese) under conditions of strict secrecy. In an unusual move, the firm would only share information about Sora if we agreed to wait until after news of the model was made public to seek the opinions of outside experts. [Editor’s note: We’ve updated this story with outside comment below.] OpenAI has not yet released a technical report or demonstrated the model actually working. And it says it won’t be releasing Sora anytime soon. [ Update: OpenAI has now shared more technical details on its website.]

The first generative models that could produce video from snippets of text appeared in late 2022. But early examples from Meta , Google, and a startup called Runway were glitchy and grainy. Since then, the tech has been getting better fast. Runway’s gen-2 model, released last year, can produce short clips that come close to matching big-studio animation in their quality. But most of these examples are still only a few seconds long.  

The sample videos from OpenAI’s Sora are high-definition and full of detail. OpenAI also says it can generate videos up to a minute long. One video of a Tokyo street scene shows that Sora has learned how objects fit together in 3D: the camera swoops into the scene to follow a couple as they walk past a row of shops.

OpenAI also claims that Sora handles occlusion well. One problem with existing models is that they can fail to keep track of objects when they drop out of view. For example, if a truck passes in front of a street sign, the sign might not reappear afterward.  

In a video of a papercraft underwater scene, Sora has added what look like cuts between different pieces of footage, and the model has maintained a consistent style between them.

It’s not perfect. In the Tokyo video, cars to the left look smaller than the people walking beside them. They also pop in and out between the tree branches. “There’s definitely some work to be done in terms of long-term coherence,” says Brooks. “For example, if someone goes out of view for a long time, they won’t come back. The model kind of forgets that they were supposed to be there.”

Impressive as they are, the sample videos shown here were no doubt cherry-picked to show Sora at its best. Without more information, it is hard to know how representative they are of the model’s typical output.   

It may be some time before we find out. OpenAI’s announcement of Sora today is a tech tease, and the company says it has no current plans to release it to the public. Instead, OpenAI will today begin sharing the model with third-party safety testers for the first time.

In particular, the firm is worried about the potential misuses of fake but photorealistic video . “We’re being careful about deployment here and making sure we have all our bases covered before we put this in the hands of the general public,” says Aditya Ramesh, a scientist at OpenAI, who created the firm’s text-to-image model DALL-E .

But OpenAI is eyeing a product launch sometime in the future. As well as safety testers, the company is also sharing the model with a select group of video makers and artists to get feedback on how to make Sora as useful as possible to creative professionals. “The other goal is to show everyone what is on the horizon, to give a preview of what these models will be capable of,” says Ramesh.

To build Sora, the team adapted the tech behind DALL-E 3, the latest version of OpenAI’s flagship text-to-image model. Like most text-to-image models, DALL-E 3 uses what’s known as a diffusion model. These are trained to turn a fuzz of random pixels into a picture.

Sora takes this approach and applies it to videos rather than still images. But the researchers also added another technique to the mix. Unlike DALL-E or most other generative video models, Sora combines its diffusion model with a type of neural network called a transformer.

Transformers are great at processing long sequences of data, like words. That has made them the special sauce inside large language models like OpenAI’s GPT-4 and Google DeepMind’s Gemini . But videos are not made of words. Instead, the researchers had to find a way to cut videos into chunks that could be treated as if they were. The approach they came up with was to dice videos up across both space and time. “It’s like if you were to have a stack of all the video frames and you cut little cubes from it,” says Brooks.

The transformer inside Sora can then process these chunks of video data in much the same way that the transformer inside a large language model processes words in a block of text. The researchers say that this let them train Sora on many more types of video than other text-to-video models, varied in terms of resolution, duration, aspect ratio, and orientation. “It really helps the model,” says Brooks. “That is something that we’re not aware of any existing work on.”

“From a technical perspective it seems like a very significant leap forward,” says Sam Gregory, executive director at Witness, a human rights organization that specializes in the use and misuse of video technology. “But there are two sides to the coin,” he says. “The expressive capabilities offer the potential for many more people to be storytellers using video. And there are also real potential avenues for misuse.” 

OpenAI is well aware of the risks that come with a generative video model. We are already seeing the large-scale misuse of deepfake images . Photorealistic video takes this to another level.

Gregory notes that you could use technology like this to misinform people about conflict zones or protests. The range of styles is also interesting, he says. If you could generate shaky footage that looked like something shot with a phone, it would come across as more authentic.

The tech is not there yet, but generative video has gone from zero to Sora in just 18 months. “We’re going to be entering a universe where there will be fully synthetic content, human-generated content and a mix of the two,” says Gregory.

The OpenAI team plans to draw on the safety testing it did last year for DALL-E 3. Sora already includes a filter that runs on all prompts sent to the model that will block requests for violent, sexual, or hateful images, as well as images of known people. Another filter will look at frames of generated videos and block material that violates OpenAI’s safety policies.

OpenAI says it is also adapting a fake-image detector developed for DALL-E 3 to use with Sora. And the company will embed industry-standard C2PA tags , metadata that states how an image was generated, into all of Sora’s output. But these steps are far from foolproof. Fake-image detectors are hit-or-miss. Metadata is easy to remove, and most social media sites strip it from uploaded images by default.  

“We’ll definitely need to get more feedback and learn more about the types of risks that need to be addressed with video before it would make sense for us to release this,” says Ramesh.

Brooks agrees. “Part of the reason that we’re talking about this research now is so that we can start getting the input that we need to do the work necessary to figure out how it could be safely deployed,” he says.

Update 2/15: Comments from Sam Gregory were added .

Artificial intelligence

Ai for everything: 10 breakthrough technologies 2024.

Generative AI tools like ChatGPT reached mass adoption in record time, and reset the course of an entire industry.

What’s next for AI in 2024

Our writers look at the four hot trends to watch out for this year

  • Melissa Heikkilä archive page

Google’s Gemini is now in everything. Here’s how you can try it out.

Gmail, Docs, and more will now come with Gemini baked in. But Europeans will have to wait before they can download the app.

Deploying high-performance, energy-efficient AI

Investments into downsized infrastructure can help enterprises reap the benefits of AI while mitigating energy consumption, says corporate VP and GM of data center platform engineering and architecture at Intel, Zane Ball.

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IMAGES

  1. How to Write a Discussion Section

    discussion and analysis in research paper example

  2. How to Write Critical Analysis Essay with Examples

    discussion and analysis in research paper example

  3. 38+ Research Paper Samples

    discussion and analysis in research paper example

  4. 10 Easy Steps: How to Write a Critical Analysis Essay on an Article

    discussion and analysis in research paper example

  5. Analysis In A Research Paper

    discussion and analysis in research paper example

  6. Analysis essay format

    discussion and analysis in research paper example

VIDEO

  1. Research Methodology Paper Presentation

  2. How Technology Has Affected Education?

  3. Research Paper Methodology

  4. Writing A Research Paper: Discussion

  5. How to Assess the Quantitative Data Collected from Questionnaire

  6. Research Questions

COMMENTS

  1. How to Write a Discussion Section

    Step 1: Summarize your key findings Step 2: Give your interpretations Step 3: Discuss the implications Step 4: Acknowledge the limitations Step 5: Share your recommendations Discussion section example Other interesting articles Frequently asked questions about discussion sections What not to include in your discussion section

  2. How to Write Discussions and Conclusions

    a discussion of related research, and a comparison between your results and initial hypothesis. Tip: Not all journals share the same naming conventions. You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

  3. PDF Discussion Section for Research Papers

    Discussion Section for Research Papers The discussion section is one of the final parts of a research paper, in which an author describes, analyzes, and interprets their findings. They explain the significance of those results and tie everything back to the research question(s).

  4. 8. The Discussion

    The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from wher...

  5. Discussion Section of a Research Paper: Guide & Example

    Step 3. Relate Your Results. Readability checker. discussion section of a research paper is where the author analyzes and explains the importance of the study's results. It presents the conclusions drawn from the study, compares them to previous research, and addresses any potential limitations or weaknesses.

  6. How To Write A Dissertation Discussion Chapter

    The What, Why & How Explained Simply (With Examples) By: Jenna Crossley (PhD Cand). Reviewed By: Dr. Eunice Rautenbach | August 2021 If you're reading this, chances are you've reached the discussion chapter of your thesis or dissertation and are looking for a bit of guidance. Well, you've come to the right place!

  7. PDF 7th Edition Discussion Phrases Guide

    Papers usually end with a concluding section, often called the "Discussion.". The Discussion is your opportunity to evaluate and interpret the results of your study or paper, draw inferences and conclusions from it, and communicate its contributions to science and/or society. Use the present tense when writing the Discussion section.

  8. AP Research: Academic Paper: Discussion and Analysis

    The role of the discussion section is to explain your data and what it means for your project. Many students, thinking they're making discussion and analysis, simply regurgitate their numbers back in full sentences with a surface-level explanation. Phrases like "this shows" and others similar, while good building blocks and great planning tools ...

  9. How to Write the Discussion Section of a Research Paper

    The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research. This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study ...

  10. PDF Discussion and Conclusion Sections for Empirical Research Papers

    In an empirical research paper, the purpose of the Discussion section is to interpret the results and discuss their implications, thereby establishing (and often qualifying) the practical and scholarly significance of the present study.

  11. How to Write an APA Methods Section

    Structuring an APA methods section. The main heading of "Methods" should be centered, boldfaced, and capitalized. Subheadings within this section are left-aligned, boldfaced, and in title case. You can also add lower level headings within these subsections, as long as they follow APA heading styles. To structure your methods section, you ...

  12. How to write the analysis and discussion chapters in qualitative research

    In conclusion. The discussion chapters form the heart of your thesis and this is where your unique contribution comes to the forefront. This is where your data takes centre-stage and where you get to showcase your original arguments, perspectives and knowledge. To do this effectively needs you to explore the original themes and issues arising ...

  13. Q: How to write the Discussion section in a qualitative paper?

    1. Begin by discussing the research question and talking about whether it was answered in the research paper based on the results. 2. Highlight any unexpected and/or exciting results and link them to the research question 3. Point out some previous studies and draw comparisons on how your study is different 4.

  14. (PDF) How to Write an Effective Discussion in a Research Paper; a Guide

    Discussion is mainly the section in a research paper that makes the readers understand the exact meaning of the results achieved in a study by exploring the significant points of the research, its ...

  15. Writing a discussion section: how to integrate substantive and

    Background When discussing results medical research articles often tear substantive and statistical (methodical) contributions apart, just as if both were independent. Consequently, reasoning on bias tends to be vague, unclear and superficial. This can lead to over-generalized, too narrow and misleading conclusions, especially for causal research questions. Main body To get the best possible ...

  16. Discussion Section Examples and Writing Tips

    1. What is the purpose of the discussion section? The discussion section is one of the most important sections of your research paper. This is where you interpret your results, highlight your contributions, and explain the value of your work to your readers.

  17. Research Paper

    Discussion 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. Conclusion

  18. 07 Steps for writing Discussion Section of Research Paper

    Which are these 07 steps for writing an Effective Discussion Section of a Research Paper? I. Focus on the Relevance II. Highlight the Limitations III. Introduce New Discoveries IV.

  19. Discussion vs Analysis: Unraveling Commonly Confused Terms

    Here are a few examples of how to use "discussion" in a sentence: We had a lively discussion about the upcoming election. The teacher led a discussion about the assigned reading. Let's have a discussion about the best way to approach this problem. As you can see, "discussion" is used to describe a conversation or exchange of ideas.

  20. How to Write a Discussion Section

    Step 1: Summarise your key findings Step 2: Give your interpretations Step 3: Discuss the implications Step 4: Acknowledge the limitations Step 5: Share your recommendations Discussion section example What not to include in your discussion section There are a few common mistakes to avoid when writing the discussion section of your paper.

  21. Research Paper Analysis: How to Analyze a Research Article + Example

    Table of Contents 🔤 Research Analysis Definition 📋 Format 📊 How to Analyze a Research Article ️ How to Write a Research Analysis 📝 Analysis Example 🔎 More Examples 🔗 References 🔤 Research Paper Analysis: What Is It? We'll deliver a custom paper tailored to your requirements. We'll even cut 15% OFF your first order! Use discount

  22. PDF Chapter 4: Analysis and Interpretation of Results

    The analysis and interpretation of data is carried out in two phases. The. first part, which is based on the results of the questionnaire, deals with a quantitative. analysis of data. The second, which is based on the results of the interview and focus group. discussions, is a qualitative interpretation.

  23. How technology is reinventing K-12 education

    Study finds public pension plans on shaky ground. New research calls attention to a huge funding gap and growing risk exposure, raising alarms about the long-term viability of government pensions.

  24. Effect of exercise for depression: systematic review and network meta

    Objective To identify the optimal dose and modality of exercise for treating major depressive disorder, compared with psychotherapy, antidepressants, and control conditions. Design Systematic review and network meta-analysis. Methods Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Bayesian arm based, multilevel network meta ...

  25. OpenAI teases an amazing new generative video model called Sora

    OpenAI has built a striking new generative video model called Sora that can take a short text description and turn it into a detailed, high-definition film clip up to a minute long.. Based on four ...