discussion of research findings and application

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

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.

Adam Goulston, PsyD, MS, MBA, MISD, ELS

Science Marketing Consultant

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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; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

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; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

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 The Discussion Chapter

A Simple Explainer With Examples + Free Template

By: Jenna Crossley (PhD) | 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: The Discussion Chapter

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

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.

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?

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 .

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.

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

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36 Comments

Thank you this is helpful!

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

This has been very helpful indeed. Thank you.

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

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

This is really good and explicit. Thanks

Thank you, this blog has been such a help.

Thank you. This is very helpful.

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

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.

Thanks! This is helpful!

thanks alot for this informative website

Dear Sir/Madam,

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

Thank you very much!!!

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.

This was helpful

Thanks Jenna, well explained.

Thank you! This is super helpful.

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.

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

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!!!!!!!!!

Thanks alot.., I have gained much

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

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.

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

this was a very helpful and useful information

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

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

Superlative! More grease to your elbows.

Powerful, thank you for sharing.

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!

Simplified explanation. Well done.

The presentation is enlightening. Thank you very much.

Thanks for the support and guidance

This has been a great help to me and thank you do much

I second that “it is highly unlikely that a dissertation or thesis will scientifically prove something”; although, could you enlighten us on that comment and elaborate more please?

Sure, no problem.

Scientific proof is generally considered a very strong assertion that something is definitively and universally true. In most scientific disciplines, especially within the realms of natural and social sciences, absolute proof is very rare. Instead, researchers aim to provide evidence that supports or rejects hypotheses. This evidence increases or decreases the likelihood that a particular theory is correct, but it rarely proves something in the absolute sense.

Dissertations and theses, as substantial as they are, typically focus on exploring a specific question or problem within a larger field of study. They contribute to a broader conversation and body of knowledge. The aim is often to provide detailed insight, extend understanding, and suggest directions for further research rather than to offer definitive proof. These academic works are part of a cumulative process of knowledge building where each piece of research connects with others to gradually enhance our understanding of complex phenomena.

Furthermore, the rigorous nature of scientific inquiry involves continuous testing, validation, and potential refutation of ideas. What might be considered a “proof” at one point can later be challenged by new evidence or alternative interpretations. Therefore, the language of “proof” is cautiously used in academic circles to maintain scientific integrity and humility.

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Guide to Writing the Results and Discussion Sections of a Scientific Article

A quality research paper has both the qualities of in-depth research and good writing ( Bordage, 2001 ). In addition, a research paper must be clear, concise, and effective when presenting the information in an organized structure with a logical manner ( Sandercock, 2013 ).

In this article, we will take a closer look at the results and discussion section. Composing each of these carefully with sufficient data and well-constructed arguments can help improve your paper overall.

The results section of your research paper contains a description about the main findings of your research, whereas the discussion section interprets the results for readers and provides the significance of the findings. The discussion should not repeat the results.

Let’s dive in a little deeper about how to properly, and clearly organize each part.

How to Organize the Results Section

Since your results follow your methods, you’ll want to provide information about what you discovered from the methods you used, such as your research data. In other words, what were the outcomes of the methods you used?

You may also include information about the measurement of your data, variables, treatments, and statistical analyses.

To start, organize your research data based on how important those are in relation to your research questions. This section should focus on showing major results that support or reject your research hypothesis. Include your least important data as supplemental materials when submitting to the journal.

The next step is to prioritize your research data based on importance – focusing heavily on the information that directly relates to your research questions using the subheadings.

The organization of the subheadings for the results section usually mirrors the methods section. It should follow a logical and chronological order.

Subheading organization

Subheadings within your results section are primarily going to detail major findings within each important experiment. And the first paragraph of your results section should be dedicated to your main findings (findings that answer your overall research question and lead to your conclusion) (Hofmann, 2013).

In the book “Writing in the Biological Sciences,” author Angelika Hofmann recommends you structure your results subsection paragraphs as follows:

• Experimental purpose
• Interpretation

Each subheading may contain a combination of ( Bahadoran, 2019 ; Hofmann, 2013, pg. 62-63):

• Text: to explain about the research data
• Figures: to display the research data and to show trends or relationships, for examples using graphs or gel pictures.
• Tables: to represent a large data and exact value

Decide on the best way to present your data — in the form of text, figures or tables (Hofmann, 2013).

Data or Results?

Sometimes we get confused about how to differentiate between data and results . Data are information (facts or numbers) that you collected from your research ( Bahadoran, 2019 ).

Whereas, results are the texts presenting the meaning of your research data ( Bahadoran, 2019 ).

One mistake that some authors often make is to use text to direct the reader to find a specific table or figure without further explanation. This can confuse readers when they interpret data completely different from what the authors had in mind. So, you should briefly explain your data to make your information clear for the readers.

Common Elements in Figures and Tables

Figures and tables present information about your research data visually. The use of these visual elements is necessary so readers can summarize, compare, and interpret large data at a glance. You can use graphs or figures to compare groups or patterns. Whereas, tables are ideal to present large quantities of data and exact values.

Several components are needed to create your figures and tables. These elements are important to sort your data based on groups (or treatments). It will be easier for the readers to see the similarities and differences among the groups.

When presenting your research data in the form of figures and tables, organize your data based on the steps of the research leading you into a conclusion.

Common elements of the figures (Bahadoran, 2019):

• Figure number
• Figure title
• Figure legend (for example a brief title, experimental/statistical information, or definition of symbols).

Tables in the result section may contain several elements (Bahadoran, 2019):

• Table number
• Table title
• Row headings (for example groups)
• Column headings
• Row subheadings (for example categories or groups)
• Column subheadings (for example categories or variables)
• Footnotes (for example statistical analyses)

Tips to Write the Results Section

• Direct the reader to the research data and explain the meaning of the data.
• Avoid using a repetitive sentence structure to explain a new set of data.
• Write and highlight important findings in your results.
• Use the same order as the subheadings of the methods section.
• Match the results with the research questions from the introduction. Your results should answer your research questions.
• Be sure to mention the figures and tables in the body of your text.
• Make sure there is no mismatch between the table number or the figure number in text and in figure/tables.
• Only present data that support the significance of your study. You can provide additional data in tables and figures as supplementary material.

How to Organize the Discussion Section

It’s not enough to use figures and tables in your results section to convince your readers about the importance of your findings. You need to support your results section by providing more explanation in the discussion section about what you found.

In the discussion section, based on your findings, you defend the answers to your research questions and create arguments to support your conclusions.

Below is a list of questions to guide you when organizing the structure of your discussion section ( Viera et al ., 2018 ):

• What experiments did you conduct and what were the results?
• What do the results mean?
• What were the important results from your study?
• How did the results answer your research questions?
• Did your results support your hypothesis or reject your hypothesis?
• What are the variables or factors that might affect your results?
• What were the strengths and limitations of your study?
• What other published works support your findings?
• What other published works contradict your findings?
• What possible factors might cause your findings different from other findings?
• What is the significance of your research?
• What are new research questions to explore based on your findings?

Organizing the Discussion Section

The structure of the discussion section may be different from one paper to another, but it commonly has a beginning, middle-, and end- to the section.

One way to organize the structure of the discussion section is by dividing it into three parts (Ghasemi, 2019):

• The beginning: The first sentence of the first paragraph should state the importance and the new findings of your research. The first paragraph may also include answers to your research questions mentioned in your introduction section.
• The middle: The middle should contain the interpretations of the results to defend your answers, the strength of the study, the limitations of the study, and an update literature review that validates your findings.
• The end: The end concludes the study and the significance of your research.

Another possible way to organize the discussion section was proposed by Michael Docherty in British Medical Journal: is by using this structure ( Docherty, 1999 ):

• Discussion of important findings
• Comparison of your results with other published works
• Include the strengths and limitations of the study
• Conclusion and possible implications of your study, including the significance of your study – address why and how is it meaningful
• Future research questions based on your findings

Finally, a last option is structuring your discussion this way (Hofmann, 2013, pg. 104):

• First Paragraph: Provide an interpretation based on your key findings. Then support your interpretation with evidence.
• Secondary results
• Limitations
• Unexpected findings
• Comparisons to previous publications
• Last Paragraph: The last paragraph should provide a summarization (conclusion) along with detailing the significance, implications and potential next steps.

Remember, at the heart of the discussion section is presenting an interpretation of your major findings.

Tips to Write the Discussion Section

• Highlight the significance of your findings
• Mention how the study will fill a gap in knowledge.
• Indicate the implication of your research.
• Avoid generalizing, misinterpreting your results, drawing a conclusion with no supportive findings from your results.

Aggarwal, R., & Sahni, P. (2018). The Results Section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 21-38): Springer.

Bahadoran, Z., Mirmiran, P., Zadeh-Vakili, A., Hosseinpanah, F., & Ghasemi, A. (2019). The principles of biomedical scientific writing: Results. International journal of endocrinology and metabolism, 17(2).

Bordage, G. (2001). Reasons reviewers reject and accept manuscripts: the strengths and weaknesses in medical education reports. Academic medicine, 76(9), 889-896.

Cals, J. W., & Kotz, D. (2013). Effective writing and publishing scientific papers, part VI: discussion. Journal of clinical epidemiology, 66(10), 1064.

Docherty, M., & Smith, R. (1999). The case for structuring the discussion of scientific papers: Much the same as that for structuring abstracts. In: British Medical Journal Publishing Group.

Faber, J. (2017). Writing scientific manuscripts: most common mistakes. Dental press journal of orthodontics, 22(5), 113-117.

Fletcher, R. H., & Fletcher, S. W. (2018). The discussion section. In Reporting and Publishing Research in the Biomedical Sciences (pp. 39-48): Springer.

Ghasemi, A., Bahadoran, Z., Mirmiran, P., Hosseinpanah, F., Shiva, N., & Zadeh-Vakili, A. (2019). The Principles of Biomedical Scientific Writing: Discussion. International journal of endocrinology and metabolism, 17(3).

Hofmann, A. H. (2013). Writing in the biological sciences: a comprehensive resource for scientific communication . New York: Oxford University Press.

Kotz, D., & Cals, J. W. (2013). Effective writing and publishing scientific papers, part V: results. Journal of clinical epidemiology, 66(9), 945.

Mack, C. (2014). How to Write a Good Scientific Paper: Structure and Organization. Journal of Micro/ Nanolithography, MEMS, and MOEMS, 13. doi:10.1117/1.JMM.13.4.040101

Moore, A. (2016). What's in a Discussion section? Exploiting 2‐dimensionality in the online world…. Bioessays, 38(12), 1185-1185.

Peat, J., Elliott, E., Baur, L., & Keena, V. (2013). Scientific writing: easy when you know how: John Wiley & Sons.

Sandercock, P. M. L. (2012). How to write and publish a scientific article. Canadian Society of Forensic Science Journal, 45(1), 1-5.

Teo, E. K. (2016). Effective Medical Writing: The Write Way to Get Published. Singapore Medical Journal, 57(9), 523-523. doi:10.11622/smedj.2016156

Van Way III, C. W. (2007). Writing a scientific paper. Nutrition in Clinical Practice, 22(6), 636-640.

Vieira, R. F., Lima, R. C. d., & Mizubuti, E. S. G. (2019). How to write the discussion section of a scientific article. Acta Scientiarum. Agronomy, 41.

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" discussion section

"discussion and conclusions checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018., peer review.

• LITERATURE CITED
• Bibliography of guides to scientific writing and presenting
• Presentations
• Lab Report Writing Guides on the Web

This is is usually the hardest section to write. You are trying to bring out the true meaning of your data without being too long. Do not use words to conceal your facts or reasoning. Also do not repeat your results, this is a discussion.

• Present principles, relationships and generalizations shown by the results
• Point out exceptions or lack of correlations. Define why you think this is so.
• Show how your results agree or disagree with previously published works
• Discuss the theoretical implications of your work as well as practical applications
• State your conclusions clearly. Summarize your evidence for each conclusion.
• Discuss the significance of the results
•  Evidence does not explain itself; the results must be presented and then explained.
• Typical stages in the discussion: summarizing the results, discussing whether results are expected or unexpected, comparing these results to previous work, interpreting and explaining the results (often by comparison to a theory or model), and hypothesizing about their generality.
• Discuss any problems or shortcomings encountered during the course of the work.
• Discuss possible alternate explanations for the results.
• Avoid: presenting results that are never discussed; presenting discussion that does not relate to any of the results; presenting results and discussion in chronological order rather than logical order; ignoring results that do not support the conclusions; drawing conclusions from results without logical arguments to back them up. 

CONCLUSIONS

• Provide a very brief summary of the Results and Discussion.
• Emphasize the implications of the findings, explaining how the work is significant and providing the key message(s) the author wishes to convey.
• Provide the most general claims that can be supported by the evidence.
• Provide a future perspective on the work.
• Avoid: repeating the abstract; repeating background information from the Introduction; introducing new evidence or new arguments not found in the Results and Discussion; repeating the arguments made in the Results and Discussion; failing to address all of the research questions set out in the Introduction. 

WHAT HAPPENS AFTER I COMPLETE MY PAPER?

 The peer review process is the quality control step in the publication of ideas.  Papers that are submitted to a journal for publication are sent out to several scientists (peers) who look carefully at the paper to see if it is "good science".  These reviewers then recommend to the editor of a journal whether or not a paper should be published. Most journals have publication guidelines. Ask for them and follow them exactly.    Peer reviewers examine the soundness of the materials and methods section.  Are the materials and methods used written clearly enough for another scientist to reproduce the experiment?  Other areas they look at are: originality of research, significance of research question studied, soundness of the discussion and interpretation, correct spelling and use of technical terms, and length of the article.

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

The PhD Discussion Chapter: What It Is & How To Write It

Sep 11, 2023

Your PhD discussion chapter is your thesis’s intellectual epicenter. Think of it as the scholarly equivalent of a courtroom closing argument, where you summarise the evidence and make your case. Perhaps that’s why it’s so tricky – the skills you need in your discussion chapter aren’t skills you’ve likely had to deploy before: it’s where you start to speak like a Doctor.

In this guide, I want to present a comprehensive guide to the PhD discussion chapter. We’ll look at a number of key topics:

What is the purpose of a PhD Discussion Chapter?

• Suggested outlines for a discussion chapter:

Advice for improving your discussion chapter

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  The PhD discussion chapter is the place where your findings, research questions, literature, theoretical framework and methodology coalesce into a coherent narrative. A common pitfall is when students see the discussion chapter as a summary of everything that has come before. This isn’t the case. Instead, the PhD discussion chapter offers a deep, analytical synthesis of your research, providing context, interpretation, and evaluation of your findings.

It’s the place in which you engage with existing theories, explore the significance of your work, and directly address the “So What?” question, highlighting the real-world implications and academic contributions of your research.

 Let’s dig down into each of these things.

Summarising and explaining the research

Before you launch into the detail, start by laying out your findings in a clear, easy to follow way. This is typically done in the introduction and the first proper section of the chapter.

Starting the PhD discussion chapter by clearly laying out your findings serves as an anchor for your reader and sets the stage for the more complex discussions that follow. This foundational step ensures that the reader is equipped with all the necessary information to fully grasp the significance and implications of your work. It’s akin to laying the groundwork before building a complex structure; without a solid base, the intricate analyses may lose their impact or be misunderstood.

For example, if you’re a PhD student in environmental science studying the effects of a specific pollutant on marine life, begin by presenting the key data points, such as the pollutant concentration levels in various regions and the corresponding health indices of marine species studied. Use tables, figures, or graphs to help visualise the data and make it more accessible.

• Laying out Quantitative Findings : If your research is quantitative, use statistical measures to present your results. Clearly state the metrics you’ve considered, such as means, variances, p-values, etc., and what they imply about your research question.
• Laying out Qualitative Findings : In case of qualitative research, such as ethnographic studies or interviews, narrate the trends, patterns, or themes that have emerged. Use representative quotes or observations as illustrative examples.
• Mixed-Methods Approach : If you’ve used both quantitative and qualitative methods, start by outlining how these different types of data will be integrated in your discussion. This could involve presenting the qualitative findings as a contextual backdrop for quantitative data or vice versa.

Remember, your objective at this initial stage is not to overwhelm the reader with complexity but to build a transparent, easily-followable narrative of what you’ve found. By starting with a clear presentation of your findings, you’re laying the groundwork for a powerful, credible discussion chapter that can tackle sophisticated analyses and weighty implications, underpinned by a comprehensible and compelling dataset.

There will be a necessary degree of overlap and repetition between this section (and the discussion chapter in general) and the findings chapter. However, there’s a subtle difference in the way in which the data is introduced in the findings and discussion chapters .

In the findings chapter, you’re generally presenting raw data or observations without interpreting what they mean. In the Discussion chapter, you take those same findings and begin to explore their implications, relate them to existing theories, and evaluate their significance. The danger, however, lies in creating excessive repetition between the two chapters, which can fatigue the reader and dilute the impact of your arguments.

To mitigate this, consider employing the following strategies:

• Selective Highlighting : Choose only the most critical findings to revisit in the Discussion chapter. You don’t need to regurgitate every data point, only those central to the questions you aim to answer in this chapter.
• Narrative Framing : When you bring up a finding in the Discussion chapter, introduce it as a stepping stone to a broader point or argument, rather than an isolated fact. This technique helps the reader understand why you’re revisiting this information and what new aspects you’ll be unveiling.
• Use Different Presentation Formats : If the Findings chapter is heavy on tables and figures, consider summarising key points in a narrative form in the Discussion chapter or vice versa.

By thoughtfully selecting what to revisit and framing it within a new context, you can transform what might appear as repetition into a coherent and evolving narrative that adds value to your thesis. Read more about the difference between the findings and discussion chapters here .

Interpreting and Contextualising Results 

It’s in the discussion chapter that you offer the interpretation and context for your research findings.

Here, you transition from being a data ‘gatherer’ to a data ‘interpreter’, weaving together the threads of research questions, data, methods, literature and theory to tell a complex story. While the Results chapter may offer the “what,” the PhD discussion chapter sheds light on the “why” and “how.” 

For example, if you’re a social scientist studying the effects of social media on mental health, your results chapter might show statistical data indicating a correlation between social media use and anxiety. However, it’s in your discussion chapter that you would compare these findings to existing literature, perhaps linking them to existing theories or debates. This adds a layer of depth and context that transcends the numerical data, inviting academic dialogue and potential future research avenues.

There are three ways in which you can synthesise your findings:

• Interpretation : Begin by interpreting your findings. Use comparisons, contrasts, and correlations to explain the significance of the results. This is where you should also address any unexpected outcomes and explain them.
• Contextualisation : After interpretation, provide a context to situate your findings within the existing body of knowledge. Link back to your Literature Review and Theoretical Framework to show how your research aligns with or diverges from previous work. More on this below.
• Evaluation : Finally, critically evaluate your own research. Discuss its limitations, the implications of your findings, and offer recommendations for future research.

Whether you’re in natural sciences exploring a new chemical compound or in humanities dissecting a piece of classical literature, the discussion chapter is your opportunity to show that your research not only answers specific questions but also contributes to a wider understanding of your field. It’s not enough to say, for instance, that a new drug successfully reduced symptoms of depression in 60% of study participants. You must explore what that 60% means.

• Is it a statistically significant improvement over existing treatments?
• What might be the physiological or psychological mechanisms at work?
• Could your research method have influenced these outcomes?

There’s an art to explaining and synthesising your findings [Link to “How to Explain Your Findings”], but think of it this way: this is where you shine a light on the ‘why’ and ‘how’ of your findings, delving into the nuances that raw data can’t express.

Evaluating Existing Theories and Models  

Beyond explaining your findings, the PhD discussion chapter allows you to evaluate the existing theories and models that you’ve cited in your literature review  and/or theory framework chapter (not sure of the difference? Click here) . Your results could either reinforce established theories or challenge them, both of which significantly contribute to your field.

• For instance, did your research on renewable energy technologies confirm the economic theories suggesting that green energy can be cost-effective?
• Or did your social research provide empirical evidence that contradicts widely held beliefs in your field?

The PhD discussion chapter therefore serves as the space where the theories, concepts, ideas and hypotheses that make up and informed your theory framework and which you touched upon in your literature review intersect with the empirical data you’ve presented.

You’re not just mapping your findings onto the theories and models; you’re dissecting them, affirming or challenging them, and potentially even extending or refining them based on what you’ve discovered.

For instance, if you’re working on a thesis in psychology concerning cognitive development in early childhood, your Literature Review may have discussed Piaget’s stages of cognitive development. However, let’s say your findings indicate some nuances or exceptions to Piaget’s theories, or perhaps children in a certain demographic don’t follow the stages as previously thought.

Your discussion chapter is where you can make the argument that perhaps Piaget’s model, while generally accurate, might require some modification to account for these cases.

• Affirming Theories : If your data aligns closely with the existing theories and models, the PhD discussion chapter serves to strengthen their credibility. Here, you’re lending empirical support to theoretical frameworks.
• Challenging Theories : Alternatively, your findings might contradict or challenge the prevailing theories. This is not a shortcoming; instead, it opens the door for re-evaluation and progress in the field, which is just as valuable.
• Extending or Refining Theories : Perhaps your research uncovers additional variables or conditions that existing models have not accounted for. In such cases, you’re pushing the envelope, extending the current boundaries of understanding.

As you evaluate existing theories and models, be comprehensive yet nuanced. Draw on varied disciplines if relevant. For example, if your thesis is at the intersection of public health and social policy, integrate models from both fields to offer a multi-faceted discussion. Being interdisciplinary can make your discussion richer and more impactful.

Ultimately, the discussion chapter offers you a platform to voice your scholarly interpretation and judgment. You’re participating in a broader academic dialogue, not just narrating your findings but positioning them in a web of knowledge that spans across time, disciplines, and viewpoints.

Discuss Unexpected Results

The discussion chapter is where you also discuss things that didn’t quite work out as planned. In particular, results that were unexpected.

Sometimes the most perplexing data offers the most valuable insights. Don’t shy away from discussing unexpected results; these could be the starting points for future research or even paradigm shifts in your field.

When your research yields findings that diverge from established theories or commonly held beliefs, you’re offered a unique opportunity to challenge and extend existing knowledge.

Take the field of primary education as an illustrative example. Assume you’re researching the efficacy of a specific teaching methodology that prior studies have lauded. However, your data reveals that while the method works wonders for one subgroup of students, it fails to benefit another subgroup. Far from diminishing the value of your research, this unexpected outcome presents an exciting opening. It beckons further inquiry into why the teaching methodology yielded disparate impacts, which could eventually result in more tailored and effective educational strategies.

In the realm of scientific discoveries, the significance of unexpected results cannot be overstated. Alexander Fleming’s accidental discovery of penicillin originated from what appeared to be a ‘failed’ experiment, but it revolutionised medicine. Similarly, the unintended discovery of cosmic microwave background radiation provided pivotal support for the Big Bang theory. In both instances, what seemed like anomalies paved the way for transformative understanding.

The first task when you encounter unexpected findings is to set them apart from the expected outcomes clearly. Delineate a specific section in your discussion chapter to delve into these anomalies, affording them the attention they merit.

Next, engage in hypothesising why these peculiarities emerged. This could be the point where your years of study and your depth of understanding of your subject really shine. Are there confounding variables that weren’t initially apparent? Could there be an entirely unexplored underlying mechanism at play? Take your reader on this exploration with you, and offer educated guesses based on your literature review and study design.

Lastly, don’t forget to consider and discuss the wider implications of these findings. Could they potentially refute longstanding theories or present the need for a shift in the prevailing school of thought? Or perhaps they hint at previously unthought-of applications or solutions to existing problems? Reflect on how these unexpected results might fit into the broader academic conversation and where future research might take these findings.

By earnestly and transparently tackling unexpected results, you exhibit a commitment to rigorous academic research. The willingness to entertain complexity and to follow the research—even when it leads in unpredictable directions—is a mark of scholarly integrity and courage. This holds true irrespective of your academic discipline, from the humanities and social sciences to STEM fields.

Answering the “so what?” Question

 In your findings chapter you would have presented the data. In the discussion chapter, you answer the ‘so what’ question. Make sure to address it explicitly. Why does your research matter? Who benefits from it? How does it advance the scholarly discourse?

 As a PhD student, you’ve already invested a substantial amount of time and effort into your research. Therefore, it’s crucial to articulate its importance not only to validate your own work but also to contribute meaningfully to your field and, in some cases, to society at large.

 Answering the “so what?” question means connecting the dots between your isolated research findings and the larger intellectual landscape. It requires you to extend your analysis beyond the specifics of your study, considering how it advances the scholarly discourse in your field. For instance, if your research closes a significant gap in the literature, makes a theoretical breakthrough,

Example in Public Health : If your research finds that community-led sanitation programs are far more effective than government-implemented ones, then the “So What?” is clear: policy-makers need to see this data. But that doesn’t mean you don’t still need to discuss it.

Example in Literature : If your research uncovers previously unnoticed patterns of symbolism in 19th-century Russian literature, the “So What?” could be a deeper understanding of how literature reflects societal anxieties of the time.

In order to make your discussion chapter compelling and relevant, it’s imperative to always highlight why your research matters. This goes beyond simply reiterating your findings; you need to connect the dots and show how your research fits into the broader academic landscape. Are you challenging existing theories, confirming previous studies, or offering a new perspective? Establishing the academic importance of your work provides a solid footing for its wider application.

Further to establishing academic relevance, also aim to illuminate the real-world implications of your findings. What are the practical outcomes that could arise from your research? Are there specific scenarios or applications where your research could be a game-changer? For instance, if your study uncovers a more effective method of teaching reading to children with dyslexia, explicitly mention how this could revolutionise educational approaches and improve quality of life for those affected. Providing concrete scenarios enhances the applicability of your research, proving that it doesn’t merely exist in the realm of academic abstraction, but has tangible impacts that can affect change.

Limitations and Future Research

 The quest for perfection is more a journey than a destination. This especially holds true in the context of a PhD thesis. Therefore, a well-crafted Discussion chapter should include a section devoted to the limitations of your research, as it establishes the scope, reliability, and validity of your work. Acknowledging limitations is not an act of undermining your research; instead, it embodies scholarly integrity and rigorous academic thinking.

Being upfront about limitations is essentially about being honest, not only with your readers but also with yourself as a researcher. For instance, if you’ve conducted a survey-based study in psychology but only managed to collect a small number of responses, admitting this limitation provides context for your findings. Perhaps the conclusions drawn from such a sample size are not universally applicable but could still offer significant insights into a particular demographic or condition

• Do not shy away from discussing limitations in fear that it might weaken your arguments.
• Clearly delineate the scope of your research, specifying what it does and doesn’t address.

For example, in a medical research study, if your sample size predominantly consists of individuals from a particular age group, admitting this limitation helps frame your research within that context. Or, if you’re a literature student, if your analysis focuses solely on the works of a single author, your findings might not be generalisable to broader literary trends.

Discussing limitations openly doesn’t devalue your work; it adds a layer of trustworthiness. It assures the reader—and the academic community at large—that you have a nuanced understanding of your research context. It demonstrates that you can critically evaluate your own work, a skill that is paramount.

Your PhD Thesis. On one page.

Example outline for a discussion chapter:.

I’ve included a suggested outline for a PhD discussion chapter. It’s important to note that no two PhDs are alike, and yours may well (probably will) diverge from this. The purpose here is to show how all the various factors we’ve discussed above fit together.

Introduction

• Brief Overview of Research Objectives and Key Findings
• Purpose of the Discussion Chapter

Summary of Key Findings

• Brief Restatement of Research Findings
• Comparison with Initial Hypotheses or Research Questions

Interpretation of Findings

• Contextualisation of Results
• Significance and Implications of the Findings

Evaluation of Existing Theories and Models

• How Your Findings Support or Challenge Previous Work
• Conceptual Contributions of Your Study
• Acknowledgment of Study Limitations
• Suggestions for Future Research
• Summation of Key Points
• Broader Implications and Contributions of the Research
• Final Thoughts and Future Directions

Once you’ve navigated through the complexities of your PhD research, you’re now faced with the challenge of bringing it all together in your discussion chapter. While you’ve already considered various facets like summarising findings, evaluating existing theories, and acknowledging limitations, there are some “easy wins”—small, yet impactful steps—that can help strengthen this critical chapter.

The Power of a Well-Structured Narrative

Begin with a well-structured narrative that clearly outlines your arguments. Tell the reader what the destination is at the outset of the chapter, and then make sure each paragraph is a stepping stone to that destination.

Each paragraph should serve a purpose and should logically follow the previous one. This helps in making your discussion coherent and easy to follow.

• Use transition sentences between paragraphs to guide the reader through your argument.
• Make sure each paragraph adds a new dimension to your discussion.

Data Visualisation Tools

Visual aids aren’t just for presentations; they can provide tremendous value in a discussion chapter. Diagrams, charts, or graphs can provide a visual break and help to emphasise your points effectively.

• Use graphs or charts to represent trends that support your argument.
• Always caption your visuals and reference them in the text.

Integrate Feedback Actively

It’s often beneficial to have colleagues, advisors, or other experts review your discussion section before finalising it. They can offer fresh perspectives and may catch gaps or ambiguities that you’ve missed.

• Seek feedback but also know when to filter it, sticking to advice that genuinely enhances your work.
• Don’t wait until the last minute for feedback; give reviewers ample time.

Highlight the Broader Implications

While you’ll delve into this more in your conclusion, don’t shy away from previewing the broader implications of your work in your discussion. Make it clear why your research matters in a wider context.

• State the broader implications but keep them tightly related to your research findings.
• Avoid making grand claims that your research can’t support

In the journey toward a PhD, learning ‘how to write like a doctor’ is more than mastering grammar or honing your prose; it’s about flexing your academic muscles with confidence and authority. It is in the discussion chapter that you really start flexing, and which you really can and need to speak like a doctor.

For many, this is the first instance of challenging the hegemony of existing literature, refuting established theories, or proposing innovative frameworks. It’s an intimidating task; after all, these are the ideas and research paradigms you’ve been learning about throughout your educational journey. Suddenly, you’re not just absorbing knowledge; you’re contributing to it, critiquing it, and perhaps even changing its trajectory. If it feels challenging, remember that’s because it’s new, and that’s why it’s hard. However, you’ve made it this far, and that alone testifies to your academic rigour and capability. You’ve earned the right to be heard; now it’s time to speak with the academic authority that has been years in the making. So, don’t hold back—flex those academic muscles and carve your niche in the scholarly conversation.

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Writing a Research Paper Conclusion | Step-by-Step Guide

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

• Restate the problem statement addressed in the paper
• Summarize your overall arguments or findings
• Suggest the key takeaways from your paper

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

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Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

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Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

• Do they understand from your conclusion what your research was about?
• Are they able to summarize the implications of your findings?
• Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

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The conclusion of a research paper has several key elements you should make sure to include:

• A restatement of the research problem
• A summary of your key arguments and/or findings
• A short discussion of the implications of your research

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

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Caulfield, J. (2023, April 13). Writing a Research Paper Conclusion | Step-by-Step Guide. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/research-paper/research-paper-conclusion/

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Writing your Dissertation:  Results and Discussion

When writing a dissertation or thesis, the results and discussion sections can be both the most interesting as well as the most challenging sections to write.

You may choose to write these sections separately, or combine them into a single chapter, depending on your university’s guidelines and your own preferences.

There are advantages to both approaches.

Writing the results and discussion as separate sections allows you to focus first on what results you obtained and set out clearly what happened in your experiments and/or investigations without worrying about their implications.This can focus your mind on what the results actually show and help you to sort them in your head.

However, many people find it easier to combine the results with their implications as the two are closely connected.

Check your university’s requirements carefully before combining the results and discussions sections as some specify that they must be kept separate.

Results Section

The Results section should set out your key experimental results, including any statistical analysis and whether or not the results of these are significant.

You should cover any literature supporting your interpretation of significance. It does not have to include everything you did, particularly for a doctorate dissertation. However, for an undergraduate or master's thesis, you will probably find that you need to include most of your work.

You should write your results section in the past tense: you are describing what you have done in the past.

Every result included MUST have a method set out in the methods section. Check back to make sure that you have included all the relevant methods.

Conversely, every method should also have some results given so, if you choose to exclude certain experiments from the results, make sure that you remove mention of the method as well.

If you are unsure whether to include certain results, go back to your research questions and decide whether the results are relevant to them. It doesn’t matter whether they are supportive or not, it’s about relevance. If they are relevant, you should include them.

Having decided what to include, next decide what order to use. You could choose chronological, which should follow the methods, or in order from most to least important in the answering of your research questions, or by research question and/or hypothesis.

You also need to consider how best to present your results: tables, figures, graphs, or text. Try to use a variety of different methods of presentation, and consider your reader: 20 pages of dense tables are hard to understand, as are five pages of graphs, but a single table and well-chosen graph that illustrate your overall findings will make things much clearer.

Make sure that each table and figure has a number and a title. Number tables and figures in separate lists, but consecutively by the order in which you mention them in the text. If you have more than about two or three, it’s often helpful to provide lists of tables and figures alongside the table of contents at the start of your dissertation.

Summarise your results in the text, drawing on the figures and tables to illustrate your points.

The text and figures should be complementary, not repeat the same information. You should refer to every table or figure in the text. Any that you don’t feel the need to refer to can safely be moved to an appendix, or even removed.

Make sure that you including information about the size and direction of any changes, including percentage change if appropriate. Statistical tests should include details of p values or confidence intervals and limits.

While you don’t need to include all your primary evidence in this section, you should as a matter of good practice make it available in an appendix, to which you should refer at the relevant point.

For example:

Details of all the interview participants can be found in Appendix A, with transcripts of each interview in Appendix B.

You will, almost inevitably, find that you need to include some slight discussion of your results during this section. This discussion should evaluate the quality of the results and their reliability, but not stray too far into discussion of how far your results support your hypothesis and/or answer your research questions, as that is for the discussion section.

See our pages: Analysing Qualitative Data and Simple Statistical Analysis for more information on analysing your results.

Discussion Section

This section has four purposes, it should:

• Interpret and explain your results
• Answer your research question
• Justify your approach
• Critically evaluate your study

The discussion section therefore needs to review your findings in the context of the literature and the existing knowledge about the subject.

You also need to demonstrate that you understand the limitations of your research and the implications of your findings for policy and practice. This section should be written in the present tense.

The Discussion section needs to follow from your results and relate back to your literature review . Make sure that everything you discuss is covered in the results section.

Some universities require a separate section on recommendations for policy and practice and/or for future research, while others allow you to include this in your discussion, so check the guidelines carefully.

Starting the Task

Most people are likely to write this section best by preparing an outline, setting out the broad thrust of the argument, and how your results support it.

You may find techniques like mind mapping are helpful in making a first outline; check out our page: Creative Thinking for some ideas about how to think through your ideas. You should start by referring back to your research questions, discuss your results, then set them into the context of the literature, and then into broader theory.

This is likely to be one of the longest sections of your dissertation, and it’s a good idea to break it down into chunks with sub-headings to help your reader to navigate through the detail.

Fleshing Out the Detail

Once you have your outline in front of you, you can start to map out how your results fit into the outline.

This will help you to see whether your results are over-focused in one area, which is why writing up your research as you go along can be a helpful process. For each theme or area, you should discuss how the results help to answer your research question, and whether the results are consistent with your expectations and the literature.

The Importance of Understanding Differences

If your results are controversial and/or unexpected, you should set them fully in context and explain why you think that you obtained them.

Your explanations may include issues such as a non-representative sample for convenience purposes, a response rate skewed towards those with a particular experience, or your own involvement as a participant for sociological research.

You do not need to be apologetic about these, because you made a choice about them, which you should have justified in the methodology section. However, you do need to evaluate your own results against others’ findings, especially if they are different. A full understanding of the limitations of your research is part of a good discussion section.

At this stage, you may want to revisit your literature review, unless you submitted it as a separate submission earlier, and revise it to draw out those studies which have proven more relevant.

Conclude by summarising the implications of your findings in brief, and explain why they are important for researchers and in practice, and provide some suggestions for further work.

You may also wish to make some recommendations for practice. As before, this may be a separate section, or included in your discussion.

The results and discussion, including conclusion and recommendations, are probably the most substantial sections of your dissertation. Once completed, you can begin to relax slightly: you are on to the last stages of writing!

Continue to: Dissertation: Conclusion and Extras Writing your Methodology

See also: Writing a Literature Review Writing a Research Proposal Academic Referencing What Is the Importance of Using a Plagiarism Checker to Check Your Thesis?

This page has been archived and is no longer being updated regularly.

Degree In Sight

Discussing your findings

Your dissertation's discussion should tell a story, say experts. What do your data say?

By Beth Azar

"Many students reach this stage of their careers having been focused for several years on the 'trees,'" says Yale University cognitive psychology professor Brian Scholl, PhD. "This section of the dissertation provides an opportunity to revisit the 'forest.'"

Fellow students, your adviser and your dissertation committee members can help provide that outside perspective, adds Yale clinical psychology professor Susan Nolen-Hoeksema, PhD, who teaches a course on writing in psychology.

And while the discussion should put your research into context and tell a story, say experts, it should not overstate your conclusions. How do you find the balance? Follow these do's and don'ts.

DO: Provide context and explain why people should care. DON'T: Simply rehash your results.

Your discussion should begin with a cogent, one-paragraph summary of the study's key findings, but then go beyond that to put the findings into context, says Stephen Hinshaw, PhD, chair of the psychology department at the University of California, Berkeley.

"The point of a discussion, in my view, is to transcend 'just the facts,' and engage in productive speculation," he says.

That means going back to the literature and grappling with what your findings mean, including how they fit in with previous work. If your results differ from others' findings, you should try to explain why, says Nolen-Hoeksema. Then, launch into "bigger picture" issues. For example, a clinical study might discuss how psychologists might apply the findings in a clinical setting or a social psychology project might talk about political implications.

By exploring those kinds of implications, students address what Scholl considers the most important-and often overlooked-purpose of the discussion: to directly explain why others should care about your findings.

"You can't and shouldn't rely on others to intuitively appreciate the beauty and importance of your work," he says.

Sounds simple, right? In fact, choosing what to include can be overwhelming, warns sixth-year Yale University social psychology graduate student Aaron Sackett.

"It is easy to get caught up in the desire to be extremely comprehensive and to bring up every potential issue, flaw, future direction and tangentially related concept," says Sackett. "However, this will make your dissertation seem like it has raised more questions than it answers."

Limit your discussion to a handful of the most important points, as Sackett did on the advice of his adviser.

"No reader wants to wade through ten pages of suppositional reasoning," says Roddy Roediger, PhD, chair of psychology at Washington University.

DO: Emphasize the positive. DON'T: Exaggerate.

One of the biggest errors students make in their discussion is exaggeration, say experts. Speculation is fine as long as you acknowledge that you're speculating and you don't stray too far from your data, say experts. That includes avoiding language that implies causality when your study can only make relational conclusions.

"If your study was not a true experiment, replace verbs that imply causation with words and phrases such as 'correlated with,' 'was associated with' and 'related to,'" write John Cone, PhD, and Sharon Foster, PhD, in a forthcoming revision of "Dissertations and Theses from Start to Finish" (APA, 2006).

Steven David, PhD, who successfully defended his dissertation in clinical geropsychology at the University of Southern California last May, found this point to be particularly difficult. When he defended his master's thesis, his committee told him his conclusions went too far out on a limb. He used more restraint with his dissertation and his committee thought he wasn't positive enough.

"The moral here is to try to find a balance where you set a tone that indeed celebrates interesting findings without too many leaps, while at the same time reporting limitations without being unnecessarily negative," says David.

Indeed, every discussion should include a "humility" section that addresses the study's limitations, write Cone and Foster. But avoid beginning the discussion with a long list of study limitations, says Nolen-Hoeksema.

"This makes me think 'Then why should I care or believe anything you found,' and want to stop reading right there," she says. "Limitations should be noted, but after you've discussed your positive results."

DO: Look toward the future. DON'T: End with it.

Along with noting your work's limitations, it's helpful to also suggest follow-up studies. But don't dwell on the future at the expense of the present,says Scholl.

"I think that too many discussions make the mistake of ending with 'the future,'" he says. "Too often I am left excited not by what was in the dissertation, but by what was not in the dissertation."

Roediger agrees: "Conclude the general discussion with a strong paragraph stating the main point or points again, in somewhat different terms-if possible-than used before."

Remember, adds Scholl, you want readers to remember you and your work. The discussion section is the place to leave your mark. So instead of simply summarizing your data and suggesting a few obvious follow-up studies, think about presenting your data in a novel way, showing how the work might resolve an existing controversy in the literature or explaining how it connects to an entirely different literature.

By the time readers get to your discussion, they're tired, adds Sackett. Give them something clear, concise and interesting to read, and they're sure to appreciate it.

Beth Azar is a writer in Portland, Ore.

10 most common dissertation discussion mistakes

Starting with limitations instead of implications.

Going overboard on limitations, leading readers to wonder why they should read on.

Failing to acknowledge limitations or dismissing them out of hand. 

Making strong claims about weak results.

Failing to differentiate between strong and weak results as you make conclusions about them.

Lapsing into causal language when your data were correlational.

Repeating the introduction.

Restating the results without interpretation or links to other research.

Presenting new results; such data belong in the results section.

Offering no concluding statements or ending with the limitations.

Source: Susan Nolen-Hoeksema, PhD, Yale University

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Home » Implications in Research – Types, Examples and Writing Guide

Implications in Research – Types, Examples and Writing Guide

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Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

• Restate the main findings: Begin by restating the main findings of the study in a brief summary .
• Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
• Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
• Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
• Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
• Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

• To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
• To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
• To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
• To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
• To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

• Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
• Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
• Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
• Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
• Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

• Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
• Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
• Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
• Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
• Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
• Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
• Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

• Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
• Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
• Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
• Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
• Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
• Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

• Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
• Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
• Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

• Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
• Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
• Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
• Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
• Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Writing Tips

How to Write an “Implications of Research” Section

4-minute read

• 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

Are you currently working on a thesis or dissertation? Once you’ve finished your paper (implications included), our proofreading team can help ensure that your spelling, punctuation, and grammar are perfect. Consider submitting a 500-word document for free.

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Implications in research: A quick guide

Last updated

11 January 2024

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Short on time? Get an AI generated summary of this article instead

Implications are a bridge between data and action, giving insight into the effects of the research and what it means. It's a chance for researchers to explain the  why  behind the research. 

When writing a research paper , reviewers will want to see you clearly state the implications of your research. If it's missing, they’ll likely reject your article. 

Let's explore what research implications are, why they matter, and how to include them in your next article or research paper. 

• What are implications in research?

Research implications are the consequences of research findings. They go beyond results and explore your research’s ramifications. 

Researchers can connect their research to the real-world impact by identifying the implications. These can inform further research, shape policy, or spark new solutions to old problems. 

Always clearly state your implications so they’re obvious to the reader. Never leave the reader to guess why your research matters. While it might seem obvious to you, it may not be evident to someone who isn't a subject matter expert. 

For example, you may do important sociological research with political implications. If a policymaker can't understand or connect those implications logically with your research, it reduces your impact.

• What are the key features of implications?

When writing your implications, ensure they have these key features: 

Implications should be clear, concise, and easily understood by a broad audience. You'll want to avoid overly technical language or jargon. Clearly stating your implications increases their impact and accessibility. 

Implications should link to specific results within your research to ensure they’re grounded in reality. You want them to demonstrate an impact on a particular field or research topic . 

Evidence-based

Give your implications a solid foundation of evidence. They need to be rational and based on data from your research, not conjecture. An evidence-based approach to implications will lend credibility and validity to your work.

Implications should take a balanced approach, considering the research's potential positive and negative consequences. A balanced perspective acknowledges the challenges and limitations of research and their impact on stakeholders. 

Future-oriented

Don't confine your implications to their immediate outcomes. You can explore the long-term effects of the research, including the impact on future research, policy decisions, and societal changes. Looking beyond the immediate adds more relevance to your research. 

When your implications capture these key characteristics, your research becomes more meaningful, impactful, and engaging. 

• Types of implications in research

The implications of your research will largely depend on what you are researching. 

However, we can broadly categorize the implications of research into two types: 

Practical: These implications focus on real-world applications and could improve policies and practices.

Theoretical: These implications are broader and might suggest changes to existing theories of models of the world. 

You'll first consider your research's implications in these two broad categories. Will your key findings have a real-world impact? Or are they challenging existing theories? 

Once you've established whether the implications are theoretical or practical, you can break your implication into more specific types. This might include: 

Political implications: How findings influence governance, policies, or political decisions

Social implications: Effects on societal norms, behaviors, or cultural practices

Technological implications: Impact on technological advancements or innovation

Clinical implications: Effects on healthcare, treatments, or medical practices

Commercial or business-relevant implications: Possible strategic paths or actions

Implications for future research: Guidance for future research, such as new avenues of study or refining the study methods

When thinking about the implications of your research, keep them clear and relevant. Consider the limitations and context of your research. 

For example, if your study focuses on a specific population in South America, you may not be able to claim the research has the same impact on the global population. The implication may be that we need further research on other population groups. 

• Understanding recommendations vs. implications

While "recommendations" and "implications" may be interchangeable, they have distinct roles within research.

Recommendations suggest action. They are specific, actionable suggestions you could take based on the research. Recommendations may be a part of the larger implication. 

Implications explain consequences. They are broader statements about how the research impacts specific fields, industries, institutions, or societies. 

Within a paper, you should always identify your implications before making recommendations. 

While every good research paper will include implications of research, it's not always necessary to include recommendations. Some research could have an extraordinary impact without real-world recommendations. 

• How to write implications in research

Including implications of research in your article or journal submission is essential. You need to clearly state your implications to tell the reviewer or reader why your research matters. 

Because implications are so important, writing them can feel overwhelming.

Here’s our step-by-step guide to make the process more manageable:

1. Summarize your key findings

Start by summarizing your research and highlighting the key discoveries or emerging patterns. This summary will become the foundation of your implications. 

2. Identify the implications

Think critically about the potential impact of your key findings. Consider how your research could influence practices, policies, theories, or societal norms. 

Address the positive and negative implications, and acknowledge the limitations and challenges of your research. 

If you still need to figure out the implications of your research, reread your introduction. Your introduction should include why you’re researching the subject and who might be interested in the results. This can help you consider the implications of your final research. 

3. Consider the larger impact

Go beyond the immediate impact and explore the implications on stakeholders outside your research group. You might include policymakers, practitioners, or other researchers.

4. Support with evidence

Cite specific findings from your research that support the implications. Connect them to your original thesis statement. 

You may have included why this research matters in your introduction, but now you'll want to support that implication with evidence from your research. 

Your evidence may result in implications that differ from the expected impact you cited in the introduction of your paper or your thesis statement. 

5. Review for clarity

Review your implications to ensure they are clear, concise, and jargon-free. Double-check that your implications link directly to your research findings and original thesis statement. 

Following these steps communicates your research implications effectively, boosting its long-term impact. 

Where do implications go in your research paper?

Implications often appear in the discussion section of a research paper between the presentation of findings and the conclusion. 

Putting them here allows you to naturally transition from the key findings to why the research matters. You'll be able to convey the larger impact of your research and transition to a conclusion.

• Examples of research implications

Thinking about and writing research implications can be tricky. 

To spark your critical thinking skills and articulate implications for your research, here are a few hypothetical examples of research implications: 

Teaching strategies

A study investigating the effectiveness of a new teaching method might have practical implications for educators. 

The research might suggest modifying current teaching strategies or changing the curriculum’s design. 

There may be an implication for further research into effective teaching methods and their impact on student testing scores. 

Social media impact

A research paper examines the impact of social media on teen mental health. 

Researchers find that spending over an hour on social media daily has significantly worse mental health effects than 15 minutes. 

There could be theoretical implications around the relationship between technology and human behavior. There could also be practical implications in writing responsible social media usage guidelines. 

Disease prevalence

A study analyzes the prevalence of a particular disease in a specific population. 

The researchers find this disease occurs in higher numbers in mountain communities. This could have practical implications on policy for healthcare allocation and resource distribution. 

There may be an implication for further research into why the disease appears in higher numbers at higher altitudes.

These examples demonstrate the considerable range of implications that research can generate.

Clearly articulating the implications of research allows you to enhance the impact and visibility of your work as a researcher. It also enables you to contribute to societal advancements by sharing your knowledge.

The implications of your work could make positive changes in the world around us.

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• Dissertation
• 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.

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 …

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A step-by-step guide to causal study design using real-world data

• Open access
• Published: 19 June 2024

Cite this article

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• Sarah Ruth Hoffman 1 ,
• Nilesh Gangan 1 ,
• Xiaoxue Chen 2 ,
• Joseph L. Smith 1 ,
• Arlene Tave 1 ,
• Yiling Yang 1 ,
• Christopher L. Crowe 1 ,
• Susan dosReis 3 &
• Michael Grabner 1  

Due to the need for generalizable and rapidly delivered evidence to inform healthcare decision-making, real-world data have grown increasingly important to answer causal questions. However, causal inference using observational data poses numerous challenges, and relevant methodological literature is vast. We endeavored to identify underlying unifying themes of causal inference using real-world healthcare data and connect them into a single schema to aid in observational study design, and to demonstrate this schema using a previously published research example. A multidisciplinary team (epidemiology, biostatistics, health economics) reviewed the literature related to causal inference and observational data to identify key concepts. A visual guide to causal study design was developed to concisely and clearly illustrate how the concepts are conceptually related to one another. A case study was selected to demonstrate an application of the guide. An eight-step guide to causal study design was created, integrating essential concepts from the literature, anchored into conceptual groupings according to natural steps in the study design process. The steps include defining the causal research question and the estimand; creating a directed acyclic graph; identifying biases and design and analytic techniques to mitigate their effect, and techniques to examine the robustness of findings. The cardiovascular case study demonstrates the applicability of the steps to developing a research plan. This paper used an existing study to demonstrate the relevance of the guide. We encourage researchers to incorporate this guide at the study design stage in order to elevate the quality of future real-world evidence.

Avoid common mistakes on your manuscript.

1 Introduction

Approximately 50 new drugs are approved each year in the United States (Mullard 2022 ). For all new drugs, randomized controlled trials (RCTs) are the gold-standard by which potential effectiveness (“efficacy”) and safety are established. However, RCTs cannot guarantee how a drug will perform in a less controlled context. For this reason, regulators frequently require observational, post-approval studies using “real-world” data, sometimes even as a condition of drug approval. The “real-world” data requested by regulators is often derived from insurance claims databases and/or healthcare records. Importantly, these data are recorded during routine clinical care without concern for potential use in research. Yet, in recent years, there has been increasing use of such data for causal inference and regulatory decision making, presenting a variety of methodologic challenges for researchers and stakeholders to consider (Arlett et al. 2022 ; Berger et al. 2017 ; Concato and ElZarrad 2022 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Franklin and Schneeweiss 2017 ; Girman et al. 2014 ; Hernán and Robins 2016 ; International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 2022 ; International Society for Pharmacoepidemiology (ISPE) 2020 ; Stuart et al. 2013 ; U.S. Food and Drug Administration 2018 ; Velentgas et al. 2013 ).

Current guidance for causal inference using observational healthcare data articulates the need for careful study design (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Hernán and Robins 2016 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). In 2009, Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases (Cox et al. 2009 ). In 2013, Stuart et al. emphasized counterfactual theory and trial emulation, offered several approaches to address unmeasured confounding, and provided guidance on the use of propensity scores to balance confounding covariates (Stuart et al. 2013 ). In 2013, the Agency for Healthcare Research and Quality (AHRQ) released an extensive, 200-page guide to developing a protocol for comparative effectiveness research using observational data (Velentgas et al. 2013 ). The guide emphasized development of the research question, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs (Velentgas et al. 2013 ). In 2014, Girman et al. provided a clear set of steps for assessing study feasibility including examination of the appropriateness of the data for the research question (i.e., ‘fit-for-purpose’), empirical equipoise, and interpretability, stating that comparative effectiveness research using observational data “should be designed with the goal of drawing a causal inference” (Girman et al. 2014 ). In 2017 , Berger et al. described aspects of “study hygiene,” focusing on procedural practices to enhance confidence in, and credibility of, real-world data studies (Berger et al. 2017 ). Currently, the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) maintains a guide on methodological standards in pharmacoepidemiology which discusses causal inference using observational data and includes an overview of study designs, a chapter on methods to address bias and confounding, and guidance on writing statistical analysis plans (European Medicines Agency 2023 ). In addition to these resources, the “target trial framework” provides a structured approach to planning studies for causal inferences from observational databases (Hernán and Robins 2016 ; Wang et al. 2023b ). This framework, published in 2016, encourages researchers to first imagine a clinical trial for the study question of interest and then to subsequently design the observational study to reflect the hypothetical trial (Hernán and Robins 2016 ).

While the literature addresses critical issues collectively, there remains a need for a framework that puts key components, including the target trial approach, into a simple, overarching schema (Loveless 2022 ) so they can be more easily remembered, and communicated to all stakeholders including (new) researchers, peer-reviewers, and other users of the research findings (e.g., practicing providers, professional clinical societies, regulators). For this reason, we created a step-by-step guide for causal inference using administrative health data, which aims to integrate these various best practices at a high level and complements existing, more specific guidance, including those from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) (Berger et al. 2017 ; Cox et al. 2009 ; Girman et al. 2014 ). We demonstrate the application of this schema using a previously published paper in cardiovascular research.

This work involved a formative phase and an implementation phase to evaluate the utility of the causal guide. In the formative phase, a multidisciplinary team with research expertise in epidemiology, biostatistics, and health economics reviewed selected literature (peer-reviewed publications, including those mentioned in the introduction, as well as graduate-level textbooks) related to causal inference and observational healthcare data from the pharmacoepidemiologic and pharmacoeconomic perspectives. The potential outcomes framework served as the foundation for our conception of causal inference (Rubin 2005 ). Information was grouped into the following four concepts: (1) Defining the Research Question; (2) Defining the Estimand; (3) Identifying and Mitigating Biases; (4) Sensitivity Analysis. A step-by-step guide to causal study design was developed to distill the essential elements of each concept, organizing them into a single schema so that the concepts are clearly related to one another. References for each step of the schema are included in the Supplemental Table.

In the implementation phase we tested the application of the causal guide to previously published work (Dondo et al. 2017 ). The previously published work utilized data from the Myocardial Ischaemia National Audit Project (MINAP), the United Kingdom’s national heart attack register. The goal of the study was to assess the effect of β-blockers on all-cause mortality among patients hospitalized for acute myocardial infarction without heart failure or left ventricular systolic dysfunction. We selected this paper for the case study because of its clear descriptions of the research goal and methods, and the explicit and methodical consideration of potential biases and use of sensitivity analyses to examine the robustness of the main findings.

3.1 Overview of the eight steps

The step-by-step guide to causal inference comprises eight distinct steps (Fig.  1 ) across the four concepts. As scientific inquiry and study design are iterative processes, the various steps may be completed in a different order than shown, and steps may be revisited.

A step-by-step guide for causal study design

Abbreviations: GEE: generalized estimating equations; IPC/TW: inverse probability of censoring/treatment weighting; ITR: individual treatment response; MSM: marginal structural model; TE: treatment effect

Please refer to the Supplemental Table for references providing more in-depth information.

1 Ensure that the exposure and outcome are well-defined based on literature and expert opinion.

2 More specifically, measures of association are not affected by issues such as confounding and selection bias because they do not intend to isolate and quantify a single causal pathway. However, information bias (e.g., variable misclassification) can negatively affect association estimates, and association estimates remain subject to random variability (and are hence reported with confidence intervals).

3 This list is not exhaustive; it focuses on frequently encountered biases.

4 To assess bias in a nonrandomized study following the target trial framework, use of the ROBINS-I tool is recommended ( https://www.bmj.com/content/355/bmj.i4919 ).

5 Only a selection of the most popular approaches is presented here. Other methods exist; e.g., g-computation and g-estimation for both time-invariant and time-varying analysis; instrumental variables; and doubly-robust estimation methods. There are also program evaluation methods (e.g., difference-in-differences, regression discontinuities) that can be applied to pharmacoepidemiologic questions. Conventional outcome regression analysis is not recommended for causal estimation due to issues determining covariate balance, correct model specification, and interpretability of effect estimates.

6 Online tools include, among others, an E-value calculator for unmeasured confounding ( https://www.evalue-calculator.com /) and the P95 outcome misclassification estimator ( http://apps.p-95.com/ISPE /).

3.2 Defining the Research question (step 1)

The process of designing a study begins with defining the research question. Research questions typically center on whether a causal relationship exists between an exposure and an outcome. This contrasts with associative questions, which, by their nature, do not require causal study design elements because they do not attempt to isolate a causal pathway from a single exposure to an outcome under study. It is important to note that the phrasing of the question itself should clarify whether an association or a causal relationship is of interest. The study question “Does statin use reduce the risk of future cardiovascular events?” is explicitly causal and requires that the study design addresses biases such as confounding. In contrast, the study question “Is statin use associated with a reduced risk of future cardiovascular events?” can be answered without control of confounding since the word “association” implies correlation. Too often, however, researchers use the word “association” to describe their findings when their methods were created to address explicitly causal questions (Hernán 2018 ). For example, a study that uses propensity score-based methods to balance risk factors between treatment groups is explicitly attempting to isolate a causal pathway by removing confounding factors. This is different from a study that intends only to measure an association. In fact, some journals may require that the word “association” be used when causal language would be more appropriate; however, this is beginning to change (Flanagin et al. 2024 ).

3.3 Defining the estimand (steps 2, 3, 4)

The estimand is the causal effect of research interest and is described in terms of required design elements: the target population for the counterfactual contrast, the kind of effect, and the effect/outcome measure.

In Step 2, the study team determines the target population of interest, which depends on the research question of interest. For example, we may want to estimate the effect of the treatment in the entire study population, i.e., the hypothetical contrast between all study patients taking the drug of interest versus all study patients taking the comparator (the average treatment effect; ATE). Other effects can be examined, including the average treatment effect in the treated or untreated (ATT or ATU).When covariate distributions are the same across the treated and untreated populations and there is no effect modification by covariates, these effects are generally the same (Wang et al. 2017 ). In RCTs, this occurs naturally due to randomization, but in non-randomized data, careful study design and statistical methods must be used to mitigate confounding bias.

In Step 3, the study team decides whether to measure the intention-to-treat (ITT), per-protocol, or as-treated effect. The ITT approach is also known as “first-treatment-carried-forward” in the observational literature (Lund et al. 2015 ). In trials, the ITT measures the effect of treatment assignment rather than the treatment itself, and in observational data the ITT can be conceptualized as measuring the effect of treatment as started . To compute the ITT effect from observational data, patients are placed into the exposure group corresponding to the treatment that they initiate, and treatment switching or discontinuation are purposely ignored in the analysis. Alternatively, a per-protocol effect can be measured from observational data by classifying patients according to the treatment that they initiated but censoring them when they stop, switch, or otherwise change treatment (Danaei et al. 2013 ; Yang et al. 2014 ). Finally, “as-treated” effects are estimated from observational data by classifying patients according to their actual treatment exposure during follow-up, for example by using multiple time windows to measure exposure changes (Danaei et al. 2013 ; Yang et al. 2014 ).

Step 4 is the final step in specifying the estimand in which the research team determines the effect measure of interest. Answering this question has two parts. First, the team must consider how the outcome of interest will be measured. Risks, rates, hazards, odds, and costs are common ways of measuring outcomes, but each measure may be best suited to a particular scenario. For example, risks assume patients across comparison groups have equal follow-up time, while rates allow for variable follow-up time (Rothman et al. 2008 ). Costs may be of interest in studies focused on economic outcomes, including as inputs to cost-effectiveness analyses. After deciding how the outcome will be measured, it is necessary to consider whether the resulting quantity will be compared across groups using a ratio or a difference. Ratios convey the effect of exposure in a way that is easy to understand, but they do not provide an estimate of how many patients will be affected. On the other hand, differences provide a clearer estimate of the potential public health impact of exposure; for example, by allowing the calculation of the number of patients that must be treated to cause or prevent one instance of the outcome of interest (Tripepi et al. 2007 ).

3.4 Identifying and mitigating biases (steps 5, 6, 7)

Observational, real-world studies can be subject to multiple potential sources of bias, which can be grouped into confounding, selection, measurement, and time-related biases (Prada-Ramallal et al. 2019 ).

In Step 5, as a practical first approach in developing strategies to address threats to causal inference, researchers should create a visual mapping of factors that may be related to the exposure, outcome, or both (also called a directed acyclic graph or DAG) (Pearl 1995 ). While creating a high-quality DAG can be challenging, guidance is increasingly available to facilitate the process (Ferguson et al. 2020 ; Gatto et al. 2022 ; Hernán and Robins 2020 ; Rodrigues et al. 2022 ; Sauer 2013 ). The types of inter-variable relationships depicted by DAGs include confounders, colliders, and mediators. Confounders are variables that affect both exposure and outcome, and it is necessary to control for them in order to isolate the causal pathway of interest. Colliders represent variables affected by two other variables, such as exposure and outcome (Griffith et al. 2020 ). Colliders should not be conditioned on since by doing so, the association between exposure and outcome will become distorted. Mediators are variables that are affected by the exposure and go on to affect the outcome. As such, mediators are on the causal pathway between exposure and outcome and should also not be conditioned on, otherwise a path between exposure and outcome will be closed and the total effect of the exposure on the outcome cannot be estimated. Mediation analysis is a separate type of analysis aiming to distinguish between direct and indirect (mediated) effects between exposure and outcome and may be applied in certain cases (Richiardi et al. 2013 ). Overall, the process of creating a DAG can create valuable insights about the nature of the hypothesized underlying data generating process and the biases that are likely to be encountered (Digitale et al. 2022 ). Finally, an extension to DAGs which incorporates counterfactual theory is available in the form of Single World Intervention Graphs (SWIGs) as described in a 2013 primer (Richardson and Robins 2013 ).

In Step 6, researchers comprehensively assess the possibility of different types of bias in their study, above and beyond what the creation of the DAG reveals. Many potential biases have been identified and summarized in the literature (Berger et al. 2017 ; Cox et al. 2009 ; European Medicines Agency 2023 ; Girman et al. 2014 ; Stuart et al. 2013 ; Velentgas et al. 2013 ). Every study can be subject to one or more biases, each of which can be addressed using one or more methods. The study team should thoroughly and explicitly identify all possible biases with consideration for the specifics of the available data and the nuances of the population and health care system(s) from which the data arise. Once the potential biases are identified and listed, the team can consider potential solutions using a variety of study design and analytic techniques.

In Step 7, the study team considers solutions to the biases identified in Step 6. “Target trial” thinking serves as the basis for many of these solutions by requiring researchers to consider how observational studies can be designed to ensure comparison groups are similar and produce valid inferences by emulating RCTs (Labrecque and Swanson 2017 ; Wang et al. 2023b ). Designing studies to include only new users of a drug and an active comparator group is one way of increasing the similarity of patients across both groups, particularly in terms of treatment history. Careful consideration must be paid to the specification of the time periods and their relationship to inclusion/exclusion criteria (Suissa and Dell’Aniello 2020 ). For instance, if a drug is used intermittently, a longer wash-out period is needed to ensure adequate capture of prior use in order to avoid bias (Riis et al. 2015 ). The study team should consider how to approach confounding adjustment, and whether both time-invariant and time-varying confounding may be present. Many potential biases exist, and many methods have been developed to address them in order to improve causal estimation from observational data. Many of these methods, such as propensity score estimation, can be enhanced by machine learning (Athey and Imbens 2019 ; Belthangady et al. 2021 ; Mai et al. 2022 ; Onasanya et al. 2024 ; Schuler and Rose 2017 ; Westreich et al. 2010 ). Machine learning has many potential applications in the causal inference discipline, and like other tools, must be used with careful planning and intentionality. To aid in the assessment of potential biases, especially time-related ones, and the development of a plan to address them, the study design should be visualized (Gatto et al. 2022 ; Schneeweiss et al. 2019 ). Additionally, we note the opportunity for collaboration across research disciplines (e.g., the application of difference-in-difference methods (Zhou et al. 2016 ) to the estimation of comparative drug effectiveness and safety).

3.5 Quality Control & sensitivity analyses (step 8)

Causal study design concludes with Step 8, which includes planning quality control and sensitivity analyses to improve the internal validity of the study. Quality control begins with reviewing study output for prima facie validity. Patient characteristics (e.g., distributions of age, sex, region) should align with expected values from the researchers’ intuition and the literature, and researchers should assess reasons for any discrepancies. Sensitivity analyses should be conducted to determine the robustness of study findings. Researchers can test the stability of study estimates using a different estimand or type of model than was used in the primary analysis. Sensitivity analysis estimates that are similar to those of the primary analysis might confirm that the primary analysis estimates are appropriate. The research team may be interested in how changes to study inclusion/exclusion criteria may affect study findings or wish to address uncertainties related to measuring the exposure or outcome in the administrative data by modifying the algorithms used to identify exposure or outcome (e.g., requiring hospitalization with a diagnosis code in a principal position rather than counting any claim with the diagnosis code in any position). As feasible, existing validation studies for the exposure and outcome should be referenced, or new validation efforts undertaken. The results of such validation studies can inform study estimates via quantitative bias analyses (Lanes and Beachler 2023 ). The study team may also consider biases arising from unmeasured confounding and plan quantitative bias analyses to explore how unmeasured confounding may impact estimates. Quantitative bias analysis can assess the directionality, magnitude, and uncertainty of errors arising from a variety of limitations (Brenner and Gefeller 1993 ; Lash et al. 2009 , 2014 ; Leahy et al. 2022 ).

3.6 Illustration using a previously published research study

In order to demonstrate how the guide can be used to plan a research study utilizing causal methods, we turn to a previously published study (Dondo et al. 2017 ) that assessed the causal relationship between the use of 𝛽-blockers and mortality after acute myocardial infarction in patients without heart failure or left ventricular systolic dysfunction. The investigators sought to answer a causal research question (Step 1), and so we proceed to Step 2. Use (or no use) of 𝛽-blockers was determined after discharge without taking into consideration discontinuation or future treatment changes (i.e., intention-to-treat). Considering treatment for whom (Step 3), both ATE and ATT were evaluated. Since survival was the primary outcome, an absolute difference in survival time was chosen as the effect measure (Step 4). While there was no explicit directed acyclic graph provided, the investigators specified a list of confounders.

Robust methodologies were established by consideration of possible sources of biases and addressing them using viable solutions (Steps 6 and 7). Table  1 offers a list of the identified potential biases and their corresponding solutions as implemented. For example, to minimize potential biases including prevalent-user bias and selection bias, the sample was restricted to patients with no previous use of 𝛽-blockers, no contraindication for 𝛽-blockers, and no prescription of loop diuretics. To improve balance across the comparator groups in terms of baseline confounders, i.e., those that could influence both exposure (𝛽-blocker use) and outcome (mortality), propensity score-based inverse probability of treatment weighting (IPTW) was employed. However, we noted that the baseline look-back period to assess measured covariates was not explicitly listed in the paper.

Quality control and sensitivity analysis (Step 8) is described extensively. The overlap of propensity score distributions between comparator groups was tested and confounder balance was assessed. Since observations in the tail-end of the propensity score distribution may violate the positivity assumption (Crump et al. 2009 ), a sensitivity analysis was conducted including only cases within 0.1 to 0.9 of the propensity score distribution. While not mentioned by the authors, the PS tails can be influenced by unmeasured confounders (Sturmer et al. 2021 ), and the findings were robust with and without trimming. An assessment of extreme IPTW weights, while not included, would further help increase confidence in the robustness of the analysis. An instrumental variable approach was employed to assess potential selection bias due to unmeasured confounding, using hospital rates of guideline-indicated prescribing as the instrument. Additionally, potential bias caused by missing data was attenuated through the use of multiple imputation, and separate models were built for complete cases only and imputed/complete cases.

4 Discussion

We have described a conceptual schema for designing observational real-world studies to estimate causal effects. The application of this schema to a previously published study illuminates the methodologic structure of the study, revealing how each structural element is related to a potential bias which it is meant to address. Real-world evidence is increasingly accepted by healthcare stakeholders, including the FDA (Concato and Corrigan-Curay 2022 ; Concato and ElZarrad 2022 ), and its use for comparative effectiveness and safety assessments requires appropriate causal study design; our guide is meant to facilitate this design process and complement existing, more specific, guidance.

Existing guidance for causal inference using observational data includes components that can be clearly mapped onto the schema that we have developed. For example, in 2009 Cox et al. described common sources of bias in observational data and recommended specific strategies to mitigate these biases, corresponding to steps 6–8 of our step-by-step guide (Cox et al. 2009 ). In 2013, the AHRQ emphasized development of the research question, corresponding to steps 1–4 of our guide, with additional chapters on study design, comparator selection, sensitivity analyses, and directed acyclic graphs which correspond to steps 7 and 5, respectively (Velentgas et al. 2013 ). Much of Girman et al.’s manuscript (Girman et al. 2014 ) corresponds with steps 1–4 of our guide, and the matter of equipoise and interpretability specifically correspond to steps 3 and 7–8. The current ENCePP guide on methodological standards in pharmacoepidemiology contains a section on formulating a meaningful research question, corresponding to step 1, and describes strategies to mitigate specific sources of bias, corresponding to steps 6–8 (European Medicines Agency 2023 ). Recent works by the FDA Sentinel Innovation Center (Desai et al. 2024 ) and the Joint Initiative for Causal Inference (Dang et al. 2023 ) provide more advanced exposition of many of the steps in our guide. The target trial framework contains guidance on developing seven components of the study protocol, including eligibility criteria, treatment strategies, assignment procedures, follow-up period, outcome, causal contrast of interest, and analysis plan (Hernán and Robins 2016 ). Our work places the target trial framework into a larger context illustrating its relationship with other important study planning considerations, including the creation of a directed acyclic graph and incorporation of prespecified sensitivity and quantitative bias analyses.

Ultimately, the feasibility of estimating causal effects relies on the capabilities of the available data. Real-world data sources are complex, and the investigator must carefully consider whether the data on hand are sufficient to answer the research question. For example, a study that relies solely on claims data for outcome ascertainment may suffer from outcome misclassification bias (Lanes and Beachler 2023 ). This bias can be addressed through medical record validation for a random subset of patients, followed by quantitative bias analysis (Lanes and Beachler 2023 ). If instead, the investigator wishes to apply a previously published, claims-based algorithm validated in a different database, they must carefully consider the transportability of that algorithm to their own study population. In this way, causal inference from real-world data requires the ability to think creatively and resourcefully about how various data sources and elements can be leveraged, with consideration for the strengths and limitations of each source. The heart of causal inference is in the pairing of humility and creativity: the humility to acknowledge what the data cannot do, and the creativity to address those limitations as best as one can at the time.

4.1 Limitations

As with any attempt to synthesize a broad array of information into a single, simplified schema, there are several limitations to our work. Space and useability constraints necessitated simplification of the complex source material and selections among many available methodologies, and information about the relative importance of each step is not currently included. Additionally, it is important to consider the context of our work. This step-by-step guide emphasizes analytic techniques (e.g., propensity scores) that are used most frequently within our own research environment and may not include less familiar study designs and analytic techniques. However, one strength of the guide is that additional designs and techniques or concepts can easily be incorporated into the existing schema. The benefit of a schema is that new information can be added and is more readily accessed due to its association with previously sorted information (Loveless 2022 ). It is also important to note that causal inference was approached as a broad overarching concept defined by the totality of the research, from start to finish, rather than focusing on a particular analytic technique, however we view this as a strength rather than a limitation.

Finally, the focus of this guide was on the methodologic aspects of study planning. As a result, we did not include steps for drafting or registering the study protocol in a public database or for communicating results. We strongly encourage researchers to register their study protocols and communicate their findings with transparency. A protocol template endorsed by ISPOR and ISPE for studies using real-world data to evaluate treatment effects is available (Wang et al. 2023a ). Additionally, the steps described above are intended to illustrate an order of thinking in the study planning process, and these steps are often iterative. The guide is not intended to reflect the order of study execution; specifically, quality control procedures and sensitivity analyses should also be formulated up-front at the protocol stage.

5 Conclusion

We outlined steps and described key conceptual issues of importance in designing real-world studies to answer causal questions, and created a visually appealing, user-friendly resource to help researchers clearly define and navigate these issues. We hope this guide serves to enhance the quality, and thus the impact, of real-world evidence.

Data availability

No datasets were generated or analysed during the current study.

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Hoffman, S.R., Gangan, N., Chen, X. et al. A step-by-step guide to causal study design using real-world data. Health Serv Outcomes Res Method (2024). https://doi.org/10.1007/s10742-024-00333-6

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Estimated incidence per 100 000 person-years at age 60 years is shown by birth year (1908-1983). All curves are on the log 10 scale. These and additional curves are shown in eFigure 11 in Supplement 1 . Tick marks on the x-axis indicate start years for consecutive social generations: 1928-1945, Silent Generation; 1946-1964, Baby Boomers; 1965-1980, Generation X. Shaded areas indicate 95% CIs.

Estimated incidence per 100 000 person-years at age 60 years is shown by birth year (1908-1983). All curves are on the log 10 scale. These and additional curves are shown in eFigure 13 in Supplement 1 . Tick marks on the x-axis indicate start years for consecutive social generations: 1928-1945, Silent Generation; 1946-1964, Baby Boomers; 1965-1980, Generation X. Shaded areas indicate 95% CIs.

Gen X indicates Generation X. Shaded areas indicate 95% CIs.

Fitted cohort pattern curves by cancer site are adjusted for race and ethnicity as described in the Methods, plotted on a natural log scale, and sorted from largest (top) to smallest (bottom). IRR indicates incidence rate ratio; NHL, non-Hodgkins lymphoma.

Arrow plots indicate magnitude and direction of change from older to younger generations. Corresponding percentage changes and 95% CIs are also graphed in eFigure 19 in Supplement 1 .

eMethods. Statistical Methods

eTable 1. Organ-Specific Site Code Used in the Classification of Cancer Sites Using SEER*Stat 8.4.0

eTable 2. Age-Standardized Incidence Rates and Cancer Cases by Sex, Race and Ethnicity, United States, 35-84 Years Old, 1992-2018

eFigure 1. Observed Rates in Females

eFigure 2. APC Fitted Values in Females

eFigure 3. Observed Rates in Males

eFigure 4. APC Fitted Values in Males

eFigure 5. Lack of Fit (LOF) in Females

eFigure 6. Lack of Fit (LOF) in Males

eFigure 7. Higher-Order Deviations vs Lack of Fit (LOF) in Females

eFigure 8. Higher-Order Deviations vs Lack of Fit (LOF) in Males

eFigure 9. Local Drifts in Females

eFigure 10. Local Drifts in Males

eFigure 11. Fitted Cohort Patterns (FCPs) by Cancer Site, Race, and Ethnicity: Females

eFigure 12. Estimated Annual Percentage Change (EAPC) of the Fitted Cohort Pattern (FCP): Females

eFigure 13. Fitted Cohort Patterns (FCPs) by Cancer Site, Race, and Ethnicity: Males

eFigure 14. Estimated Annual Percentage Change (EAPC) of the Fitted Cohort Pattern (FCP): Males

eFigure 15. Average Incidence at Age 60: Generation X vs Baby Boomers

eFigure 16. Average Incidence at Age 60: Baby Boomers vs the Silent Generation

eFigure 17. Average Incidence at Age 60: Silent vs Greatest Generations

eFigure 18. Site-Adjusted Cancer Incidence Rate Ratios (IRRs) for Non-Hispanic Black, Hispanic, and Asian or Pacific Islander vs Non-Hispanic White by Sex and Social Generation

eFigure 19. Percent Changes in Incidence of Leading Cancers at Age 60 Years per 100 000 Person-Years in Successive Generations

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Rosenberg PS , Miranda-Filho A. Cancer Incidence Trends in Successive Social Generations in the US. JAMA Netw Open. 2024;7(6):e2415731. doi:10.1001/jamanetworkopen.2024.15731

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Cancer Incidence Trends in Successive Social Generations in the US

• 1 Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, Rockville, Maryland

Question   Is cancer incidence in successive social generations in the US slowing or growing?

Findings   In this cohort study of 3.8 million patients with cancer ascertained by the Surveillance, Epidemiology, and End Results Program, members of Generation X born between 1965 and 1980 have been experiencing larger per-capita increases in the incidence of leading cancers combined than any prior generation born between 1908 and 1964.

Meaning   These findings suggest that based on current trajectories, cancer incidence in the US might remain high for decades.

Importance   The incidence of some cancers in the US is increasing in younger age groups, but underlying trends in cancer patterns by birth year remain unclear.

Objective   To estimate cancer incidence trends in successive social generations.

Design, Setting, and Participants   In this cohort study, incident invasive cancers were ascertained from the Surveillance, Epidemiology, and End Results (SEER) program’s 13-registry database (November 2020 submission, accessed August 14, 2023). Invasive cancers diagnosed at ages 35 to 84 years during 1992 to 2018 within 152 strata were defined by cancer site, sex, and race and ethnicity.

Exposure   Invasive cancer.

Main Outcome and Measures   Stratum-specific semiparametric age-period-cohort (SAGE) models were fitted and incidence per 100 000 person-years at the reference age of 60 years was calculated for single-year birth cohorts from 1908 through 1983 (fitted cohort patterns [FCPs]). The FCPs and FCP incidence rate ratios (IRRs) were compared by site for Generation X (born between 1965 and 1980) and Baby Boomers (born between 1946 and 1964).

Results   A total of 3.8 million individuals with invasive cancer (51.0% male; 8.6% Asian or Pacific Islander, 9.5% Hispanic, 10.4% non-Hispanic Black, and 71.5% non-Hispanic White) were included in the analysis. In Generation X vs Baby Boomers, FCP IRRs among women increased significantly for thyroid (2.76; 95% CI, 2.41-3.15), kidney (1.99; 95% CI, 1.70-2.32), rectal (1.84; 95% CI, 1.52-2.22), corpus uterine (1.75; 95% CI, 1.40-2.18), colon (1.56; 95% CI, 1.27-1.92), and pancreatic (1.39; 95% CI, 1.07-1.80) cancers; non-Hodgkins lymphoma (1.40; 95% CI, 1.08-1.82); and leukemia (1.27; 95% CI, 1.03-1.58). Among men, IRRs increased for thyroid (2.16; 95% CI, 1.87-2.50), kidney (2.14; 95% CI, 1.86-2.46), rectal (1.80; 95% CI, 1.52-2.12), colon (1.60; 95% CI, 1.32-1.94), and prostate (1.25; 95% CI, 1.03-1.52) cancers and leukemia (1.34; 95% CI, 1.08-1.66). Lung (IRR, 0.60; 95% CI, 0.50-0.72) and cervical (IRR, 0.71; 95% CI, 0.57-0.89) cancer incidence decreased among women, and lung (IRR, 0.51; 95% CI, 0.43-0.60), liver (IRR, 0.76; 95% CI, 0.63-0.91), and gallbladder (IRR, 0.85; 95% CI, 0.72-1.00) cancer and non-Hodgkins lymphoma (IRR, 0.75; 95% CI, 0.61-0.93) incidence decreased among men. For all cancers combined, FCPs were higher in Generation X than for Baby Boomers because gaining cancers numerically overtook falling cancers in all groups except Asian or Pacific Islander men.

Conclusions and Relevance   In this model-based cohort analysis of incident invasive cancer in the general population, decreases in lung and cervical cancers in Generation X may be offset by gains at other sites. Generation X may be experiencing larger per-capita increases in the incidence of leading cancers than any prior generation born in 1908 through 1964. On current trajectories, cancer incidence could remain high for decades.

Is cancer incidence in the US—ie, the number of newly diagnosed cases per capita per year—slowing or growing? Notably, while the incidence of certain cancers is declining, 1 others are increasing in younger age groups (aged <50 years). 2 These temporal patterns encompass a diversity of neoplasms and vary over time by demographic factors, including age, sex, and race and ethnicity. A more fundamental question is: are we collectively experiencing lower cancer incidence as we age than the generation of our parents?

Analyses that focus on birth years and social generations can provide insights into cancer prevention and health care accessibility and highlight persistent demographic and socioeconomic inequalities that span generations. Nevertheless, tracking the history of cancer in the US is challenging because cancers evolve over the lifetimes of individuals, 3 yet the observational period of contemporary cancer registries spans at most a few decades. 4 Hence, we must reconstruct the longitudinal history of cancer in the population from a time-limited series of cross-sectional observations. To understand the overarching trends, it is imperative to assemble these snapshots into a cohesive longitudinal narrative for a variety of cancer types in diverse demographic groups.

With the use of new statistical methods, 5 , 6 we can now obtain single-year reconstructions of cancer incidence by age, period, and birth cohort with unprecedented precision. Our goal is to model the shifting landscape of cancer from one birth year to the next for leading cancers in men and women by race and ethnicity. From this ensemble, we hope to glean insights into the overall trajectory of cancer in the US from the Greatest Generation (born from 1908 through 1927) through Generation X (born from 1965 through 1980).

Our cohort study was based on publicly available, deidentified data and, therefore, the National Cancer Institute institutional review board determined it exempt from review and informed consent. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We collected case and person-years at risk data from the Surveillance, Epidemiology, and End Results (SEER) program’s 13-registery database using SEER*Stat, version 8.4.0. Data ascertained for the November 2020 submission were retrieved on August 14, 2023 (eTable 1 in Supplement 1 ). Using these data, we assembled 152 rate matrix Lexis diagrams 7 covering 50 single years of age (35-84 years) and 27 calendar years (1992-2018), spanning 76 single-year birth cohorts (1908-1983) for 21 leading invasive cancers in women and men within 4 racial and ethnic categories: Asian or Pacific Islander, Hispanic, non-Hispanic Black, and non-Hispanic White. The definitions of race and ethnicity follow the criteria established by the SEER program. 8 In brief, the SEER program uses an algorithm to recode detailed race and origin variables in the SEER incidence data. The 21 leading invasive cancers were esophageal, stomach, gallbladder, liver, pancreatic, colon, rectal, kidney, bladder, leukemia, non-Hodgkin lymphoma (NHL), myeloma, brain and central nervous system (hereafter, brain), thyroid, lung, melanoma of skin (hereafter, melanoma), breast, ovarian, corpus uterine (hereafter, corpus), cervix uterine (hereafter, cervical), and prostate (eTable 2 in Supplement 1 ).

We analyzed each Lexis diagram using semiparametric age-period-cohort (SAGE) analysis. 6 The basic idea is to denoise the observed Lexis diagrams upfront by using a contemporary nonparametric procedure, 5 then fit the new age-period-cohort (APC) model to the smoothed Lexis diagrams. 9 The new APC model contains parameters that describe how expected rates vary as a function of birth cohort, accounting for age and calendar period effects. We used the model parameters to estimate the fitted cohort pattern (FCP). The FCP estimates the absolute incidence of a cancer at an arbitrary reference age (here, 60 years) for each birth year in the Lexis diagram. 9 Additional details are provided in the eMethods in Supplement 1 .

To further characterize FCP features, we fitted Joinpoint models 10 to the FCPs, allowing up to 5 segments, each with 10 or more birth years. We also computed mean FCPs and corresponding variances for the following social generations: Greatest Generation (1908-1927), Silent Generation (1928-1945), Baby Boomers (1946-1964), and Generation X (1965-1980). We had too few data points to produce estimates for Millennials (1981-1996).

To compare and contrast mean FCP values across generations, we fitted log-linear models to the generational differences in site-specific FCPs among men and women separately for the Silent vs Greatest Generations, Baby Boomers vs the Silent Generation, and Generation X vs Baby Boomers. We adjusted for race and ethnicity with non-Hispanic White as the reference group and weighted the data inversely with the estimated variance of each difference.

The SEER registries do not record the parents’ birth year for each cancer case. However, on average, the parents of Generation X are Baby Boomers and members of the Silent Generation, and the parents of Baby Boomers are members of the Silent and Greatest Generations. To see this assumption, subtract 20 through 29 years (mean maternal age at birth during our study period 11 ) from the end points of each generation, yielding 1917-1944 for Baby Boomers, which straddles the Greatest and Silent Generations (eg, 1917-1927 and 1928-1944, respectively), and 1936-1960 for Generation X, which straddles the Silent Generation and Baby Boomers (eg, 1936-1945 and 1946-1960, respectively). Similarly, the proxy parents of the Millennial generation (1981-1996) straddle the Baby Boomers and Generation X (eg, 1952-1964 and 1965-1976, respectively). In our study, we make no adjustments for multiple testing.

In total, we analyzed 3.8 million cases of incident cancer occurring over 521 million person-years (eTable 2 in Supplement 1 ). Overall, 51.0% of individuals were male (compared with 49.0% female) and 71.5% were non-Hispanic White (compared with 8.6% Asian or Pacific Islander, 9.5% Hispanic, and 10.4% non-Hispanic Black).

The SAGE analysis produced optimally smoothed single-year estimates of incidence for 80 female strata (observed Lexis diagrams, eFigure 1 in Supplement 1 ; smoothed APC fitted rates, eFigure 2 in Supplement 1 ) and 72 male strata (observed, eFigure 3 in Supplement 1 ; smoothed, eFigure 4 in Supplement 1 ). For the majority, lack of fit (LOF) (females, eFigure 5 in Supplement 1 ; males, eFigure 6 in Supplement 1 ) was negligible or small relative to the corresponding annual fluctuations (females, eFigure 7 in Supplement 1 ; males, eFigure 8 in Supplement 1 ). The most prominent LOF was observed for liver, rectal, and brain cancers and melanoma in females and liver, brain, thyroid, and prostate cancers in males. Time trends by age (local drifts) were significant in all strata (females, eFigure 9 in Supplement 1 ; males, eFigure 10 in Supplement 1 ). As shown previously, 6 LOF had little effect on the local drifts for the Lexis diagrams included in our study. Hence, the model fits appear adequate. 6

eFigure 11 in Supplement 1 presents FCPs by cancer site for women by race and ethnicity. Six of these FCPs, including pancreatic, colon, kidney, thyroid, lung, and cervical, are also shown in Figure 1 A through F, respectively. The statistical efficiency of the SAGE analysis yielded partially or fully nonoverlapping curves within most FCPs; the conclusions below were obtain by visual inspection.

Differences by race and ethnicity strongly depended on birth cohort. There were marked declines in cervical FCPs across all racial and ethnic groups. Incidence rates were highest among the Greatest Generation in non-Hispanic Black women for esophageal, pancreatic, and colon cancers and myeloma; in Hispanic women for gallbladder cancer; in Asian or Pacific Islander women for stomach and liver cancers; and in non-Hispanic White women for bladder, breast, ovarian, and corpus cancers and leukemia, NHL, and melanoma. Patterns by race and ethnicity were similar for the Silent Generation. Patterns changed with the Baby Boomers. Notable changes included convergence of esophageal cancers in non-Hispanic Black and non-Hispanic White women; steep declines for stomach cancer in Asian or Pacific Islander women; and steep increases in corpus cancer for Asian or Pacific Islander, Hispanic, and non-Hispanic Black women. In Generation X women, the FCPs consistently increased for liver, colon, rectal, kidney, thyroid, and corpus cancers ( Figure 1 ; eFigure 11 in Supplement 1 ). Estimates of the corresponding estimated annual percentage changes (EAPCs) of the FCPs obtained by Joinpoint analysis are shown in eFigure 12 in Supplement 1 .

eFigure 13 in Supplement 1 presents FCPs by cancer site for men. Six of these FCPs, including pancreatic, colon, kidney, thyroid, lung, and prostate, are also shown in Figure 2 A through F, respectively. For thyroid cancer, increases by cohort were slower in Asian or Pacific Islander, Hispanic, and non-Hispanic Black men compared with women, and peak incidence for lung cancer occurred earlier in men compared with women. The FCPs for prostate cancer were parallel across racial and ethnic groups, being highest in non-Hispanic Black and lowest in Asian or Pacific Islander men. In Generation X men, the FCPs were consistently increasing for colon, rectal, kidney, and thyroid cancers ( Figure 2 ; eFigure 13 in Supplement 1 ). eFigure 14 in Supplement 1 provides the corresponding estimated EAPCs.

Figure 3 presents the sum of the cancer site–specific FCPs (20 sites in women and 18 in men) by sex and race and ethnicity. Among men, incidence at age 60 years declined through the Greatest and Silent Generations in all 4 racial and ethnic groups. Subsequently, incidence increased for the Baby Boomers. Except for Asian or Pacific Islander men, incidence continued to increase in Generation X.

In contrast, among women, incidence was comparatively stable among members of the Greatest and Silent Generations and then increased beginning with the Baby Boomers for non-Hispanic White individuals ( Figure 3 A), then the Silent Generation for Hispanic and Asian or Pacific Islander ( Figure 3 C and D) and Generation X for non-Hispanic Black individuals ( Figure 3 B). Within each racial and ethnic group, the large male excess in the Greatest Generation diminished in subsequent generations. By Generation X, Hispanic and non-Hispanic White women had a similar incidence to their male counterparts ( Figure 3 A and C), and Asian or Pacific Islander women had a higher incidence compared with their male counterparts ( Figure 3 D). In the non-Hispanic Black group, the male excess persisted.

eFigure 15 in Supplement 1 presents arrow plots of mean FCP values by cancer site for Generation X vs Baby Boomers stratified by sex and race and ethnicity. Within each stratum, the FCP for Generation X was higher than the corresponding FCP for Baby Boomers at some sites and lower at others. Similar heterogeneity was observed in arrow plots of Baby Boomers vs the Silent Generation (eFigure 16 in Supplement 1 ) and the Silent vs Greatest Generations (eFigure 17 in Supplement 1 ).

To synthesize these data, we estimated FCP incidence rate ratios (IRRs) by fitting log-linear models to the estimated FCPs in eFigures 15 to 17 in Supplement 1 . For example, for Generation X vs Baby Boomer women, we synthesized the 80 arrows shown in panels eFigure 15A to D in Supplement 1 . Each arrow, which is indexed by site and race and ethnicity, contributed 1 difference or IRR to the modeled data. The estimated variances of the differences were used as weights.

Figure 4 presents forest plots of the site-specific IRRs between successive social generations adjusted for race and ethnicity in women and men. Point estimates are shown for non-Hispanic White individuals. Estimates for other racial and ethnic groups are similar (eFigure 18 in Supplement 1 ). Site-specific IRRs varied markedly by generation except for the brain.

The incidence of many cancers increased significantly in members of Generation X vs Baby Boomers ( Figure 4 ). Among women, increases were observed for the following cancers: thyroid (IRR, 2.76, 95% CI, 2.41-3.15), kidney (IRR, 1.99; 95% CI, 1.70-2.32), rectal (IRR, 1.84; 95% CI, 1.52-2.22), corpus (IRR, 1.75; 95% CI, 1.40-2.18), colon (IRR, 1.56; 95% CI, 1.27-1.92), NHL (IRR, 1.40, 95% CI 1.08-1.82), pancreatic (IRR, 1.39; 95% CI, 1.07-1.80), and leukemia (IRR, 1.27; 95% CI, 1.03-1.58). Among men, increases were observed for the following cancers: thyroid (IRR, 2.16; 95% CI, 1.87-2.50), kidney (IRR, 2.14; 95% CI, 1.86-2.46), rectal (IRR, 1.80; 95% CI, 1.52-2.12), colon (IRR, 1.60; 95% CI, 1.32-1.94), leukemia (IRR, 1.34; 95% CI, 1.08-1.66), and prostate (IRR, 1.25; 95% CI, 1.03-1.52). Other cancers decreased, including lung (IRR, 0.60; 95% CI, 0.50-0.72) and cervical (IRR, 0.71; 95% CI, 0.57-0.89) among women and lung (IRR, 0.51; 95% CI, 0.43-0.60), NHL (IRR, 0.75; 95% CI, 0.61-0.93), liver (IRR, 0.76; 95% CI, 0.63-0.91), and gallbladder (IRR, 0.85; 95% CI, 0.72-1.00) among men.

Figure 5 summarizes incidence at age 60 years for leading cancers combined, averaged over birth years within successive generations. eFigure 19 in Supplement 1 plots the corresponding percentage changes. Comparing the Silent vs Greatest Generations ( Figure 5 A), combined incidence decreased significantly in both sexes and all 4 racial and ethnic groups. The decreases were greater for men compared with women. For example, incidence decreased by 41.0% (95% CI, −43.9% to −38.1%) in non-Hispanic White men vs 5.6% (95% CI, −6.6% to −4.6%) in non-Hispanic White women. In contrast, comparing Baby Boomers with the Silent Generation ( Figure 5 B), combined incidence had a mixed pattern, decreasing in some groups, eg, by 5.4% (95% CI, −6.1% to −4.8%) and 10.6% (95% CI, −11.6% to −9.5%), respectively, in non-Hispanic White women and men, but increasing in others, eg, by 13.0% (95% CI, 11.1%-14.9%) among Hispanic women.

Comparing Generation X with Baby Boomers ( Figure 5 C), combined incidence increased in all groups except for Asian or Pacific Islander men. The increases ranged from 5.5% (95% CI, 1.4%-9.7%) in non-Hispanic Black women to 28.0% (95% CI, 23.5%-32.5%) in Hispanic women. The increases ranged from 10.4% (95% CI, 0.7%-20.1%) in Hispanic men to 13.7% (95% CI, 2.4%-24.9%) in non-Hispanic White men. The increase among non-Hispanic Black men was 13.7% (95% CI, −1.9% to 29.2%). Incidence decreased by 8.2% (95% CI, −15.6% to −0.9%) among Asian or Pacific Islander men. Except for Asian or Pacific Islander men, these increases were larger than any observed in prior social generations (eg, point estimates in Figure 5 C are larger than corresponding point estimates in Figure 5 A and B). In absolute terms, non-Hispanic Black men in Generation X had the highest combined incidence at 1561 cases per 100 000 person-years (95% CI, 1493-1629 cases per 100 000 person-years), while Asian or Pacific Islander men had the lowest at 519 cases per 100 000 person-years (95% CI, 474-563 cases per 100 000 person-years).

Male Baby Boomers had a lower combined incidence than their proxy parents (Greatest and Silent Generation birth cohorts from 1917 to 1944) ( Figure 5 D), with the greatest reduction of 28.8% (95% CI, −31.0 to −26.7%) in non-Hispanic Black men. The decreases among men were statistically significant in the other 3 racial and ethnic groups (Asian or Pacific Islander, −16.8% [95% CI, −19.5% to −14.2%]; Hispanic, −11.3% [95% CI, −14.0% to −8.6%]; non-Hispanic White, −20.8% [95% CI, −21.9% to −19.7%]). Female Baby Boomers had a mixed pattern: non-Hispanic White women, 7.0% lower (95% CI, −7.7% to −6.3%); essentially unchanged in non-Hispanic Black women, 0.7% higher (95% CI, −1.3% to 2.7%); Hispanic women, 12.1% higher (95% CI, 9.8%-14.4%); Asian or Pacific Islander women, 7.7% higher (95% CI, 5.4%-10.0%).

In contrast, members of Generation X had a higher combined incidence than their proxy parents (Silent Generation and Baby Boomers birth cohorts from 1936 to 1960) ( Figure 5 E) in all demographic groups except for Asian or Pacific Islander men. These increases were statistically significant in all 4 racial and ethnic groups among women (Asian or Pacific Islander, 20.1% [95% CI, 15.7%-24.4%]; Hispanic, 34.9% [95% CI, 29.8%-40.0%]; non-Hispanic Black, 6.1% [95% CI, 1.7%-10.5%]; non-Hispanic White, 15.1% [95% CI, 13.1%-17.2%]) and in Hispanic (14.1%; 95% CI, 3.6%-24.5%) and non-Hispanic White (11.9%; 95% CI, 0.7%-23.0%) men. Similarly, the proxy parents of the Millennials (Baby Boomer and Generation X birth cohorts from 1952 to 1976) also had higher cancer incidence than the proxy parents of Generation X in all demographic groups except Asian or Pacific Islander males ( Figure 5 F). The increases were statistically significant in Asian or Pacific Islander (11.1%; 95% CI, 8.9%-13.2%), Hispanic (17.8%; 95% CI, 15.4%-20.2%), and non-Hispanic White (4.5%; 95% CI, 3.6%-5.5%) women and in Hispanic (9.0%; 95% CI, 4.1%-14.0%) and non-Hispanic White (4.7%; 95% CI, 0.4%-9.0%) men.

In this cohort study, we asked whether we, collectively, are experiencing lower cancer incidence as we age than our parents. Using SAGE analysis, we were able to reconstruct the cancer experience of social generations from the Greatest Generation through Generation X and use selected birth years from the 2 preceding social generations as a proxy for the corresponding parental generation. To arrive at an overall conclusion, we used a simple summary measure: the incidence of leading cancers combined (20 sites in women and 18 sites in men).

For Baby Boomers (1946-1964) vs their proxy parents (1917-1944), the answer was yes for men (ie, progress) and mixed for women ( Figure 5 D). For Generation X (1965-1980) vs their proxy parents (1936-1960), the answer was no in all groups except for Asian or Pacific Islander males ( Figure 5 E).

The increases in cancer incidence in members of Generation X vs their proxy parents were substantial, especially among Hispanic women (a 34.9% increase) and men (a 14.1% increase). In contrast, the corresponding increases among non-Hispanic White women and men were 15.1% and 11.9%, respectively. We obtained similar results when we compared the Generation X to Baby Boomer social generations ( Figure 5 C). Furthermore, between the Greatest Generation and Generation X, the historical male cancer excess narrowed (non-Hispanic Black and non-Hispanic White men), declined to parity (Hispanic men), or reversed (Asian or Pacific Islander men) ( Figure 3 ).

Our conclusions are more concerning than previously reported increases in cancer incidence in younger age groups. 2 , 12 Those results, based on local drifts, describe the slope of the FCP curve in consecutive blocks of P birth years, where P is the total number of calendar years in the analysis (eg, P  = 19 in Sung et al 2 ; P  = 27 in our analyses). 9 For this reason, local drifts provide lagging and conservative indicators of changes from one birth year to the next. Furthermore, using SAGE analysis, we were able to estimate absolute FCP values for single-year birth cohorts and consecutive changes in incidence per birth year vs the 10-year overlapping cohort relative risks reported by Sung et al. 2

The substantial increases we identified in Generation X vs both the Baby Boomers and their proxy parents surprised us. Numerous preventable causes of cancer have been identified. 13 Cancer control initiatives have led to substantial declines in tobacco consumption. 14 Screening is well accepted for precancerous lesions of the colon, rectum, cervix, uterus, and breast. 15 However, other suspected carcinogenic exposures are increasing. 16 - 18

Unfortunately, as shown in our detailed comparative analysis of Generation X vs Baby Boomers, gaining cancers have numerically overtaken falling cancers. Among Generation X women, statistically significant declines in lung and cervical cancers have been overtaken by significant increases in thyroid, kidney, rectal, corpus, colon, pancreatic, and ovarian cancer and NHL and leukemia ( Figure 4 C). Among Generation X men, declines in lung, liver, and gallbladder cancers and NHL have been overtaken by gains in thyroid, kidney, rectal, colon, and prostate cancers and leukemia ( Figure 4 F).

Some portion of these increases can be attributed to rising obesity rates 19 and increasingly sedentary lifestyles. 20 - 23 Another portion might be explained by changes in cancer registry policies and International Statistical Classification of Diseases and Related Health Problems, 10th Revision (World Health Organization) classifications, 24 leading to inclusion of relatively indolent lesions in more recent periods that might not have been diagnosed as cancer in earlier periods. Furthermore, radiologic diagnoses have become more common following widespread deployment of sophisticated medical imaging technologies, 25 especially for thyroid 26 , 27 and kidney 28 , 29 cancers. We chose not to exclude any leading cancer site from our summaries because our granular estimates are freely available (eFigures 15-17 in Supplement 1 ).

Our results beg the question of what the cancer experience may be like among the 72 million Millennials (1981-1996) when they enter their 40s, 50s, and 60s. On one hand, our analysis shows that the proxy parents of the Millennials are experiencing as much or more cancer than the proxy parents of Generation X ( Figure 5 F). This increase is concerning because of shared cancer-predisposing lifestyle factors and exposures. On the other hand, thanks to the global investment in cancer research, there are tremendous opportunities to prospectively reduce the Millennials’ future cancer burden.

The American Cancer Society, 30 Centers for Disease Control and Prevention, 31 and World Health Organization 13 advocate a series of preventive actions to diminish cancer risks. These include reducing tobacco and alcohol use, increasing physical activity, improving dietary habits, and promoting breastfeeding. These recommendations can also reduce heart disease 32 and cognitive decline. 33

Unfortunately, universal implementation of these recommendations in the US is a work in progress. The Black-to-White cancer mortality gap narrowed following passage of the Patient Protection and Affordable Care Act. 34 , 35 However, income inequality, 36 underinsurance, 37 - 39 food swamps and deserts, 40 , 41 deficits in the built environment, 42 and other factors make it difficult for everyone to eat healthy and stay active. 30 Taken together, these findings indicate that for many people in the US, a healthy lifestyle remains, to various degrees, an unattainable privilege rather than a fundamental right. The extent to which lifestyle disparities explain rising generational cancer rates in our data and falling life expectancies in other studies 43 is unclear and, in our view, merits further study.

Our study has 2 major limitations. First, the numbers of less common cancers in the SEER 13-registry database among Asian or Pacific Islander, Hispanic, and non-Hispanic Black men and women are limited, especially for esophageal and gallbladder cancers and melanoma. Second, our conclusions derive from modeling. Even so, we believe that our detailed analysis of 3.8 million individuals with invasive cancers in 152 distinct strata mitigated many potential biases. Furthermore, at most cancer sites, the birth cohort effects were substantial, and the LOF was relatively small. For this reason, we believe that it is appropriate to draw conclusions from our FCP estimates. However, it is important to appreciate that the FCP incorporates backward projection for older cohorts and forward projection for younger cohorts; in other words, it is very much a model-based quantity.

The models in this cohort study suggest that Generation X is experiencing larger per-capita increases in the incidence of leading cancers combined than any prior generation born from 1908 through 1964. In addition, the rate of leading cancers appears to be as high or higher in the proxy parents of the Millennials than the proxy parents of Generation X. Therefore, if the Millennials’ cancer experience follows the estimated trajectory of their proxy parents, cancer incidence in the US could remain unacceptably high for decades to come.

Accepted for Publication: April 8, 2024.

Published: June 10, 2024. doi:10.1001/jamanetworkopen.2024.15731

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Rosenberg PS et al. JAMA Network Open .

Corresponding Author: Philip S. Rosenberg, PhD, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NCI Shady Grove, Room 7E-130, 9609 Medical Center Dr, Bethesda, MD 20892 ( [email protected] ).

Author Contributions: Drs Rosenberg and Miranda-Filho had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: All authors.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: All authors.

Administrative, technical, or material support: Miranda-Filho.

Supervision: Rosenberg.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute (Drs Rosenberg and Miranda-Filho) and through an appointment to the National Cancer Institute Oak Ridge Institute for Science and Education Research Participation Program under contract DE-SC0014554 from the Department of Energy (Dr Miranda-Filho).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation of the manuscript. The Division of Cancer Epidemiology and Genetics, National Cancer Institute, reviewed and approved the manuscript and approved the decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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Peer-reviewed

Research Article

Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Child and Adolescent Mental Health, Department of Brain Sciences, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

Roles Conceptualization, Supervision, Validation, Writing – review & editing

* E-mail: [email protected]

Affiliation Behavioural Brain Sciences Unit, Population Policy Practice Programme, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom

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• Published: June 4, 2024
• https://doi.org/10.1371/journal.pmen.0000022
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Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent’s behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10–19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

Citation: Chang MLY, Lee IO (2024) Functional connectivity changes in the brain of adolescents with internet addiction: A systematic literature review of imaging studies. PLOS Ment Health 1(1): e0000022. https://doi.org/10.1371/journal.pmen.0000022

Editor: Kizito Omona, Uganda Martyrs University, UGANDA

Received: December 29, 2023; Accepted: March 18, 2024; Published: June 4, 2024

Copyright: © 2024 Chang, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The behavioural addiction brought on by excessive internet use has become a rising source of concern [ 1 ] since the last decade. According to clinical studies, individuals with Internet Addiction (IA) or Internet Gaming Disorder (IGD) may have a range of biopsychosocial effects and is classified as an impulse-control disorder owing to its resemblance to pathological gambling and substance addiction [ 2 , 3 ]. IA has been defined by researchers as a person’s inability to resist the urge to use the internet, which has negative effects on their psychological well-being as well as their social, academic, and professional lives [ 4 ]. The symptoms can have serious physical and interpersonal repercussions and are linked to mood modification, salience, tolerance, impulsivity, and conflict [ 5 ]. In severe circumstances, people may experience severe pain in their bodies or health issues like carpal tunnel syndrome, dry eyes, irregular eating and disrupted sleep [ 6 ]. Additionally, IA is significantly linked to comorbidities with other psychiatric disorders [ 7 ].

Stevens et al (2021) reviewed 53 studies including 17 countries and reported the global prevalence of IA was 3.05% [ 8 ]. Asian countries had a higher prevalence (5.1%) than European countries (2.7%) [ 8 ]. Strikingly, adolescents and young adults had a global IGD prevalence rate of 9.9% which matches previous literature that reported historically higher prevalence among adolescent populations compared to adults [ 8 , 9 ]. Over 80% of adolescent population in the UK, the USA, and Asia have direct access to the internet [ 10 ]. Children and adolescents frequently spend more time on media (possibly 7 hours and 22 minutes per day) than at school or sleeping [ 11 ]. Developing nations have also shown a sharp rise in teenage internet usage despite having lower internet penetration rates [ 10 ]. Concerns regarding the possible harms that overt internet use could do to adolescents and their development have arisen because of this surge, especially the significant impacts by the COVID-19 pandemic [ 12 ]. The growing prevalence and neurocognitive consequences of IA among adolescents makes this population a vital area of study [ 13 ].

Adolescence is a crucial developmental stage during which people go through significant changes in their biology, cognition, and personalities [ 14 ]. Adolescents’ emotional-behavioural functioning is hyperactivated, which creates risk of psychopathological vulnerability [ 15 ]. In accordance with clinical study results [ 16 ], this emotional hyperactivity is supported by a high level of neuronal plasticity. This plasticity enables teenagers to adapt to the numerous physical and emotional changes that occur during puberty as well as develop communication techniques and gain independence [ 16 ]. However, the strong neuronal plasticity is also associated with risk-taking and sensation seeking [ 17 ] which may lead to IA.

Despite the fact that the precise neuronal mechanisms underlying IA are still largely unclear, functional magnetic resonance imaging (fMRI) method has been used by scientists as an important framework to examine the neuropathological changes occurring in IA, particularly in the form of functional connectivity (FC) [ 18 ]. fMRI research study has shown that IA alters both the functional and structural makeup of the brain [ 3 ].

We hypothesise that IA has widespread neurological alteration effects rather than being limited to a few specific brain regions. Further hypothesis holds that according to these alterations of FC between the brain regions or certain neural networks, adolescents with IA would experience behavioural changes. An investigation of these domains could be useful for creating better procedures and standards as well as minimising the negative effects of overt internet use. This literature review aims to summarise and analyse the evidence of various imaging studies that have investigated the effects of IA on the FC in adolescents. This will be addressed through two research questions:

• How does internet addiction affect the functional connectivity in the adolescent brain?
• How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The review protocol was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (see S1 Checklist ).

Search strategy and selection process

A systematic search was conducted up until April 2023 from two sources of database, PubMed and PsycINFO, using a range of terms relevant to the title and research questions (see full list of search terms in S1 Appendix ). All the searched articles can be accessed in the S1 Data . The eligible articles were selected according to the inclusion and exclusion criteria. Inclusion criteria used for the present review were: (i) participants in the studies with clinical diagnosis of IA; (ii) participants between the ages of 10 and 19; (iii) imaging research investigations; (iv) works published between January 2013 and April 2023; (v) written in English language; (vi) peer-reviewed papers and (vii) full text. The numbers of articles excluded due to not meeting the inclusion criteria are shown in Fig 1 . Each study’s title and abstract were screened for eligibility.

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https://doi.org/10.1371/journal.pmen.0000022.g001

Quality appraisal

Full texts of all potentially relevant studies were then retrieved and further appraised for eligibility. Furthermore, articles were critically appraised based on the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) framework to evaluate the individual study for both quality and bias. The subsequent quality levels were then appraised to each article and listed as either low, moderate, or high.

Data collection process

Data that satisfied the inclusion requirements was entered into an excel sheet for data extraction and further selection. An article’s author, publication year, country, age range, participant sample size, sex, area of interest, measures, outcome and article quality were all included in the data extraction spreadsheet. Studies looking at FC, for instance, were grouped, while studies looking at FC in specific area were further divided into sub-groups.

Data synthesis and analysis

Articles were classified according to their location in the brain as well as the network or pathway they were a part of to create a coherent narrative between the selected studies. Conclusions concerning various research trends relevant to particular groupings were drawn from these groupings and subgroupings. To maintain the offered information in a prominent manner, these assertions were entered into the data extraction excel spreadsheet.

With the search performed on the selected databases, 238 articles in total were identified (see Fig 1 ). 15 duplicated articles were eliminated, and another 6 items were removed for various other reasons. Title and abstract screening eliminated 184 articles because they were not in English (number of article, n, = 7), did not include imaging components (n = 47), had adult participants (n = 53), did not have a clinical diagnosis of IA (n = 19), did not address FC in the brain (n = 20), and were published outside the desired timeframe (n = 38). A further 21 papers were eliminated for failing to meet inclusion requirements after the remaining 33 articles underwent full-text eligibility screening. A total of 12 papers were deemed eligible for this review analysis.

Characteristics of the included studies, as depicted in the data extraction sheet in Table 1 provide information of the author(s), publication year, sample size, study location, age range, gender, area of interest, outcome, measures used and quality appraisal. Most of the studies in this review utilised resting state functional magnetic resonance imaging techniques (n = 7), with several studies demonstrating task-based fMRI procedures (n = 3), and the remaining studies utilising whole-brain imaging measures (n = 2). The studies were all conducted in Asiatic countries, specifically coming from China (8), Korea (3), and Indonesia (1). Sample sizes ranged from 12 to 31 participants with most of the imaging studies having comparable sample sizes. Majority of the studies included a mix of male and female participants (n = 8) with several studies having a male only participant pool (n = 3). All except one of the mixed gender studies had a majority male participant pool. One study did not disclose their data on the gender demographics of their experiment. Study years ranged from 2013–2022, with 2 studies in 2013, 3 studies in 2014, 3 studies in 2015, 1 study in 2017, 1 study in 2020, 1 study in 2021, and 1 study in 2022.

https://doi.org/10.1371/journal.pmen.0000022.t001

(1) How does internet addiction affect the functional connectivity in the adolescent brain?

The included studies were organised according to the brain region or network that they were observing. The specific networks affected by IA were the default mode network, executive control system, salience network and reward pathway. These networks are vital components of adolescent behaviour and development [ 31 ]. The studies in each section were then grouped into subsections according to their specific brain regions within their network.

Default mode network (DMN)/reward network.

Out of the 12 studies, 3 have specifically studied the default mode network (DMN), and 3 observed whole-brain FC that partially included components of the DMN. The effect of IA on the various centres of the DMN was not unilaterally the same. The findings illustrate a complex mix of increases and decreases in FC depending on the specific region in the DMN (see Table 2 and Fig 2 ). The alteration of FC in posterior cingulate cortex (PCC) in the DMN was the most frequently reported area in adolescents with IA, which involved in attentional processes [ 32 ], but Lee et al. (2020) additionally found alterations of FC in other brain regions, such as anterior insula cortex, a node in the DMN that controls the integration of motivational and cognitive processes [ 20 ].

https://doi.org/10.1371/journal.pmen.0000022.g002

The overall changes of functional connectivity in the brain network including default mode network (DMN), executive control network (ECN), salience network (SN) and reward network. IA = Internet Addiction, FC = Functional Connectivity.

https://doi.org/10.1371/journal.pmen.0000022.t002

Ding et al. (2013) revealed altered FC in the cerebellum, the middle temporal gyrus, and the medial prefrontal cortex (mPFC) [ 22 ]. They found that the bilateral inferior parietal lobule, left superior parietal lobule, and right inferior temporal gyrus had decreased FC, while the bilateral posterior lobe of the cerebellum and the medial temporal gyrus had increased FC [ 22 ]. The right middle temporal gyrus was found to have 111 cluster voxels (t = 3.52, p<0.05) and the right inferior parietal lobule was found to have 324 cluster voxels (t = -4.07, p<0.05) with an extent threshold of 54 voxels (figures above this threshold are deemed significant) [ 22 ]. Additionally, there was a negative correlation, with 95 cluster voxels (p<0.05) between the FC of the left superior parietal lobule and the PCC with the Chen Internet Addiction Scores (CIAS) which are used to determine the severity of IA [ 22 ]. On the other hand, in regions of the reward system, connection with the PCC was positively connected with CIAS scores [ 22 ]. The most significant was the right praecuneus with 219 cluster voxels (p<0.05) [ 22 ]. Wang et al. (2017) also discovered that adolescents with IA had 33% less FC in the left inferior parietal lobule and 20% less FC in the dorsal mPFC [ 24 ]. A potential connection between the effects of substance use and overt internet use is revealed by the generally decreased FC in these areas of the DMN of teenagers with drug addiction and IA [ 35 ].

The putamen was one of the main regions of reduced FC in adolescents with IA [ 19 ]. The putamen and the insula-operculum demonstrated significant group differences regarding functional connectivity with a cluster size of 251 and an extent threshold of 250 (Z = 3.40, p<0.05) [ 19 ]. The molecular mechanisms behind addiction disorders have been intimately connected to decreased striatal dopaminergic function [ 19 ], making this function crucial.

Executive Control Network (ECN).

5 studies out of 12 have specifically viewed parts of the executive control network (ECN) and 3 studies observed whole-brain FC. The effects of IA on the ECN’s constituent parts were consistent across all the studies examined for this analysis (see Table 2 and Fig 3 ). The results showed a notable decline in all the ECN’s major centres. Li et al. (2014) used fMRI imaging and a behavioural task to study response inhibition in adolescents with IA [ 25 ] and found decreased activation at the striatum and frontal gyrus, particularly a reduction in FC at inferior frontal gyrus, in the IA group compared to controls [ 25 ]. The inferior frontal gyrus showed a reduction in FC in comparison to the controls with a cluster size of 71 (t = 4.18, p<0.05) [ 25 ]. In addition, the frontal-basal ganglia pathways in the adolescents with IA showed little effective connection between areas and increased degrees of response inhibition [ 25 ].

https://doi.org/10.1371/journal.pmen.0000022.g003

Lin et al. (2015) found that adolescents with IA demonstrated disrupted corticostriatal FC compared to controls [ 33 ]. The corticostriatal circuitry experienced decreased connectivity with the caudate, bilateral anterior cingulate cortex (ACC), as well as the striatum and frontal gyrus [ 33 ]. The inferior ventral striatum showed significantly reduced FC with the subcallosal ACC and caudate head with cluster size of 101 (t = -4.64, p<0.05) [ 33 ]. Decreased FC in the caudate implies dysfunction of the corticostriatal-limbic circuitry involved in cognitive and emotional control [ 36 ]. The decrease in FC in both the striatum and frontal gyrus is related to inhibitory control, a common deficit seen with disruptions with the ECN [ 33 ].

The dorsolateral prefrontal cortex (DLPFC), ACC, and right supplementary motor area (SMA) of the prefrontal cortex were all found to have significantly decreased grey matter volume [ 29 ]. In addition, the DLPFC, insula, temporal cortices, as well as significant subcortical regions like the striatum and thalamus, showed decreased FC [ 29 ]. According to Tremblay (2009), the striatum plays a significant role in the processing of rewards, decision-making, and motivation [ 37 ]. Chen et al. (2020) reported that the IA group demonstrated increased impulsivity as well as decreased reaction inhibition using a Stroop colour-word task [ 26 ]. Furthermore, Chen et al. (2020) observed that the left DLPFC and dorsal striatum experienced a negative connection efficiency value, specifically demonstrating that the dorsal striatum activity suppressed the left DLPFC [ 27 ].

Salience network (SN).

Out of the 12 chosen studies, 3 studies specifically looked at the salience network (SN) and 3 studies have observed whole-brain FC. Relative to the DMN and ECN, the findings on the SN were slightly sparser. Despite this, adolescents with IA demonstrated a moderate decrease in FC, as well as other measures like fibre connectivity and cognitive control, when compared to healthy control (see Table 2 and Fig 4 ).

https://doi.org/10.1371/journal.pmen.0000022.g004

Xing et al. (2014) used both dorsal anterior cingulate cortex (dACC) and insula to test FC changes in the SN of adolescents with IA and found decreased structural connectivity in the SN as well as decreased fractional anisotropy (FA) that correlated to behaviour performance in the Stroop colour word-task [ 21 ]. They examined the dACC and insula to determine whether the SN’s disrupted connectivity may be linked to the SN’s disruption of regulation, which would explain the impaired cognitive control seen in adolescents with IA. However, researchers did not find significant FC differences in the SN when compared to the controls [ 21 ]. These results provided evidence for the structural changes in the interconnectivity within SN in adolescents with IA.

Wang et al. (2017) investigated network interactions between the DMN, ECN, SN and reward pathway in IA subjects [ 24 ] (see Fig 5 ), and found 40% reduction of FC between the DMN and specific regions of the SN, such as the insula, in comparison to the controls (p = 0.008) [ 24 ]. The anterior insula and dACC are two areas that are impacted by this altered FC [ 24 ]. This finding supports the idea that IA has similar neurobiological abnormalities with other addictive illnesses, which is in line with a study that discovered disruptive changes in the SN and DMN’s interaction in cocaine addiction [ 38 ]. The insula has also been linked to the intensity of symptoms and has been implicated in the development of IA [ 39 ].

“+” indicates an increase in behaivour; “-”indicates a decrease in behaviour; solid arrows indicate a direct network interaction; and the dotted arrows indicates a reduction in network interaction. This diagram depicts network interactions juxtaposed with engaging in internet related behaviours. Through the neural interactions, the diagram illustrates how the networks inhibit or amplify internet usage and vice versa. Furthermore, it demonstrates how the SN mediates both the DMN and ECN.

https://doi.org/10.1371/journal.pmen.0000022.g005

(2) How is adolescent behaviour and development impacted by functional connectivity changes due to internet addiction?

The findings that IA individuals demonstrate an overall decrease in FC in the DMN is supported by numerous research [ 24 ]. Drug addict populations also exhibited similar decline in FC in the DMN [ 40 ]. The disruption of attentional orientation and self-referential processing for both substance and behavioural addiction was then hypothesised to be caused by DMN anomalies in FC [ 41 ].

In adolescents with IA, decline of FC in the parietal lobule affects visuospatial task-related behaviour [ 22 ], short-term memory [ 42 ], and the ability of controlling attention or restraining motor responses during response inhibition tests [ 42 ]. Cue-induced gaming cravings are influenced by the DMN [ 43 ]. A visual processing area called the praecuneus links gaming cues to internal information [ 22 ]. A meta-analysis found that the posterior cingulate cortex activity of individuals with IA during cue-reactivity tasks was connected with their gaming time [ 44 ], suggesting that excessive gaming may impair DMN function and that individuals with IA exert more cognitive effort to control it. Findings for the behavioural consequences of FC changes in the DMN illustrate its underlying role in regulating impulsivity, self-monitoring, and cognitive control.

Furthermore, Ding et al. (2013) reported an activation of components of the reward pathway, including areas like the nucleus accumbens, praecuneus, SMA, caudate, and thalamus, in connection to the DMN [ 22 ]. The increased FC of the limbic and reward networks have been confirmed to be a major biomarker for IA [ 45 , 46 ]. The increased reinforcement in these networks increases the strength of reward stimuli and makes it more difficult for other networks, namely the ECN, to down-regulate the increased attention [ 29 ] (See Fig 5 ).

Executive control network (ECN).

The numerous IA-affected components in the ECN have a role in a variety of behaviours that are connected to both response inhibition and emotional regulation [ 47 ]. For instance, brain regions like the striatum, which are linked to impulsivity and the reward system, are heavily involved in the act of playing online games [ 47 ]. Online game play activates the striatum, which suppresses the left DLPFC in ECN [ 48 ]. As a result, people with IA may find it difficult to control their want to play online games [ 48 ]. This system thus causes impulsive and protracted gaming conduct, lack of inhibitory control leading to the continued use of internet in an overt manner despite a variety of negative effects, personal distress, and signs of psychological dependence [ 33 ] (See Fig 5 ).

Wang et al. (2017) report that disruptions in cognitive control networks within the ECN are frequently linked to characteristics of substance addiction [ 24 ]. With samples that were addicted to heroin and cocaine, previous studies discovered abnormal FC in the ECN and the PFC [ 49 ]. Electronic gaming is known to promote striatal dopamine release, similar to drug addiction [ 50 ]. According to Drgonova and Walther (2016), it is hypothesised that dopamine could stimulate the reward system of the striatum in the brain, leading to a loss of impulse control and a failure of prefrontal lobe executive inhibitory control [ 51 ]. In the end, IA’s resemblance to drug use disorders may point to vital biomarkers or underlying mechanisms that explain how cognitive control and impulsive behaviour are related.

A task-related fMRI study found that the decrease in FC between the left DLPFC and dorsal striatum was congruent with an increase in impulsivity in adolescents with IA [ 26 ]. The lack of response inhibition from the ECN results in a loss of control over internet usage and a reduced capacity to display goal-directed behaviour [ 33 ]. Previous studies have linked the alteration of the ECN in IA with higher cue reactivity and impaired ability to self-regulate internet specific stimuli [ 52 ].

Salience network (SN)/ other networks.

Xing et al. (2014) investigated the significance of the SN regarding cognitive control in teenagers with IA [ 21 ]. The SN, which is composed of the ACC and insula, has been demonstrated to control dynamic changes in other networks to modify cognitive performance [ 21 ]. The ACC is engaged in conflict monitoring and cognitive control, according to previous neuroimaging research [ 53 ]. The insula is a region that integrates interoceptive states into conscious feelings [ 54 ]. The results from Xing et al. (2014) showed declines in the SN regarding its structural connectivity and fractional anisotropy, even though they did not observe any appreciable change in FC in the IA participants [ 21 ]. Due to the small sample size, the results may have indicated that FC methods are not sensitive enough to detect the significant functional changes [ 21 ]. However, task performance behaviours associated with impaired cognitive control in adolescents with IA were correlated with these findings [ 21 ]. Our comprehension of the SN’s broader function in IA can be enhanced by this relationship.

Research study supports the idea that different psychological issues are caused by the functional reorganisation of expansive brain networks, such that strong association between SN and DMN may provide neurological underpinnings at the system level for the uncontrollable character of internet-using behaviours [ 24 ]. In the study by Wang et al. (2017), the decreased interconnectivity between the SN and DMN, comprising regions such the DLPFC and the insula, suggests that adolescents with IA may struggle to effectively inhibit DMN activity during internally focused processing, leading to poorly managed desires or preoccupations to use the internet [ 24 ] (See Fig 5 ). Subsequently, this may cause a failure to inhibit DMN activity as well as a restriction of ECN functionality [ 55 ]. As a result, the adolescent experiences an increased salience and sensitivity towards internet addicting cues making it difficult to avoid these triggers [ 56 ].

The primary aim of this review was to present a summary of how internet addiction impacts on the functional connectivity of adolescent brain. Subsequently, the influence of IA on the adolescent brain was compartmentalised into three sections: alterations of FC at various brain regions, specific FC relationships, and behavioural/developmental changes. Overall, the specific effects of IA on the adolescent brain were not completely clear, given the variety of FC changes. However, there were overarching behavioural, network and developmental trends that were supported that provided insight on adolescent development.

The first hypothesis that was held about this question was that IA was widespread and would be regionally similar to substance-use and gambling addiction. After conducting a review of the information in the chosen articles, the hypothesis was predictably supported. The regions of the brain affected by IA are widespread and influence multiple networks, mainly DMN, ECN, SN and reward pathway. In the DMN, there was a complex mix of increases and decreases within the network. However, in the ECN, the alterations of FC were more unilaterally decreased, but the findings of SN and reward pathway were not quite clear. Overall, the FC changes within adolescents with IA are very much network specific and lay a solid foundation from which to understand the subsequent behaviour changes that arise from the disorder.

The second hypothesis placed emphasis on the importance of between network interactions and within network interactions in the continuation of IA and the development of its behavioural symptoms. The results from the findings involving the networks, DMN, SN, ECN and reward system, support this hypothesis (see Fig 5 ). Studies confirm the influence of all these neural networks on reward valuation, impulsivity, salience to stimuli, cue reactivity and other changes that alter behaviour towards the internet use. Many of these changes are connected to the inherent nature of the adolescent brain.

There are multiple explanations that underlie the vulnerability of the adolescent brain towards IA related urges. Several of them have to do with the inherent nature and underlying mechanisms of the adolescent brain. Children’s emotional, social, and cognitive capacities grow exponentially during childhood and adolescence [ 57 ]. Early teenagers go through a process called “social reorientation” that is characterised by heightened sensitivity to social cues and peer connections [ 58 ]. Adolescents’ improvements in their social skills coincide with changes in their brains’ anatomical and functional organisation [ 59 ]. Functional hubs exhibit growing connectivity strength [ 60 ], suggesting increased functional integration during development. During this time, the brain’s functional networks change from an anatomically dominant structure to a scattered architecture [ 60 ].

The adolescent brain is very responsive to synaptic reorganisation and experience cues [ 61 ]. As a result, one of the distinguishing traits of the maturation of adolescent brains is the variation in neural network trajectory [ 62 ]. Important weaknesses of the adolescent brain that may explain the neurobiological change brought on by external stimuli are illustrated by features like the functional gaps between networks and the inadequate segregation of networks [ 62 ].

The implications of these findings towards adolescent behaviour are significant. Although the exact changes and mechanisms are not fully clear, the observed changes in functional connectivity have the capacity of influencing several aspects of adolescent development. For example, functional connectivity has been utilised to investigate attachment styles in adolescents [ 63 ]. It was observed that adolescent attachment styles were negatively associated with caudate-prefrontal connectivity, but positively with the putamen-visual area connectivity [ 63 ]. Both named areas were also influenced by the onset of internet addiction, possibly providing a connection between the two. Another study associated neighbourhood/socioeconomic disadvantage with functional connectivity alterations in the DMN and dorsal attention network [ 64 ]. The study also found multivariate brain behaviour relationships between the altered/disadvantaged functional connectivity and mental health and cognition [ 64 ]. This conclusion supports the notion that the functional connectivity alterations observed in IA are associated with specific adolescent behaviours as well as the fact that functional connectivity can be utilised as a platform onto which to compare various neurologic conditions.

Limitations/strengths

There were several limitations that were related to the conduction of the review as well as the data extracted from the articles. Firstly, the study followed a systematic literature review design when analysing the fMRI studies. The data pulled from these imaging studies were namely qualitative and were subject to bias contrasting the quantitative nature of statistical analysis. Components of the study, such as sample sizes, effect sizes, and demographics were not weighted or controlled. The second limitation brought up by a similar review was the lack of a universal consensus of terminology given IA [ 47 ]. Globally, authors writing about this topic use an array of terminology including online gaming addiction, internet addiction, internet gaming disorder, and problematic internet use. Often, authors use multiple terms interchangeably which makes it difficult to depict the subtle similarities and differences between the terms.

Reviewing the explicit limitations in each of the included studies, two major limitations were brought up in many of the articles. One was relating to the cross-sectional nature of the included studies. Due to the inherent qualities of a cross-sectional study, the studies did not provide clear evidence that IA played a causal role towards the development of the adolescent brain. While several biopsychosocial factors mediate these interactions, task-based measures that combine executive functions with imaging results reinforce the assumed connection between the two that is utilised by the papers studying IA. Another limitation regarded the small sample size of the included studies, which averaged to around 20 participants. The small sample size can influence the generalisation of the results as well as the effectiveness of statistical analyses. Ultimately, both included study specific limitations illustrate the need for future studies to clarify the causal relationship between the alterations of FC and the development of IA.

Another vital limitation was the limited number of studies applying imaging techniques for investigations on IA in adolescents were a uniformly Far East collection of studies. The reason for this was because the studies included in this review were the only fMRI studies that were found that adhered to the strict adolescent age restriction. The adolescent age range given by the WHO (10–19 years old) [ 65 ] was strictly followed. It is important to note that a multitude of studies found in the initial search utilised an older adolescent demographic that was slightly higher than the WHO age range and had a mean age that was outside of the limitations. As a result, the results of this review are biased and based on the 12 studies that met the inclusion and exclusion criteria.

Regarding the global nature of the research, although the journals that the studies were published in were all established western journals, the collection of studies were found to all originate from Asian countries, namely China and Korea. Subsequently, it pulls into question if the results and measures from these studies are generalisable towards a western population. As stated previously, Asian countries have a higher prevalence of IA, which may be the reasoning to why the majority of studies are from there [ 8 ]. However, in an additional search including other age groups, it was found that a high majority of all FC studies on IA were done in Asian countries. Interestingly, western papers studying fMRI FC were primarily focused on gambling and substance-use addiction disorders. The western papers on IA were less focused on fMRI FC but more on other components of IA such as sleep, game-genre, and other non-imaging related factors. This demonstrated an overall lack of western fMRI studies on IA. It is important to note that both western and eastern fMRI studies on IA presented an overall lack on children and adolescents in general.

Despite the several limitations, this review provided a clear reflection on the state of the data. The strengths of the review include the strict inclusion/exclusion criteria that filtered through studies and only included ones that contained a purely adolescent sample. As a result, the information presented in this review was specific to the review’s aims. Given the sparse nature of adolescent specific fMRI studies on the FC changes in IA, this review successfully provided a much-needed niche representation of adolescent specific results. Furthermore, the review provided a thorough functional explanation of the DMN, ECN, SN and reward pathway making it accessible to readers new to the topic.

Future directions and implications

Through the search process of the review, there were more imaging studies focused on older adolescence and adulthood. Furthermore, finding a review that covered a strictly adolescent population, focused on FC changes, and was specifically depicting IA, was proven difficult. Many related reviews, such as Tereshchenko and Kasparov (2019), looked at risk factors related to the biopsychosocial model, but did not tackle specific alterations in specific structural or functional changes in the brain [ 66 ]. Weinstein (2017) found similar structural and functional results as well as the role IA has in altering response inhibition and reward valuation in adolescents with IA [ 47 ]. Overall, the accumulated findings only paint an emerging pattern which aligns with similar substance-use and gambling disorders. Future studies require more specificity in depicting the interactions between neural networks, as well as more literature on adolescent and comorbid populations. One future field of interest is the incorporation of more task-based fMRI data. Advances in resting-state fMRI methods have yet to be reflected or confirmed in task-based fMRI methods [ 62 ]. Due to the fact that network connectivity is shaped by different tasks, it is critical to confirm that the findings of the resting state fMRI studies also apply to the task based ones [ 62 ]. Subsequently, work in this area will confirm if intrinsic connectivity networks function in resting state will function similarly during goal directed behaviour [ 62 ]. An elevated focus on adolescent populations as well as task-based fMRI methodology will help uncover to what extent adolescent network connectivity maturation facilitates behavioural and cognitive development [ 62 ].

A treatment implication is the potential usage of bupropion for the treatment of IA. Bupropion has been previously used to treat patients with gambling disorder and has been effective in decreasing overall gambling behaviour as well as money spent while gambling [ 67 ]. Bae et al. (2018) found a decrease in clinical symptoms of IA in line with a 12-week bupropion treatment [ 31 ]. The study found that bupropion altered the FC of both the DMN and ECN which in turn decreased impulsivity and attentional deficits for the individuals with IA [ 31 ]. Interventions like bupropion illustrate the importance of understanding the fundamental mechanisms that underlie disorders like IA.

The goal for this review was to summarise the current literature on functional connectivity changes in adolescents with internet addiction. The findings answered the primary research questions that were directed at FC alterations within several networks of the adolescent brain and how that influenced their behaviour and development. Overall, the research demonstrated several wide-ranging effects that influenced the DMN, SN, ECN, and reward centres. Additionally, the findings gave ground to important details such as the maturation of the adolescent brain, the high prevalence of Asian originated studies, and the importance of task-based studies in this field. The process of making this review allowed for a thorough understanding IA and adolescent brain interactions.

Given the influx of technology and media in the lives and education of children and adolescents, an increase in prevalence and focus on internet related behavioural changes is imperative towards future children/adolescent mental health. Events such as COVID-19 act to expose the consequences of extended internet usage on the development and lifestyle of specifically young people. While it is important for parents and older generations to be wary of these changes, it is important for them to develop a base understanding of the issue and not dismiss it as an all-bad or all-good scenario. Future research on IA will aim to better understand the causal relationship between IA and psychological symptoms that coincide with it. The current literature regarding functional connectivity changes in adolescents is limited and requires future studies to test with larger sample sizes, comorbid populations, and populations outside Far East Asia.

This review aimed to demonstrate the inner workings of how IA alters the connection between the primary behavioural networks in the adolescent brain. Predictably, the present answers merely paint an unfinished picture that does not necessarily depict internet usage as overwhelmingly positive or negative. Alternatively, the research points towards emerging patterns that can direct individuals on the consequences of certain variables or risk factors. A clearer depiction of the mechanisms of IA would allow physicians to screen and treat the onset of IA more effectively. Clinically, this could be in the form of more streamlined and accurate sessions of CBT or family therapy, targeting key symptoms of IA. Alternatively clinicians could potentially prescribe treatment such as bupropion to target FC in certain regions of the brain. Furthermore, parental education on IA is another possible avenue of prevention from a public health standpoint. Parents who are aware of the early signs and onset of IA will more effectively handle screen time, impulsivity, and minimize the risk factors surrounding IA.

Additionally, an increased attention towards internet related fMRI research is needed in the West, as mentioned previously. Despite cultural differences, Western countries may hold similarities to the eastern countries with a high prevalence of IA, like China and Korea, regarding the implications of the internet and IA. The increasing influence of the internet on the world may contribute to an overall increase in the global prevalence of IA. Nonetheless, the high saturation of eastern studies in this field should be replicated with a Western sample to determine if the same FC alterations occur. A growing interest in internet related research and education within the West will hopefully lead to the knowledge of healthier internet habits and coping strategies among parents with children and adolescents. Furthermore, IA research has the potential to become a crucial proxy for which to study adolescent brain maturation and development.

Supporting information

S1 checklist. prisma checklist..

https://doi.org/10.1371/journal.pmen.0000022.s001

S1 Appendix. Search strategies with all the terms.

https://doi.org/10.1371/journal.pmen.0000022.s002

S1 Data. Article screening records with details of categorized content.

https://doi.org/10.1371/journal.pmen.0000022.s003

Acknowledgments

The authors thank https://www.stockio.com/free-clipart/brain-01 (with attribution to Stockio.com); and https://www.rawpixel.com/image/6442258/png-sticker-vintage for the free images used to create Figs 2 – 4 .

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