what is discussion and analysis in research paper

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

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

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

What makes an effective discussion?

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

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

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

Tip: Not all journals share the same naming conventions.

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

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

Questions to ask yourself:

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

How to structure a discussion

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

Writing Tips

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

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

Don’t

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

Snippets of Effective Discussions:

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

Identifying reliable indicators of fitness in polar bears

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Academic Paper: Discussion and Analysis

5 min read • march 10, 2023

Dylan Black

Introduction

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

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

Source: GIPHY

Writing a Discussion and Analysis

Explain what your data means.

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

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

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

Why Did Your Results Happen?

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

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

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

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

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

Discussion vs. Summary & Repetition

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

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

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

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

Rubric Points

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

Source: CollegeBoard

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

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

Score of Three: New Understanding, Not Enough Support

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

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

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

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

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

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How to Write the Discussion Section of a Research Paper

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

Updated on September 15, 2023

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?

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

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Research Paper

Definition:

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

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

Structure of Research Paper

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

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

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

Introduction

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

Literature Review

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

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

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

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

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

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

How to Write Research Paper

You can write Research Paper by the following guide:

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

Research Paper Example

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

Research Paper Example sample for Students:

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

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

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

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

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

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

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

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

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

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

References :

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

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

Social Media and Mental Health Survey

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

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

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

When to Write Research Paper

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

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

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

Purpose of Research Paper

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

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

Characteristics of Research Paper

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

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

Advantages of Research Paper

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

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

Limitations of Research Paper

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

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

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

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

What Is in the Discussion Section of a Research Paper

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

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

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

How to Write a Discussion Section in 5 Steps

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

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

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

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

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

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

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

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

Step 3. Relate Your Results in a Discussion Section

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

Step 4. Describe the Limitations in Your Discussion Section

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

Step 5. Conclude Your Discussion Section With Recommendations

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

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

Discussion Section of a Research Paper Example

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

How to Write a Discussion Section: Final Thoughts

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

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

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

Our academic writing service is just a click away. We are proud to say that our writers are professionals in their fields. Buy a research paper and our experts can provide prompt solutions without compromising the quality.

Discussion Section of a Research Paper: Frequently Asked Questions

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

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

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

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

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

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

4. What is the purpose of the discussion section?

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

Joe Eckel is an expert on Dissertations writing. He makes sure that each student gets precious insights on composing A-grade academic writing.

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07 Easy Steps for Writing Discussion Section of a Research Paper

Introduction

I. focus on the relevance.

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

IV. Highlight the Observations

V. compare and relate with other research works.

• VI. Provide  Alternate View Points

A. Future Directions

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

• Your Next Move...

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

II. Highlight  the Limitations 

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

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

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

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

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

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

III. Introduce  New Discoveries

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

VI. Provide  Alternate View Points

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

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

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

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

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

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

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

VII. Future Directions and Conclusion

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Your Next Move…

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

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

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

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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Thank you. payment completed., you will receive an email from us to confirm your registration, please click the link in the email to activate your account., there was error during payment, orcid profile found in public registry, download history, how to write the analysis and discussion chapters in qualitative (ssah) research.

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

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

Look for emerging themes and patterns

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

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

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

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

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

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

Let your data guide you

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

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

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

Work closely with the literature

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

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

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

In conclusion

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

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Organizing Academic Research Papers: 8. The Discussion

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

The purpose of the discussion is to interpret and describe the significance of your findings in light of what was already known about the research problem being investigated, and to explain any new understanding or fresh insights about the problem after you've taken the findings into consideration. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but it does not simply repeat or rearrange the introduction; the discussion should always explain how your study has moved the reader's understanding of the research problem forward from where you left them at the end of the introduction.

Importance of a Good Discussion

This section is often considered the most important part of a 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 on the findings, and to formulate a deeper, more profound understanding of the research problem you are studying.

The discussion section is where you explore the underlying meaning of your research , its possible implications in other areas of study, and the possible improvements that can be made in order to further develop the concerns of your research.

This is the section where you need to present the importance of your study and how it may be able to contribute to and/or fill existing gaps in the field. If appropriate, the discussion section is also where you state how the findings from your study revealed new gaps in the literature that had not been previously exposed or adequately described.

This part of the paper is not strictly governed by objective reporting of information but, rather, it is where you can engage in creative thinking about issues through evidence-based interpretation of findings. This is where you infuse your results with meaning.

Kretchmer, Paul. Fourteen Steps to Writing 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 using jargon.
• Follow a logical stream of thought.
• Use the present verb tense, especially for established facts; however, refer to specific works and references in the past tense.
• If needed, use subheadings to help organize your presentation or to group 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 and present explanations for the results; go into greater depth when explaining 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.
• References to previous research : 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 than being part of the general research you cited to provide context and background information.
• 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 recommending best practices.
• Hypothesis : a more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research].

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, mode of narration, and verb tense [present] that you used when when describing the research problem in the 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 sequencing of providing 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. The order of interpreting each major finding should be in the same order as they were described in your results section.
• A good discussion section includes analysis of any unexpected findings. This paragraph should begin with a description of the unexpected 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 them in the order they appeared as you gathered the data.
• Before concluding the discussion, identify potential limitations and weaknesses. 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 the findings. Avoid using an apologetic tone; however, be honest and self-critical.
• The discussion section should end with a concise summary of the principal implications of the findings regardless of statistical 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 demonstrates to the reader 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 for your readers 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.

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 meaning of the findings and why you believe they are important. After reading the discussion section, you want the reader to think about the results [“why hadn’t I thought of that?”]. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. Begin this part of the section by repeating what you consider to be your most important finding first.

III.  Relate the Findings to Similar Studies

No study is so novel or possesses such a restricted focus that it has absolutely no relation to other previously published research. The discussion section should relate your study findings to those of other studies, particularly if questions raised by previous studies served as the motivation for your study, the findings of other studies support your findings [which strengthens the importance of your study results], and/or they point out how your study differs from other similar studies. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research 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 prior assumptions or biases.

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! Describe the generalizability of your results to other situations, if applicable to the method chosen, then describe in detail problems you encountered in the method(s) you used to gather information. Note any unanswered questions or issues your study did not address, and.... VI.  Make Suggestions for Further Research

Although your study may offer important insights about the research problem, other questions related to the problem likely remain unanswered. Moreover, some unanswered questions may have become more focused because of your study. You should make suggestions for further research in the discussion section.

NOTE: Besides the literature review section, the preponderance of references to sources in your research paper are 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 and/or linked to similar studies. If a study that you cited disagrees with your findings, don't ignore it--clearly explain why the study's findings differ from yours.

V.  Problems to Avoid

• Do not waste entire sentences restating your results . Should you need to remind the reader of the finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “The lack of available housing to single women with children in rural areas of Texas suggests that...[then move to the interpretation of this finding].”
• 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.
• Do not introduce new results in the discussion. Be wary of mistaking the reiteration of a specific finding for an interpretation.
• Use of the first person is acceptable, but too much use of the first person may actually distract the reader from the main points.

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; 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 Overinterpret the Results!

Interpretation is a subjective exercise. Therefore, be careful that you do not read more into the findings than can be supported by the evidence you've gathered. Remember that the data are the data: nothing more, nothing less.

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 interpretion of those results, not just 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 you studied the impact of foreign aid on increasing levels of education among the poor in Bangladesh, it's generally not appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim. If you feel compelled to speculate, 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 the discussion in this way, while others don’t care what your opinion is beyond your efforts to interpret the data.

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Research Paper Writing Guides

Research Paper Discussion Section

Last updated on: Mar 27, 2024

A Detailed Guide: How to Write a Discussion for a Research Paper

By: Jared P.

11 min read

Reviewed By:

Published on: Mar 6, 2024

Writing a research paper discussion isn’t something everyone is good at. You have to portray the significance of your research in the best possible way. If you’re having a hard time writing a convincing discussion, don’t worry!

In this guide, we’ll discuss how to write a research paper discussion. From understanding its importance, elements, writing steps, and some examples, we have it covered. 

Continue reading, and you’ll be more than able to discuss your research paper effectively.

On this Page

What is a Research Paper Discussion?

A research paper discussion informs the reader about the learning outcomes of the research study. Here, the author provides context for the results by explaining how they relate to existing literature, theories, and the overall body of knowledge in the field. 

The discussion answers the questions you raised in the introduction by utilizing the results and findings gathered in the study.

Where to Place the Discussion Section? 

The optimal place for the discussion is to write it after the research paper introduction and before the conclusion for a research paper. It is not a hard and fast rule to sum up your discussion in a single paragraph. You can utilize 2 or at max 3 paragraphs or a convincing discussion.

Understanding the Importance of a Good Discussion Section

Here’s why the discussion is one of the most significant sections of your research paper. 

• Connects the Dots: The discussion links back to the questions raised in the introduction, showing how your study answers them.
• Engages with Experts: It places your study in a conversation with what smart people have said about the research paper topic, creating a connection to existing research.
• Guides the Reader: Think of it as a roadmap, pointing the way to the next stop in research and leaving readers at the start of new possibilities.
• Adds Meaning: It's not just a conclusion; it's the key to unlocking the meaning behind your findings, helping readers understand why your study is important.
• Highlights Significance: Demonstrates the significance of your study by explaining its impact on the reader's understanding of the research paper problem statement .
• Encourages Thinking: The discussion makes readers speculate more about why your study matters, helping them see the bigger picture.
• Keeps the Conversation Going: It suggests new questions or areas to explore, so the study isn't just an endpoint but a starting point for more research.

Read on to see what key elements make up a great discussion.

Key Components of a Research Paper Discussion

There are multiple ways to write the discussion section, but usually, it revolves around the following key elements.

• Interpretation of Results

Explains the meaning of your findings and how they address the research questions or the research paper hypotheses .

• Comparison With Previous Studies 

Discusses how your study and research findings align with or differ from previous studies in the field.

• Theoretical Implications 

Explores the theoretical significance of your findings and their contribution to existing theories.

• Practical implications 

Discusses the real-world applications or implications of your research.

• Limitations

Acknowledges any limitations in your study that may affect the interpretation of results.

• Suggestions for Future Research 

Proposes directions for future research and highlights areas that need further exploration.

• Integration of Your Research Findings 

Summarizes the key points discussed in the discussion section and restate the main conclusions drawn from the study.

After understanding the essential elements that make up the discussion section, you need to know how to combine them for a stellar discussion. 

So, let’s move on to the writing steps.

How to Write a Discussion for a Research Paper in 6 Easy Steps

Follow these steps for writing a good research paper discussion section. 

• Summarize Findings: Begin with a concise summary of your main results.
• Interpret Results: Interpret and explain the meaning of your findings.
• Compare with Literature: Compare your results to existing literature.
• Discuss Implications: Explore theoretical and practical implications.
• Acknowledge Limitations: Clearly state and discuss the limitations of your study.
• Propose Future Research: Suggest directions for future research.

Here is a detailed breakdown of each writing step.

Step 1. Summarize Your Findings

Begin this section by summarizing the key discoveries in your study. In a nutshell, tell your readers what you found, avoiding a repetition of raw data. Aim for clarity, condensing the main results into a brief paragraph.

You can start your summary with the following sentences:

• In this study, the key findings indicate...
• To distill the results, it is evident that...
• Summarizing our main discoveries, we observe...

Example: 

Step 2. Interpret Results and Your Findings 

Move on to interpreting what your findings truly mean. Don't assume your readers will automatically understand the significance. Provide your insights and explanations, considering how the results align with your research question or research paper hypothesis.

You can start interpreting your results with the following sentences:

• Interpreting these results, it becomes clear that...
• These findings imply that...
• Exploring the meaning behind the data, we can infer that...

Step 3. Compare With Existing Literature

Connect your results to existing knowledge and previous research by comparing them with what other researchers have discovered. Show where your study fits into the broader context of the literature, and highlight any differences or similarities.

You can compare your research with existing literature by incorporating the following sentences:

• Aligning our results with existing literature, we find...
• Comparing our study to prior research, a parallel emerges...
• In relation to the broader scholarly conversation, our results resonate with...

Step 4. Discuss Implications of Your Research

Explore both the theoretical and practical implications of your research. Explain how your findings contribute to existing theories and discuss any real-world applications. Clarify why your study matters in a larger context.

You can start discussing your implication with the following sentences:

• Considering the implications of our study, it becomes apparent that...
• Beyond the theoretical implications, our findings hold practical significance by...
• Exploring the broader impact, our research suggests...

Step 5. Acknowledge the Limitations 

Be transparent about the shortcomings of your study. Discuss any constraints in your research design, research paper methods , or unexpected challenges. This not only showcases your honesty but also helps readers understand the scope of your study.

Here are some limitation sentence starters: 

• While our study contributes valuable insights, it's essential to acknowledge...
• Recognizing the constraints of our research, one limitation is...
• Despite the valuable findings, it's important to note the limitations, including...

Step 6. Propose Future Research 

Wrap up by proposing directions for future research. Point out areas that need further exploration or refinement based on the gaps identified in your study. Provide a launchpad for future researchers to build upon your work.

Here are some future research sentence starters:

• This study sets the stage for future research by...
• To build upon this groundwork, future studies could...
• Exploring uncharted territory, future research might investigate...

In only 6 steps, you can craft research paper discussions that can convince readers that your study stands out from the rest and that it contributes to the specific field of research effectively. 

Having said that, it is important to keep note of some common mistakes that you might make in the writing process. Let’s see what you should avoid in your research paper discussion.

Avoiding Common Mistakes in Research Discussion Writing

For a balanced research paper discussion, read the following guidelines:

Don't Introduce Alternate Explanations Without Support: 

Avoid presenting alternate explanations in the discussion without solid evidence from the results section or relevant literature. Speculation without a clear basis can weaken the effectiveness of your discussion.

Don't Overextend Into Similar Studies Without Context:

Refrain from discussing similar studies without providing proper context. While it's essential to relate your findings to existing literature, discussing similar studies should be done thoughtfully within the boundaries of your research scope.

Don't Replicate the Results Section:

Don't repeat your results over and over again during the discussion. The discussion should focus on interpretation and analysis rather than duplicating information from the results section.

Avoid Overemphasizing Conclusions:

Avoid placing too much emphasis on conclusions in the discussion section. Save comprehensive conclusions for the dedicated conclusion section of research paper . Keep the focus on interpreting your results and engaging in an effective discussion.

Don't Ignore Suggestions for Further Research: 

Don't neglect to include suggestions for further research in your discussion. If you have ideas for what to do next, acknowledge them and include them in your discussion. However, avoid overloading the discussion with excessive recommendations.

Don't Disregard the Literature Review:

Avoid disregarding the research paper literature review in the discussion. Connect your findings back to the existing literature, ensuring a coherent narrative that contextualizes your results within the broader scholarly conversation.

Avoid Apologizing or Undermining: 

Don't apologize or make statements that make your research seem less credible. Instead of doubting your methodology or execution, focus on addressing limitations transparently without reducing the overall impact of your study.

Don't Overstate Importance Without Support:

Refrain from overstating the importance of your findings without sufficient support. Make sure that your discussion maintains a realistic and measured tone. Always avoid grand statements that may lead readers to question the validity of your research.

Don't Neglect Transition Sentences:

Don't overlook the use of bridge sentences when reminding readers of your findings in the discussion. These sentences help smoothly transition between the results section and the interpretation, contributing to an effective and cohesive discussion.

Get Help From Some Practical Tips

Here are some tips for you to polish your discussion section to perfection.

• Keep your discussion concise and directly related to your main findings
• Address potential counterarguments to strengthen your discussion
• Try to engage readers with thought-provoking questions 
• It is always a good idea to get input from peers to enhance the credibility of your discussion
• Share brief personal insights or reflections on the research process
• Always stick to the specific journal guidelines for the discussion section
• Maintain an objective and analytical tone throughout the discussion

Discussion for a Research Paper - Examples

Looking at examples is a great idea if you want to know how to write a research paper discussion. 

Example of Discussion in Research Paper PDF

Discussion for a Medical Research Paper

Example of Result and Discussion in Research Paper PDF

Example of Discussion Findings in Research

Discussion Paper Sample PDF

How to Start a Discussion in a Report

To conclude, understanding the significance of crafting a powerful research paper discussion is vital. This guide has provided valuable insights and steps for creating an impactful discussion section. 

By following our steps, we’re sure that you’ll be able to write the perfect discussion section for your research paper. However, it is still easier said than done. 

If you seek expert assistance or want to secure perfection in your research paper writing tasks, such as discussion, introduction, body, etc., turn to SharkPapers.com.

Professionals in our diverse pool of writers specialize in research paper writing and can skillfully handle every aspect. Our paper writing service online enhances your research paper with a precisely written discussion. 

Connect with our experts today for an academic boost. Your perfectly crafted discussion section awaits a simple click away!

Frequently Asked Questions

How should one initiate the discussion section in a research paper.

To start the discussion section in a research paper, start by summarizing the key findings. Provide a concise overview of your main results, avoiding raw data repetition. This sets the stage for an in-depth analysis.

What is an example of a discussion?

An example of a discussion in the context of a research paper is when researchers analyze their study's findings, interpret the results, compare them with existing literature, and explore the implications. It involves a thoughtful conversation within the paper, similar to how individuals engage in dialogue to reconcile differing opinions.

What are the guidelines for writing a discussion in a research paper following the APA format?

When writing a discussion in a research paper following APA format, adhere to these guidelines:

• Structure: Place the discussion after the introduction and before the conclusion.
• Tense: Use past tense when discussing results.
• Tone: Maintain an objective tone, avoiding overly speculative language.
• Citations: Reference relevant literature using APA citation style.
• Conciseness: Keep the discussion concise, focusing on interpretation rather than repeating results.

Jared P., a celebrated writer and writing service provider with over a decade and a half of experience, has helped countless students reach their academic goals by providing professional writing assistance. His Ph.D. in English Literature solidifies his expertise in the field and enables him to produce critical and thorough papers every time!

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Frequently asked questions

What’s the difference between results and discussion.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

Frequently asked questions: Dissertation

Dissertation word counts vary widely across different fields, institutions, and levels of education:

• An undergraduate dissertation is typically 8,000–15,000 words
• A master’s dissertation is typically 12,000–50,000 words
• A PhD thesis is typically book-length: 70,000–100,000 words

However, none of these are strict guidelines – your word count may be lower or higher than the numbers stated here. Always check the guidelines provided by your university to determine how long your own dissertation should be.

A dissertation prospectus or proposal describes what or who you plan to research for your dissertation. It delves into why, when, where, and how you will do your research, as well as helps you choose a type of research to pursue. You should also determine whether you plan to pursue qualitative or quantitative methods and what your research design will look like.

It should outline all of the decisions you have taken about your project, from your dissertation topic to your hypotheses and research objectives , ready to be approved by your supervisor or committee.

Note that some departments require a defense component, where you present your prospectus to your committee orally.

A thesis is typically written by students finishing up a bachelor’s or Master’s degree. Some educational institutions, particularly in the liberal arts, have mandatory theses, but they are often not mandatory to graduate from bachelor’s degrees. It is more common for a thesis to be a graduation requirement from a Master’s degree.

Even if not mandatory, you may want to consider writing a thesis if you:

• Plan to attend graduate school soon
• Have a particular topic you’d like to study more in-depth
• Are considering a career in research
• Would like a capstone experience to tie up your academic experience

The conclusion of your thesis or dissertation should include the following:

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

The conclusion of your thesis or dissertation shouldn’t take up more than 5–7% of your overall word count.

For a stronger dissertation conclusion , avoid including:

• Important evidence or analysis that wasn’t mentioned in the discussion section and results section
• Generic concluding phrases (e.g. “In conclusion …”)
• Weak statements that undermine your argument (e.g., “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations 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 discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

A theoretical framework can sometimes be integrated into a  literature review chapter , but it can also be included as its own chapter or section in your dissertation . As a rule of thumb, if your research involves dealing with a lot of complex theories, it’s a good idea to include a separate theoretical framework chapter.

A literature review and a theoretical framework are not the same thing and cannot be used interchangeably. While a theoretical framework describes the theoretical underpinnings of your work, a literature review critically evaluates existing research relating to your topic. You’ll likely need both in your dissertation .

While a theoretical framework describes the theoretical underpinnings of your work based on existing research, a conceptual framework allows you to draw your own conclusions, mapping out the variables you may use in your study and the interplay between them.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

In most styles, the title page is used purely to provide information and doesn’t include any images. Ask your supervisor if you are allowed to include an image on the title page before doing so. If you do decide to include one, make sure to check whether you need permission from the creator of the image.

Include a note directly beneath the image acknowledging where it comes from, beginning with the word “ Note .” (italicized and followed by a period). Include a citation and copyright attribution . Don’t title, number, or label the image as a figure , since it doesn’t appear in your main text.

Definitional terms often fall into the category of common knowledge , meaning that they don’t necessarily have to be cited. This guidance can apply to your thesis or dissertation glossary as well.

However, if you’d prefer to cite your sources , you can follow guidance for citing dictionary entries in MLA or APA style for your glossary.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, an index is a list of the contents of your work organized by page number.

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

The title page of your thesis or dissertation should include your name, department, institution, degree program, and submission date.

Glossaries are not mandatory, but if you use a lot of technical or field-specific terms, it may improve readability to add one to your thesis or dissertation. Your educational institution may also require them, so be sure to check their specific guidelines.

A glossary or “glossary of terms” is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. Your glossary only needs to include terms that your reader may not be familiar with, and is intended to enhance their understanding of your work.

A glossary is a collection of words pertaining to a specific topic. In your thesis or dissertation, it’s a list of all terms you used that may not immediately be obvious to your reader. In contrast, dictionaries are more general collections of words.

An abbreviation is a shortened version of an existing word, such as Dr. for Doctor. In contrast, an acronym uses the first letter of each word to create a wholly new word, such as UNESCO (an acronym for the United Nations Educational, Scientific and Cultural Organization).

As a rule of thumb, write the explanation in full the first time you use an acronym or abbreviation. You can then proceed with the shortened version. However, if the abbreviation is very common (like PC, USA, or DNA), then you can use the abbreviated version from the get-go.

Be sure to add each abbreviation in your list of abbreviations !

If you only used a few abbreviations in your thesis or dissertation , you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimizing confusion about abbreviations unfamiliar to your reader.

A list of abbreviations is a list of all the abbreviations that you used in your thesis or dissertation. It should appear at the beginning of your document, with items in alphabetical order, just after your table of contents .

Your list of tables and figures should go directly after your table of contents in your thesis or dissertation.

Lists of figures and tables are often not required, and aren’t particularly common. They specifically aren’t required for APA-Style, though you should be careful to follow their other guidelines for figures and tables .

If you have many figures and tables in your thesis or dissertation, include one may help you stay organized. Your educational institution may require them, so be sure to check their guidelines.

A list of figures and tables compiles all of the figures and tables that you used in your thesis or dissertation and displays them with the page number where they can be found.

The table of contents in a thesis or dissertation always goes between your abstract and your introduction .

You may acknowledge God in your dissertation acknowledgements , but be sure to follow academic convention by also thanking the members of academia, as well as family, colleagues, and friends who helped you.

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other  academic texts , with an introduction , a main body, and a conclusion .

An  annotated bibliography is a list of  source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a  paper .  

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

• The type of analysis used
• Relevant results in the form of descriptive and inferential statistics
• Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

• Recurring patterns
• Significant or representative individual responses
• Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

To automatically insert a table of contents in Microsoft Word, follow these steps:

• Apply heading styles throughout the document.
• In the references section in the ribbon, locate the Table of Contents group.
• Click the arrow next to the Table of Contents icon and select Custom Table of Contents.
• Select which levels of headings you would like to include in the table of contents.

Make sure to update your table of contents if you move text or change headings. To update, simply right click and select Update Field.

All level 1 and 2 headings should be included in your table of contents . That means the titles of your chapters and the main sections within them.

The contents should also include all appendices and the lists of tables and figures, if applicable, as well as your reference list .

Do not include the acknowledgements or abstract in the table of contents.

The abstract appears on its own page in the thesis or dissertation , after the title page and acknowledgements but before the table of contents .

An abstract for a thesis or dissertation is usually around 200–300 words. There’s often a strict word limit, so make sure to check your university’s requirements.

In a thesis or dissertation, the acknowledgements should usually be no longer than one page. There is no minimum length.

The acknowledgements are generally included at the very beginning of your thesis , directly after the title page and before the abstract .

Yes, it’s important to thank your supervisor(s) in the acknowledgements section of your thesis or dissertation .

Even if you feel your supervisor did not contribute greatly to the final product, you must acknowledge them, if only for a very brief thank you. If you do not include your supervisor, it may be seen as a snub.

In the acknowledgements of your thesis or dissertation, you should first thank those who helped you academically or professionally, such as your supervisor, funders, and other academics.

Then you can include personal thanks to friends, family members, or anyone else who supported you during the process.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

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

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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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 Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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• Michiel Schreurs   ORCID: orcid.org/0000-0002-9449-5619 1 , 2 , 3   na1 ,
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• Miguel Roncoroni   ORCID: orcid.org/0000-0001-7461-1427 1 , 2 , 3   na1 ,
• Lloyd Cool   ORCID: orcid.org/0000-0001-9936-3124 1 , 2 , 3 , 4 ,
• Beatriz Herrera-Malaver   ORCID: orcid.org/0000-0002-5096-9974 1 , 2 , 3 ,
• Christophe Vanderaa   ORCID: orcid.org/0000-0001-7443-5427 4 ,
• Florian A. Theßeling 1 , 2 , 3 ,
• Łukasz Kreft   ORCID: orcid.org/0000-0001-7620-4657 5 ,
• Alexander Botzki   ORCID: orcid.org/0000-0001-6691-4233 5 ,
• Philippe Malcorps 6 ,
• Luk Daenen 6 ,
• Tom Wenseleers   ORCID: orcid.org/0000-0002-1434-861X 4 &
• Kevin J. Verstrepen   ORCID: orcid.org/0000-0002-3077-6219 1 , 2 , 3  

Nature Communications volume  15 , Article number:  2368 ( 2024 ) Cite this article

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• Chemical engineering
• Gas chromatography
• Machine learning
• Metabolomics
• Taste receptors

The perception and appreciation of food flavor depends on many interacting chemical compounds and external factors, and therefore proves challenging to understand and predict. Here, we combine extensive chemical and sensory analyses of 250 different beers to train machine learning models that allow predicting flavor and consumer appreciation. For each beer, we measure over 200 chemical properties, perform quantitative descriptive sensory analysis with a trained tasting panel and map data from over 180,000 consumer reviews to train 10 different machine learning models. The best-performing algorithm, Gradient Boosting, yields models that significantly outperform predictions based on conventional statistics and accurately predict complex food features and consumer appreciation from chemical profiles. Model dissection allows identifying specific and unexpected compounds as drivers of beer flavor and appreciation. Adding these compounds results in variants of commercial alcoholic and non-alcoholic beers with improved consumer appreciation. Together, our study reveals how big data and machine learning uncover complex links between food chemistry, flavor and consumer perception, and lays the foundation to develop novel, tailored foods with superior flavors.

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Introduction

Predicting and understanding food perception and appreciation is one of the major challenges in food science. Accurate modeling of food flavor and appreciation could yield important opportunities for both producers and consumers, including quality control, product fingerprinting, counterfeit detection, spoilage detection, and the development of new products and product combinations (food pairing) 1 , 2 , 3 , 4 , 5 , 6 . Accurate models for flavor and consumer appreciation would contribute greatly to our scientific understanding of how humans perceive and appreciate flavor. Moreover, accurate predictive models would also facilitate and standardize existing food assessment methods and could supplement or replace assessments by trained and consumer tasting panels, which are variable, expensive and time-consuming 7 , 8 , 9 . Lastly, apart from providing objective, quantitative, accurate and contextual information that can help producers, models can also guide consumers in understanding their personal preferences 10 .

Despite the myriad of applications, predicting food flavor and appreciation from its chemical properties remains a largely elusive goal in sensory science, especially for complex food and beverages 11 , 12 . A key obstacle is the immense number of flavor-active chemicals underlying food flavor. Flavor compounds can vary widely in chemical structure and concentration, making them technically challenging and labor-intensive to quantify, even in the face of innovations in metabolomics, such as non-targeted metabolic fingerprinting 13 , 14 . Moreover, sensory analysis is perhaps even more complicated. Flavor perception is highly complex, resulting from hundreds of different molecules interacting at the physiochemical and sensorial level. Sensory perception is often non-linear, characterized by complex and concentration-dependent synergistic and antagonistic effects 15 , 16 , 17 , 18 , 19 , 20 , 21 that are further convoluted by the genetics, environment, culture and psychology of consumers 22 , 23 , 24 . Perceived flavor is therefore difficult to measure, with problems of sensitivity, accuracy, and reproducibility that can only be resolved by gathering sufficiently large datasets 25 . Trained tasting panels are considered the prime source of quality sensory data, but require meticulous training, are low throughput and high cost. Public databases containing consumer reviews of food products could provide a valuable alternative, especially for studying appreciation scores, which do not require formal training 25 . Public databases offer the advantage of amassing large amounts of data, increasing the statistical power to identify potential drivers of appreciation. However, public datasets suffer from biases, including a bias in the volunteers that contribute to the database, as well as confounding factors such as price, cult status and psychological conformity towards previous ratings of the product.

Classical multivariate statistics and machine learning methods have been used to predict flavor of specific compounds by, for example, linking structural properties of a compound to its potential biological activities or linking concentrations of specific compounds to sensory profiles 1 , 26 . Importantly, most previous studies focused on predicting organoleptic properties of single compounds (often based on their chemical structure) 27 , 28 , 29 , 30 , 31 , 32 , 33 , thus ignoring the fact that these compounds are present in a complex matrix in food or beverages and excluding complex interactions between compounds. Moreover, the classical statistics commonly used in sensory science 34 , 35 , 36 , 37 , 38 , 39 require a large sample size and sufficient variance amongst predictors to create accurate models. They are not fit for studying an extensive set of hundreds of interacting flavor compounds, since they are sensitive to outliers, have a high tendency to overfit and are less suited for non-linear and discontinuous relationships 40 .

In this study, we combine extensive chemical analyses and sensory data of a set of different commercial beers with machine learning approaches to develop models that predict taste, smell, mouthfeel and appreciation from compound concentrations. Beer is particularly suited to model the relationship between chemistry, flavor and appreciation. First, beer is a complex product, consisting of thousands of flavor compounds that partake in complex sensory interactions 41 , 42 , 43 . This chemical diversity arises from the raw materials (malt, yeast, hops, water and spices) and biochemical conversions during the brewing process (kilning, mashing, boiling, fermentation, maturation and aging) 44 , 45 . Second, the advent of the internet saw beer consumers embrace online review platforms, such as RateBeer (ZX Ventures, Anheuser-Busch InBev SA/NV) and BeerAdvocate (Next Glass, inc.). In this way, the beer community provides massive data sets of beer flavor and appreciation scores, creating extraordinarily large sensory databases to complement the analyses of our professional sensory panel. Specifically, we characterize over 200 chemical properties of 250 commercial beers, spread across 22 beer styles, and link these to the descriptive sensory profiling data of a 16-person in-house trained tasting panel and data acquired from over 180,000 public consumer reviews. These unique and extensive datasets enable us to train a suite of machine learning models to predict flavor and appreciation from a beer’s chemical profile. Dissection of the best-performing models allows us to pinpoint specific compounds as potential drivers of beer flavor and appreciation. Follow-up experiments confirm the importance of these compounds and ultimately allow us to significantly improve the flavor and appreciation of selected commercial beers. Together, our study represents a significant step towards understanding complex flavors and reinforces the value of machine learning to develop and refine complex foods. In this way, it represents a stepping stone for further computer-aided food engineering applications 46 .

To generate a comprehensive dataset on beer flavor, we selected 250 commercial Belgian beers across 22 different beer styles (Supplementary Fig.  S1 ). Beers with ≤ 4.2% alcohol by volume (ABV) were classified as non-alcoholic and low-alcoholic. Blonds and Tripels constitute a significant portion of the dataset (12.4% and 11.2%, respectively) reflecting their presence on the Belgian beer market and the heterogeneity of beers within these styles. By contrast, lager beers are less diverse and dominated by a handful of brands. Rare styles such as Brut or Faro make up only a small fraction of the dataset (2% and 1%, respectively) because fewer of these beers are produced and because they are dominated by distinct characteristics in terms of flavor and chemical composition.

Extensive analysis identifies relationships between chemical compounds in beer

For each beer, we measured 226 different chemical properties, including common brewing parameters such as alcohol content, iso-alpha acids, pH, sugar concentration 47 , and over 200 flavor compounds (Methods, Supplementary Table  S1 ). A large portion (37.2%) are terpenoids arising from hopping, responsible for herbal and fruity flavors 16 , 48 . A second major category are yeast metabolites, such as esters and alcohols, that result in fruity and solvent notes 48 , 49 , 50 . Other measured compounds are primarily derived from malt, or other microbes such as non- Saccharomyces yeasts and bacteria (‘wild flora’). Compounds that arise from spices or staling are labeled under ‘Others’. Five attributes (caloric value, total acids and total ester, hop aroma and sulfur compounds) are calculated from multiple individually measured compounds.

As a first step in identifying relationships between chemical properties, we determined correlations between the concentrations of the compounds (Fig.  1 , upper panel, Supplementary Data  1 and 2 , and Supplementary Fig.  S2 . For the sake of clarity, only a subset of the measured compounds is shown in Fig.  1 ). Compounds of the same origin typically show a positive correlation, while absence of correlation hints at parameters varying independently. For example, the hop aroma compounds citronellol, and alpha-terpineol show moderate correlations with each other (Spearman’s rho=0.39 and 0.57), but not with the bittering hop component iso-alpha acids (Spearman’s rho=0.16 and −0.07). This illustrates how brewers can independently modify hop aroma and bitterness by selecting hop varieties and dosage time. If hops are added early in the boiling phase, chemical conversions increase bitterness while aromas evaporate, conversely, late addition of hops preserves aroma but limits bitterness 51 . Similarly, hop-derived iso-alpha acids show a strong anti-correlation with lactic acid and acetic acid, likely reflecting growth inhibition of lactic acid and acetic acid bacteria, or the consequent use of fewer hops in sour beer styles, such as West Flanders ales and Fruit beers, that rely on these bacteria for their distinct flavors 52 . Finally, yeast-derived esters (ethyl acetate, ethyl decanoate, ethyl hexanoate, ethyl octanoate) and alcohols (ethanol, isoamyl alcohol, isobutanol, and glycerol), correlate with Spearman coefficients above 0.5, suggesting that these secondary metabolites are correlated with the yeast genetic background and/or fermentation parameters and may be difficult to influence individually, although the choice of yeast strain may offer some control 53 .

Spearman rank correlations are shown. Descriptors are grouped according to their origin (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)), and sensory aspect (aroma, taste, palate, and overall appreciation). Please note that for the chemical compounds, for the sake of clarity, only a subset of the total number of measured compounds is shown, with an emphasis on the key compounds for each source. For more details, see the main text and Methods section. Chemical data can be found in Supplementary Data  1 , correlations between all chemical compounds are depicted in Supplementary Fig.  S2 and correlation values can be found in Supplementary Data  2 . See Supplementary Data  4 for sensory panel assessments and Supplementary Data  5 for correlation values between all sensory descriptors.

Interestingly, different beer styles show distinct patterns for some flavor compounds (Supplementary Fig.  S3 ). These observations agree with expectations for key beer styles, and serve as a control for our measurements. For instance, Stouts generally show high values for color (darker), while hoppy beers contain elevated levels of iso-alpha acids, compounds associated with bitter hop taste. Acetic and lactic acid are not prevalent in most beers, with notable exceptions such as Kriek, Lambic, Faro, West Flanders ales and Flanders Old Brown, which use acid-producing bacteria ( Lactobacillus and Pediococcus ) or unconventional yeast ( Brettanomyces ) 54 , 55 . Glycerol, ethanol and esters show similar distributions across all beer styles, reflecting their common origin as products of yeast metabolism during fermentation 45 , 53 . Finally, low/no-alcohol beers contain low concentrations of glycerol and esters. This is in line with the production process for most of the low/no-alcohol beers in our dataset, which are produced through limiting fermentation or by stripping away alcohol via evaporation or dialysis, with both methods having the unintended side-effect of reducing the amount of flavor compounds in the final beer 56 , 57 .

Besides expected associations, our data also reveals less trivial associations between beer styles and specific parameters. For example, geraniol and citronellol, two monoterpenoids responsible for citrus, floral and rose flavors and characteristic of Citra hops, are found in relatively high amounts in Christmas, Saison, and Brett/co-fermented beers, where they may originate from terpenoid-rich spices such as coriander seeds instead of hops 58 .

Tasting panel assessments reveal sensorial relationships in beer

To assess the sensory profile of each beer, a trained tasting panel evaluated each of the 250 beers for 50 sensory attributes, including different hop, malt and yeast flavors, off-flavors and spices. Panelists used a tasting sheet (Supplementary Data  3 ) to score the different attributes. Panel consistency was evaluated by repeating 12 samples across different sessions and performing ANOVA. In 95% of cases no significant difference was found across sessions ( p  > 0.05), indicating good panel consistency (Supplementary Table  S2 ).

Aroma and taste perception reported by the trained panel are often linked (Fig.  1 , bottom left panel and Supplementary Data  4 and 5 ), with high correlations between hops aroma and taste (Spearman’s rho=0.83). Bitter taste was found to correlate with hop aroma and taste in general (Spearman’s rho=0.80 and 0.69), and particularly with “grassy” noble hops (Spearman’s rho=0.75). Barnyard flavor, most often associated with sour beers, is identified together with stale hops (Spearman’s rho=0.97) that are used in these beers. Lactic and acetic acid, which often co-occur, are correlated (Spearman’s rho=0.66). Interestingly, sweetness and bitterness are anti-correlated (Spearman’s rho = −0.48), confirming the hypothesis that they mask each other 59 , 60 . Beer body is highly correlated with alcohol (Spearman’s rho = 0.79), and overall appreciation is found to correlate with multiple aspects that describe beer mouthfeel (alcohol, carbonation; Spearman’s rho= 0.32, 0.39), as well as with hop and ester aroma intensity (Spearman’s rho=0.39 and 0.35).

Similar to the chemical analyses, sensorial analyses confirmed typical features of specific beer styles (Supplementary Fig.  S4 ). For example, sour beers (Faro, Flanders Old Brown, Fruit beer, Kriek, Lambic, West Flanders ale) were rated acidic, with flavors of both acetic and lactic acid. Hoppy beers were found to be bitter and showed hop-associated aromas like citrus and tropical fruit. Malt taste is most detected among scotch, stout/porters, and strong ales, while low/no-alcohol beers, which often have a reputation for being ‘worty’ (reminiscent of unfermented, sweet malt extract) appear in the middle. Unsurprisingly, hop aromas are most strongly detected among hoppy beers. Like its chemical counterpart (Supplementary Fig.  S3 ), acidity shows a right-skewed distribution, with the most acidic beers being Krieks, Lambics, and West Flanders ales.

Tasting panel assessments of specific flavors correlate with chemical composition

We find that the concentrations of several chemical compounds strongly correlate with specific aroma or taste, as evaluated by the tasting panel (Fig.  2 , Supplementary Fig.  S5 , Supplementary Data  6 ). In some cases, these correlations confirm expectations and serve as a useful control for data quality. For example, iso-alpha acids, the bittering compounds in hops, strongly correlate with bitterness (Spearman’s rho=0.68), while ethanol and glycerol correlate with tasters’ perceptions of alcohol and body, the mouthfeel sensation of fullness (Spearman’s rho=0.82/0.62 and 0.72/0.57 respectively) and darker color from roasted malts is a good indication of malt perception (Spearman’s rho=0.54).

Heatmap colors indicate Spearman’s Rho. Axes are organized according to sensory categories (aroma, taste, mouthfeel, overall), chemical categories and chemical sources in beer (malt (blue), hops (green), yeast (red), wild flora (yellow), Others (black)). See Supplementary Data  6 for all correlation values.

Interestingly, for some relationships between chemical compounds and perceived flavor, correlations are weaker than expected. For example, the rose-smelling phenethyl acetate only weakly correlates with floral aroma. This hints at more complex relationships and interactions between compounds and suggests a need for a more complex model than simple correlations. Lastly, we uncovered unexpected correlations. For instance, the esters ethyl decanoate and ethyl octanoate appear to correlate slightly with hop perception and bitterness, possibly due to their fruity flavor. Iron is anti-correlated with hop aromas and bitterness, most likely because it is also anti-correlated with iso-alpha acids. This could be a sign of metal chelation of hop acids 61 , given that our analyses measure unbound hop acids and total iron content, or could result from the higher iron content in dark and Fruit beers, which typically have less hoppy and bitter flavors 62 .

Public consumer reviews complement expert panel data

To complement and expand the sensory data of our trained tasting panel, we collected 180,000 reviews of our 250 beers from the online consumer review platform RateBeer. This provided numerical scores for beer appearance, aroma, taste, palate, overall quality as well as the average overall score.

Public datasets are known to suffer from biases, such as price, cult status and psychological conformity towards previous ratings of a product. For example, prices correlate with appreciation scores for these online consumer reviews (rho=0.49, Supplementary Fig.  S6 ), but not for our trained tasting panel (rho=0.19). This suggests that prices affect consumer appreciation, which has been reported in wine 63 , while blind tastings are unaffected. Moreover, we observe that some beer styles, like lagers and non-alcoholic beers, generally receive lower scores, reflecting that online reviewers are mostly beer aficionados with a preference for specialty beers over lager beers. In general, we find a modest correlation between our trained panel’s overall appreciation score and the online consumer appreciation scores (Fig.  3 , rho=0.29). Apart from the aforementioned biases in the online datasets, serving temperature, sample freshness and surroundings, which are all tightly controlled during the tasting panel sessions, can vary tremendously across online consumers and can further contribute to (among others, appreciation) differences between the two categories of tasters. Importantly, in contrast to the overall appreciation scores, for many sensory aspects the results from the professional panel correlated well with results obtained from RateBeer reviews. Correlations were highest for features that are relatively easy to recognize even for untrained tasters, like bitterness, sweetness, alcohol and malt aroma (Fig.  3 and below).

RateBeer text mining results can be found in Supplementary Data  7 . Rho values shown are Spearman correlation values, with asterisks indicating significant correlations ( p  < 0.05, two-sided). All p values were smaller than 0.001, except for Esters aroma (0.0553), Esters taste (0.3275), Esters aroma—banana (0.0019), Coriander (0.0508) and Diacetyl (0.0134).

Besides collecting consumer appreciation from these online reviews, we developed automated text analysis tools to gather additional data from review texts (Supplementary Data  7 ). Processing review texts on the RateBeer database yielded comparable results to the scores given by the trained panel for many common sensory aspects, including acidity, bitterness, sweetness, alcohol, malt, and hop tastes (Fig.  3 ). This is in line with what would be expected, since these attributes require less training for accurate assessment and are less influenced by environmental factors such as temperature, serving glass and odors in the environment. Consumer reviews also correlate well with our trained panel for 4-vinyl guaiacol, a compound associated with a very characteristic aroma. By contrast, correlations for more specific aromas like ester, coriander or diacetyl are underrepresented in the online reviews, underscoring the importance of using a trained tasting panel and standardized tasting sheets with explicit factors to be scored for evaluating specific aspects of a beer. Taken together, our results suggest that public reviews are trustworthy for some, but not all, flavor features and can complement or substitute taste panel data for these sensory aspects.

Models can predict beer sensory profiles from chemical data

The rich datasets of chemical analyses, tasting panel assessments and public reviews gathered in the first part of this study provided us with a unique opportunity to develop predictive models that link chemical data to sensorial features. Given the complexity of beer flavor, basic statistical tools such as correlations or linear regression may not always be the most suitable for making accurate predictions. Instead, we applied different machine learning models that can model both simple linear and complex interactive relationships. Specifically, we constructed a set of regression models to predict (a) trained panel scores for beer flavor and quality and (b) public reviews’ appreciation scores from beer chemical profiles. We trained and tested 10 different models (Methods), 3 linear regression-based models (simple linear regression with first-order interactions (LR), lasso regression with first-order interactions (Lasso), partial least squares regressor (PLSR)), 5 decision tree models (AdaBoost regressor (ABR), extra trees (ET), gradient boosting regressor (GBR), random forest (RF) and XGBoost regressor (XGBR)), 1 support vector regression (SVR), and 1 artificial neural network (ANN) model.

To compare the performance of our machine learning models, the dataset was randomly split into a training and test set, stratified by beer style. After a model was trained on data in the training set, its performance was evaluated on its ability to predict the test dataset obtained from multi-output models (based on the coefficient of determination, see Methods). Additionally, individual-attribute models were ranked per descriptor and the average rank was calculated, as proposed by Korneva et al. 64 . Importantly, both ways of evaluating the models’ performance agreed in general. Performance of the different models varied (Table  1 ). It should be noted that all models perform better at predicting RateBeer results than results from our trained tasting panel. One reason could be that sensory data is inherently variable, and this variability is averaged out with the large number of public reviews from RateBeer. Additionally, all tree-based models perform better at predicting taste than aroma. Linear models (LR) performed particularly poorly, with negative R 2 values, due to severe overfitting (training set R 2  = 1). Overfitting is a common issue in linear models with many parameters and limited samples, especially with interaction terms further amplifying the number of parameters. L1 regularization (Lasso) successfully overcomes this overfitting, out-competing multiple tree-based models on the RateBeer dataset. Similarly, the dimensionality reduction of PLSR avoids overfitting and improves performance, to some extent. Still, tree-based models (ABR, ET, GBR, RF and XGBR) show the best performance, out-competing the linear models (LR, Lasso, PLSR) commonly used in sensory science 65 .

GBR models showed the best overall performance in predicting sensory responses from chemical information, with R 2 values up to 0.75 depending on the predicted sensory feature (Supplementary Table  S4 ). The GBR models predict consumer appreciation (RateBeer) better than our trained panel’s appreciation (R 2 value of 0.67 compared to R 2 value of 0.09) (Supplementary Table  S3 and Supplementary Table  S4 ). ANN models showed intermediate performance, likely because neural networks typically perform best with larger datasets 66 . The SVR shows intermediate performance, mostly due to the weak predictions of specific attributes that lower the overall performance (Supplementary Table  S4 ).

Model dissection identifies specific, unexpected compounds as drivers of consumer appreciation

Next, we leveraged our models to infer important contributors to sensory perception and consumer appreciation. Consumer preference is a crucial sensory aspects, because a product that shows low consumer appreciation scores often does not succeed commercially 25 . Additionally, the requirement for a large number of representative evaluators makes consumer trials one of the more costly and time-consuming aspects of product development. Hence, a model for predicting chemical drivers of overall appreciation would be a welcome addition to the available toolbox for food development and optimization.

Since GBR models on our RateBeer dataset showed the best overall performance, we focused on these models. Specifically, we used two approaches to identify important contributors. First, rankings of the most important predictors for each sensorial trait in the GBR models were obtained based on impurity-based feature importance (mean decrease in impurity). High-ranked parameters were hypothesized to be either the true causal chemical properties underlying the trait, to correlate with the actual causal properties, or to take part in sensory interactions affecting the trait 67 (Fig.  4A ). In a second approach, we used SHAP 68 to determine which parameters contributed most to the model for making predictions of consumer appreciation (Fig.  4B ). SHAP calculates parameter contributions to model predictions on a per-sample basis, which can be aggregated into an importance score.

A The impurity-based feature importance (mean deviance in impurity, MDI) calculated from the Gradient Boosting Regression (GBR) model predicting RateBeer appreciation scores. The top 15 highest ranked chemical properties are shown. B SHAP summary plot for the top 15 parameters contributing to our GBR model. Each point on the graph represents a sample from our dataset. The color represents the concentration of that parameter, with bluer colors representing low values and redder colors representing higher values. Greater absolute values on the horizontal axis indicate a higher impact of the parameter on the prediction of the model. C Spearman correlations between the 15 most important chemical properties and consumer overall appreciation. Numbers indicate the Spearman Rho correlation coefficient, and the rank of this correlation compared to all other correlations. The top 15 important compounds were determined using SHAP (panel B).

Both approaches identified ethyl acetate as the most predictive parameter for beer appreciation (Fig.  4 ). Ethyl acetate is the most abundant ester in beer with a typical ‘fruity’, ‘solvent’ and ‘alcoholic’ flavor, but is often considered less important than other esters like isoamyl acetate. The second most important parameter identified by SHAP is ethanol, the most abundant beer compound after water. Apart from directly contributing to beer flavor and mouthfeel, ethanol drastically influences the physical properties of beer, dictating how easily volatile compounds escape the beer matrix to contribute to beer aroma 69 . Importantly, it should also be noted that the importance of ethanol for appreciation is likely inflated by the very low appreciation scores of non-alcoholic beers (Supplementary Fig.  S4 ). Despite not often being considered a driver of beer appreciation, protein level also ranks highly in both approaches, possibly due to its effect on mouthfeel and body 70 . Lactic acid, which contributes to the tart taste of sour beers, is the fourth most important parameter identified by SHAP, possibly due to the generally high appreciation of sour beers in our dataset.

Interestingly, some of the most important predictive parameters for our model are not well-established as beer flavors or are even commonly regarded as being negative for beer quality. For example, our models identify methanethiol and ethyl phenyl acetate, an ester commonly linked to beer staling 71 , as a key factor contributing to beer appreciation. Although there is no doubt that high concentrations of these compounds are considered unpleasant, the positive effects of modest concentrations are not yet known 72 , 73 .

To compare our approach to conventional statistics, we evaluated how well the 15 most important SHAP-derived parameters correlate with consumer appreciation (Fig.  4C ). Interestingly, only 6 of the properties derived by SHAP rank amongst the top 15 most correlated parameters. For some chemical compounds, the correlations are so low that they would have likely been considered unimportant. For example, lactic acid, the fourth most important parameter, shows a bimodal distribution for appreciation, with sour beers forming a separate cluster, that is missed entirely by the Spearman correlation. Additionally, the correlation plots reveal outliers, emphasizing the need for robust analysis tools. Together, this highlights the need for alternative models, like the Gradient Boosting model, that better grasp the complexity of (beer) flavor.

Finally, to observe the relationships between these chemical properties and their predicted targets, partial dependence plots were constructed for the six most important predictors of consumer appreciation 74 , 75 , 76 (Supplementary Fig.  S7 ). One-way partial dependence plots show how a change in concentration affects the predicted appreciation. These plots reveal an important limitation of our models: appreciation predictions remain constant at ever-increasing concentrations. This implies that once a threshold concentration is reached, further increasing the concentration does not affect appreciation. This is false, as it is well-documented that certain compounds become unpleasant at high concentrations, including ethyl acetate (‘nail polish’) 77 and methanethiol (‘sulfury’ and ‘rotten cabbage’) 78 . The inability of our models to grasp that flavor compounds have optimal levels, above which they become negative, is a consequence of working with commercial beer brands where (off-)flavors are rarely too high to negatively impact the product. The two-way partial dependence plots show how changing the concentration of two compounds influences predicted appreciation, visualizing their interactions (Supplementary Fig.  S7 ). In our case, the top 5 parameters are dominated by additive or synergistic interactions, with high concentrations for both compounds resulting in the highest predicted appreciation.

To assess the robustness of our best-performing models and model predictions, we performed 100 iterations of the GBR, RF and ET models. In general, all iterations of the models yielded similar performance (Supplementary Fig.  S8 ). Moreover, the main predictors (including the top predictors ethanol and ethyl acetate) remained virtually the same, especially for GBR and RF. For the iterations of the ET model, we did observe more variation in the top predictors, which is likely a consequence of the model’s inherent random architecture in combination with co-correlations between certain predictors. However, even in this case, several of the top predictors (ethanol and ethyl acetate) remain unchanged, although their rank in importance changes (Supplementary Fig.  S8 ).

Next, we investigated if a combination of RateBeer and trained panel data into one consolidated dataset would lead to stronger models, under the hypothesis that such a model would suffer less from bias in the datasets. A GBR model was trained to predict appreciation on the combined dataset. This model underperformed compared to the RateBeer model, both in the native case and when including a dataset identifier (R 2  = 0.67, 0.26 and 0.42 respectively). For the latter, the dataset identifier is the most important feature (Supplementary Fig.  S9 ), while most of the feature importance remains unchanged, with ethyl acetate and ethanol ranking highest, like in the original model trained only on RateBeer data. It seems that the large variation in the panel dataset introduces noise, weakening the models’ performances and reliability. In addition, it seems reasonable to assume that both datasets are fundamentally different, with the panel dataset obtained by blind tastings by a trained professional panel.

Lastly, we evaluated whether beer style identifiers would further enhance the model’s performance. A GBR model was trained with parameters that explicitly encoded the styles of the samples. This did not improve model performance (R2 = 0.66 with style information vs R2 = 0.67). The most important chemical features are consistent with the model trained without style information (eg. ethanol and ethyl acetate), and with the exception of the most preferred (strong ale) and least preferred (low/no-alcohol) styles, none of the styles were among the most important features (Supplementary Fig.  S9 , Supplementary Table  S5 and S6 ). This is likely due to a combination of style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original models, as well as the low number of samples belonging to some styles, making it difficult for the model to learn style-specific patterns. Moreover, beer styles are not rigorously defined, with some styles overlapping in features and some beers being misattributed to a specific style, all of which leads to more noise in models that use style parameters.

Model validation

To test if our predictive models give insight into beer appreciation, we set up experiments aimed at improving existing commercial beers. We specifically selected overall appreciation as the trait to be examined because of its complexity and commercial relevance. Beer flavor comprises a complex bouquet rather than single aromas and tastes 53 . Hence, adding a single compound to the extent that a difference is noticeable may lead to an unbalanced, artificial flavor. Therefore, we evaluated the effect of combinations of compounds. Because Blond beers represent the most extensive style in our dataset, we selected a beer from this style as the starting material for these experiments (Beer 64 in Supplementary Data  1 ).

In the first set of experiments, we adjusted the concentrations of compounds that made up the most important predictors of overall appreciation (ethyl acetate, ethanol, lactic acid, ethyl phenyl acetate) together with correlated compounds (ethyl hexanoate, isoamyl acetate, glycerol), bringing them up to 95 th percentile ethanol-normalized concentrations (Methods) within the Blond group (‘Spiked’ concentration in Fig.  5A ). Compared to controls, the spiked beers were found to have significantly improved overall appreciation among trained panelists, with panelist noting increased intensity of ester flavors, sweetness, alcohol, and body fullness (Fig.  5B ). To disentangle the contribution of ethanol to these results, a second experiment was performed without the addition of ethanol. This resulted in a similar outcome, including increased perception of alcohol and overall appreciation.

Adding the top chemical compounds, identified as best predictors of appreciation by our model, into poorly appreciated beers results in increased appreciation from our trained panel. Results of sensory tests between base beers and those spiked with compounds identified as the best predictors by the model. A Blond and Non/Low-alcohol (0.0% ABV) base beers were brought up to 95th-percentile ethanol-normalized concentrations within each style. B For each sensory attribute, tasters indicated the more intense sample and selected the sample they preferred. The numbers above the bars correspond to the p values that indicate significant changes in perceived flavor (two-sided binomial test: alpha 0.05, n  = 20 or 13).

In a last experiment, we tested whether using the model’s predictions can boost the appreciation of a non-alcoholic beer (beer 223 in Supplementary Data  1 ). Again, the addition of a mixture of predicted compounds (omitting ethanol, in this case) resulted in a significant increase in appreciation, body, ester flavor and sweetness.

Predicting flavor and consumer appreciation from chemical composition is one of the ultimate goals of sensory science. A reliable, systematic and unbiased way to link chemical profiles to flavor and food appreciation would be a significant asset to the food and beverage industry. Such tools would substantially aid in quality control and recipe development, offer an efficient and cost-effective alternative to pilot studies and consumer trials and would ultimately allow food manufacturers to produce superior, tailor-made products that better meet the demands of specific consumer groups more efficiently.

A limited set of studies have previously tried, to varying degrees of success, to predict beer flavor and beer popularity based on (a limited set of) chemical compounds and flavors 79 , 80 . Current sensitive, high-throughput technologies allow measuring an unprecedented number of chemical compounds and properties in a large set of samples, yielding a dataset that can train models that help close the gaps between chemistry and flavor, even for a complex natural product like beer. To our knowledge, no previous research gathered data at this scale (250 samples, 226 chemical parameters, 50 sensory attributes and 5 consumer scores) to disentangle and validate the chemical aspects driving beer preference using various machine-learning techniques. We find that modern machine learning models outperform conventional statistical tools, such as correlations and linear models, and can successfully predict flavor appreciation from chemical composition. This could be attributed to the natural incorporation of interactions and non-linear or discontinuous effects in machine learning models, which are not easily grasped by the linear model architecture. While linear models and partial least squares regression represent the most widespread statistical approaches in sensory science, in part because they allow interpretation 65 , 81 , 82 , modern machine learning methods allow for building better predictive models while preserving the possibility to dissect and exploit the underlying patterns. Of the 10 different models we trained, tree-based models, such as our best performing GBR, showed the best overall performance in predicting sensory responses from chemical information, outcompeting artificial neural networks. This agrees with previous reports for models trained on tabular data 83 . Our results are in line with the findings of Colantonio et al. who also identified the gradient boosting architecture as performing best at predicting appreciation and flavor (of tomatoes and blueberries, in their specific study) 26 . Importantly, besides our larger experimental scale, we were able to directly confirm our models’ predictions in vivo.

Our study confirms that flavor compound concentration does not always correlate with perception, suggesting complex interactions that are often missed by more conventional statistics and simple models. Specifically, we find that tree-based algorithms may perform best in developing models that link complex food chemistry with aroma. Furthermore, we show that massive datasets of untrained consumer reviews provide a valuable source of data, that can complement or even replace trained tasting panels, especially for appreciation and basic flavors, such as sweetness and bitterness. This holds despite biases that are known to occur in such datasets, such as price or conformity bias. Moreover, GBR models predict taste better than aroma. This is likely because taste (e.g. bitterness) often directly relates to the corresponding chemical measurements (e.g., iso-alpha acids), whereas such a link is less clear for aromas, which often result from the interplay between multiple volatile compounds. We also find that our models are best at predicting acidity and alcohol, likely because there is a direct relation between the measured chemical compounds (acids and ethanol) and the corresponding perceived sensorial attribute (acidity and alcohol), and because even untrained consumers are generally able to recognize these flavors and aromas.

The predictions of our final models, trained on review data, hold even for blind tastings with small groups of trained tasters, as demonstrated by our ability to validate specific compounds as drivers of beer flavor and appreciation. Since adding a single compound to the extent of a noticeable difference may result in an unbalanced flavor profile, we specifically tested our identified key drivers as a combination of compounds. While this approach does not allow us to validate if a particular single compound would affect flavor and/or appreciation, our experiments do show that this combination of compounds increases consumer appreciation.

It is important to stress that, while it represents an important step forward, our approach still has several major limitations. A key weakness of the GBR model architecture is that amongst co-correlating variables, the largest main effect is consistently preferred for model building. As a result, co-correlating variables often have artificially low importance scores, both for impurity and SHAP-based methods, like we observed in the comparison to the more randomized Extra Trees models. This implies that chemicals identified as key drivers of a specific sensory feature by GBR might not be the true causative compounds, but rather co-correlate with the actual causative chemical. For example, the high importance of ethyl acetate could be (partially) attributed to the total ester content, ethanol or ethyl hexanoate (rho=0.77, rho=0.72 and rho=0.68), while ethyl phenylacetate could hide the importance of prenyl isobutyrate and ethyl benzoate (rho=0.77 and rho=0.76). Expanding our GBR model to include beer style as a parameter did not yield additional power or insight. This is likely due to style-specific chemical signatures, such as iso-alpha acids and lactic acid, that implicitly convey style information to the original model, as well as the smaller sample size per style, limiting the power to uncover style-specific patterns. This can be partly attributed to the curse of dimensionality, where the high number of parameters results in the models mainly incorporating single parameter effects, rather than complex interactions such as style-dependent effects 67 . A larger number of samples may overcome some of these limitations and offer more insight into style-specific effects. On the other hand, beer style is not a rigid scientific classification, and beers within one style often differ a lot, which further complicates the analysis of style as a model factor.

Our study is limited to beers from Belgian breweries. Although these beers cover a large portion of the beer styles available globally, some beer styles and consumer patterns may be missing, while other features might be overrepresented. For example, many Belgian ales exhibit yeast-driven flavor profiles, which is reflected in the chemical drivers of appreciation discovered by this study. In future work, expanding the scope to include diverse markets and beer styles could lead to the identification of even more drivers of appreciation and better models for special niche products that were not present in our beer set.

In addition to inherent limitations of GBR models, there are also some limitations associated with studying food aroma. Even if our chemical analyses measured most of the known aroma compounds, the total number of flavor compounds in complex foods like beer is still larger than the subset we were able to measure in this study. For example, hop-derived thiols, that influence flavor at very low concentrations, are notoriously difficult to measure in a high-throughput experiment. Moreover, consumer perception remains subjective and prone to biases that are difficult to avoid. It is also important to stress that the models are still immature and that more extensive datasets will be crucial for developing more complete models in the future. Besides more samples and parameters, our dataset does not include any demographic information about the tasters. Including such data could lead to better models that grasp external factors like age and culture. Another limitation is that our set of beers consists of high-quality end-products and lacks beers that are unfit for sale, which limits the current model in accurately predicting products that are appreciated very badly. Finally, while models could be readily applied in quality control, their use in sensory science and product development is restrained by their inability to discern causal relationships. Given that the models cannot distinguish compounds that genuinely drive consumer perception from those that merely correlate, validation experiments are essential to identify true causative compounds.

Despite the inherent limitations, dissection of our models enabled us to pinpoint specific molecules as potential drivers of beer aroma and consumer appreciation, including compounds that were unexpected and would not have been identified using standard approaches. Important drivers of beer appreciation uncovered by our models include protein levels, ethyl acetate, ethyl phenyl acetate and lactic acid. Currently, many brewers already use lactic acid to acidify their brewing water and ensure optimal pH for enzymatic activity during the mashing process. Our results suggest that adding lactic acid can also improve beer appreciation, although its individual effect remains to be tested. Interestingly, ethanol appears to be unnecessary to improve beer appreciation, both for blond beer and alcohol-free beer. Given the growing consumer interest in alcohol-free beer, with a predicted annual market growth of >7% 84 , it is relevant for brewers to know what compounds can further increase consumer appreciation of these beers. Hence, our model may readily provide avenues to further improve the flavor and consumer appreciation of both alcoholic and non-alcoholic beers, which is generally considered one of the key challenges for future beer production.

Whereas we see a direct implementation of our results for the development of superior alcohol-free beverages and other food products, our study can also serve as a stepping stone for the development of novel alcohol-containing beverages. We want to echo the growing body of scientific evidence for the negative effects of alcohol consumption, both on the individual level by the mutagenic, teratogenic and carcinogenic effects of ethanol 85 , 86 , as well as the burden on society caused by alcohol abuse and addiction. We encourage the use of our results for the production of healthier, tastier products, including novel and improved beverages with lower alcohol contents. Furthermore, we strongly discourage the use of these technologies to improve the appreciation or addictive properties of harmful substances.

The present work demonstrates that despite some important remaining hurdles, combining the latest developments in chemical analyses, sensory analysis and modern machine learning methods offers exciting avenues for food chemistry and engineering. Soon, these tools may provide solutions in quality control and recipe development, as well as new approaches to sensory science and flavor research.

Beer selection

250 commercial Belgian beers were selected to cover the broad diversity of beer styles and corresponding diversity in chemical composition and aroma. See Supplementary Fig.  S1 .

Chemical dataset

Sample preparation.

Beers within their expiration date were purchased from commercial retailers. Samples were prepared in biological duplicates at room temperature, unless explicitly stated otherwise. Bottle pressure was measured with a manual pressure device (Steinfurth Mess-Systeme GmbH) and used to calculate CO 2 concentration. The beer was poured through two filter papers (Macherey-Nagel, 500713032 MN 713 ¼) to remove carbon dioxide and prevent spontaneous foaming. Samples were then prepared for measurements by targeted Headspace-Gas Chromatography-Flame Ionization Detector/Flame Photometric Detector (HS-GC-FID/FPD), Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), colorimetric analysis, enzymatic analysis, Near-Infrared (NIR) analysis, as described in the sections below. The mean values of biological duplicates are reported for each compound.

HS-GC-FID/FPD

HS-GC-FID/FPD (Shimadzu GC 2010 Plus) was used to measure higher alcohols, acetaldehyde, esters, 4-vinyl guaicol, and sulfur compounds. Each measurement comprised 5 ml of sample pipetted into a 20 ml glass vial containing 1.75 g NaCl (VWR, 27810.295). 100 µl of 2-heptanol (Sigma-Aldrich, H3003) (internal standard) solution in ethanol (Fisher Chemical, E/0650DF/C17) was added for a final concentration of 2.44 mg/L. Samples were flushed with nitrogen for 10 s, sealed with a silicone septum, stored at −80 °C and analyzed in batches of 20.

The GC was equipped with a DB-WAXetr column (length, 30 m; internal diameter, 0.32 mm; layer thickness, 0.50 µm; Agilent Technologies, Santa Clara, CA, USA) to the FID and an HP-5 column (length, 30 m; internal diameter, 0.25 mm; layer thickness, 0.25 µm; Agilent Technologies, Santa Clara, CA, USA) to the FPD. N 2 was used as the carrier gas. Samples were incubated for 20 min at 70 °C in the headspace autosampler (Flow rate, 35 cm/s; Injection volume, 1000 µL; Injection mode, split; Combi PAL autosampler, CTC analytics, Switzerland). The injector, FID and FPD temperatures were kept at 250 °C. The GC oven temperature was first held at 50 °C for 5 min and then allowed to rise to 80 °C at a rate of 5 °C/min, followed by a second ramp of 4 °C/min until 200 °C kept for 3 min and a final ramp of (4 °C/min) until 230 °C for 1 min. Results were analyzed with the GCSolution software version 2.4 (Shimadzu, Kyoto, Japan). The GC was calibrated with a 5% EtOH solution (VWR International) containing the volatiles under study (Supplementary Table  S7 ).

HS-SPME-GC-MS

HS-SPME-GC-MS (Shimadzu GCMS-QP-2010 Ultra) was used to measure additional volatile compounds, mainly comprising terpenoids and esters. Samples were analyzed by HS-SPME using a triphase DVB/Carboxen/PDMS 50/30 μm SPME fiber (Supelco Co., Bellefonte, PA, USA) followed by gas chromatography (Thermo Fisher Scientific Trace 1300 series, USA) coupled to a mass spectrometer (Thermo Fisher Scientific ISQ series MS) equipped with a TriPlus RSH autosampler. 5 ml of degassed beer sample was placed in 20 ml vials containing 1.75 g NaCl (VWR, 27810.295). 5 µl internal standard mix was added, containing 2-heptanol (1 g/L) (Sigma-Aldrich, H3003), 4-fluorobenzaldehyde (1 g/L) (Sigma-Aldrich, 128376), 2,3-hexanedione (1 g/L) (Sigma-Aldrich, 144169) and guaiacol (1 g/L) (Sigma-Aldrich, W253200) in ethanol (Fisher Chemical, E/0650DF/C17). Each sample was incubated at 60 °C in the autosampler oven with constant agitation. After 5 min equilibration, the SPME fiber was exposed to the sample headspace for 30 min. The compounds trapped on the fiber were thermally desorbed in the injection port of the chromatograph by heating the fiber for 15 min at 270 °C.

The GC-MS was equipped with a low polarity RXi-5Sil MS column (length, 20 m; internal diameter, 0.18 mm; layer thickness, 0.18 µm; Restek, Bellefonte, PA, USA). Injection was performed in splitless mode at 320 °C, a split flow of 9 ml/min, a purge flow of 5 ml/min and an open valve time of 3 min. To obtain a pulsed injection, a programmed gas flow was used whereby the helium gas flow was set at 2.7 mL/min for 0.1 min, followed by a decrease in flow of 20 ml/min to the normal 0.9 mL/min. The temperature was first held at 30 °C for 3 min and then allowed to rise to 80 °C at a rate of 7 °C/min, followed by a second ramp of 2 °C/min till 125 °C and a final ramp of 8 °C/min with a final temperature of 270 °C.

Mass acquisition range was 33 to 550 amu at a scan rate of 5 scans/s. Electron impact ionization energy was 70 eV. The interface and ion source were kept at 275 °C and 250 °C, respectively. A mix of linear n-alkanes (from C7 to C40, Supelco Co.) was injected into the GC-MS under identical conditions to serve as external retention index markers. Identification and quantification of the compounds were performed using an in-house developed R script as described in Goelen et al. and Reher et al. 87 , 88 (for package information, see Supplementary Table  S8 ). Briefly, chromatograms were analyzed using AMDIS (v2.71) 89 to separate overlapping peaks and obtain pure compound spectra. The NIST MS Search software (v2.0 g) in combination with the NIST2017, FFNSC3 and Adams4 libraries were used to manually identify the empirical spectra, taking into account the expected retention time. After background subtraction and correcting for retention time shifts between samples run on different days based on alkane ladders, compound elution profiles were extracted and integrated using a file with 284 target compounds of interest, which were either recovered in our identified AMDIS list of spectra or were known to occur in beer. Compound elution profiles were estimated for every peak in every chromatogram over a time-restricted window using weighted non-negative least square analysis after which peak areas were integrated 87 , 88 . Batch effect correction was performed by normalizing against the most stable internal standard compound, 4-fluorobenzaldehyde. Out of all 284 target compounds that were analyzed, 167 were visually judged to have reliable elution profiles and were used for final analysis.

Discrete photometric and enzymatic analysis

Discrete photometric and enzymatic analysis (Thermo Scientific TM Gallery TM Plus Beermaster Discrete Analyzer) was used to measure acetic acid, ammonia, beta-glucan, iso-alpha acids, color, sugars, glycerol, iron, pH, protein, and sulfite. 2 ml of sample volume was used for the analyses. Information regarding the reagents and standard solutions used for analyses and calibrations is included in Supplementary Table  S7 and Supplementary Table  S9 .

NIR analyses

NIR analysis (Anton Paar Alcolyzer Beer ME System) was used to measure ethanol. Measurements comprised 50 ml of sample, and a 10% EtOH solution was used for calibration.

Correlation calculations

Pairwise Spearman Rank correlations were calculated between all chemical properties.

Sensory dataset

Trained panel.

Our trained tasting panel consisted of volunteers who gave prior verbal informed consent. All compounds used for the validation experiment were of food-grade quality. The tasting sessions were approved by the Social and Societal Ethics Committee of the KU Leuven (G-2022-5677-R2(MAR)). All online reviewers agreed to the Terms and Conditions of the RateBeer website.

Sensory analysis was performed according to the American Society of Brewing Chemists (ASBC) Sensory Analysis Methods 90 . 30 volunteers were screened through a series of triangle tests. The sixteen most sensitive and consistent tasters were retained as taste panel members. The resulting panel was diverse in age [22–42, mean: 29], sex [56% male] and nationality [7 different countries]. The panel developed a consensus vocabulary to describe beer aroma, taste and mouthfeel. Panelists were trained to identify and score 50 different attributes, using a 7-point scale to rate attributes’ intensity. The scoring sheet is included as Supplementary Data  3 . Sensory assessments took place between 10–12 a.m. The beers were served in black-colored glasses. Per session, between 5 and 12 beers of the same style were tasted at 12 °C to 16 °C. Two reference beers were added to each set and indicated as ‘Reference 1 & 2’, allowing panel members to calibrate their ratings. Not all panelists were present at every tasting. Scores were scaled by standard deviation and mean-centered per taster. Values are represented as z-scores and clustered by Euclidean distance. Pairwise Spearman correlations were calculated between taste and aroma sensory attributes. Panel consistency was evaluated by repeating samples on different sessions and performing ANOVA to identify differences, using the ‘stats’ package (v4.2.2) in R (for package information, see Supplementary Table  S8 ).

Online reviews from a public database

The ‘scrapy’ package in Python (v3.6) (for package information, see Supplementary Table  S8 ). was used to collect 232,288 online reviews (mean=922, min=6, max=5343) from RateBeer, an online beer review database. Each review entry comprised 5 numerical scores (appearance, aroma, taste, palate and overall quality) and an optional review text. The total number of reviews per reviewer was collected separately. Numerical scores were scaled and centered per rater, and mean scores were calculated per beer.

For the review texts, the language was estimated using the packages ‘langdetect’ and ‘langid’ in Python. Reviews that were classified as English by both packages were kept. Reviewers with fewer than 100 entries overall were discarded. 181,025 reviews from >6000 reviewers from >40 countries remained. Text processing was done using the ‘nltk’ package in Python. Texts were corrected for slang and misspellings; proper nouns and rare words that are relevant to the beer context were specified and kept as-is (‘Chimay’,’Lambic’, etc.). A dictionary of semantically similar sensorial terms, for example ‘floral’ and ‘flower’, was created and collapsed together into one term. Words were stemmed and lemmatized to avoid identifying words such as ‘acid’ and ‘acidity’ as separate terms. Numbers and punctuation were removed.

Sentences from up to 50 randomly chosen reviews per beer were manually categorized according to the aspect of beer they describe (appearance, aroma, taste, palate, overall quality—not to be confused with the 5 numerical scores described above) or flagged as irrelevant if they contained no useful information. If a beer contained fewer than 50 reviews, all reviews were manually classified. This labeled data set was used to train a model that classified the rest of the sentences for all beers 91 . Sentences describing taste and aroma were extracted, and term frequency–inverse document frequency (TFIDF) was implemented to calculate enrichment scores for sensorial words per beer.

The sex of the tasting subject was not considered when building our sensory database. Instead, results from different panelists were averaged, both for our trained panel (56% male, 44% female) and the RateBeer reviews (70% male, 30% female for RateBeer as a whole).

Beer price collection and processing

Beer prices were collected from the following stores: Colruyt, Delhaize, Total Wine, BeerHawk, The Belgian Beer Shop, The Belgian Shop, and Beer of Belgium. Where applicable, prices were converted to Euros and normalized per liter. Spearman correlations were calculated between these prices and mean overall appreciation scores from RateBeer and the taste panel, respectively.

Pairwise Spearman Rank correlations were calculated between all sensory properties.

Machine learning models

Predictive modeling of sensory profiles from chemical data.

Regression models were constructed to predict (a) trained panel scores for beer flavors and quality from beer chemical profiles and (b) public reviews’ appreciation scores from beer chemical profiles. Z-scores were used to represent sensory attributes in both data sets. Chemical properties with log-normal distributions (Shapiro-Wilk test, p  <  0.05 ) were log-transformed. Missing chemical measurements (0.1% of all data) were replaced with mean values per attribute. Observations from 250 beers were randomly separated into a training set (70%, 175 beers) and a test set (30%, 75 beers), stratified per beer style. Chemical measurements (p = 231) were normalized based on the training set average and standard deviation. In total, three linear regression-based models: linear regression with first-order interaction terms (LR), lasso regression with first-order interaction terms (Lasso) and partial least squares regression (PLSR); five decision tree models, Adaboost regressor (ABR), Extra Trees (ET), Gradient Boosting regressor (GBR), Random Forest (RF) and XGBoost regressor (XGBR); one support vector machine model (SVR) and one artificial neural network model (ANN) were trained. The models were implemented using the ‘scikit-learn’ package (v1.2.2) and ‘xgboost’ package (v1.7.3) in Python (v3.9.16). Models were trained, and hyperparameters optimized, using five-fold cross-validated grid search with the coefficient of determination (R 2 ) as the evaluation metric. The ANN (scikit-learn’s MLPRegressor) was optimized using Bayesian Tree-Structured Parzen Estimator optimization with the ‘Optuna’ Python package (v3.2.0). Individual models were trained per attribute, and a multi-output model was trained on all attributes simultaneously.

Model dissection

GBR was found to outperform other methods, resulting in models with the highest average R 2 values in both trained panel and public review data sets. Impurity-based rankings of the most important predictors for each predicted sensorial trait were obtained using the ‘scikit-learn’ package. To observe the relationships between these chemical properties and their predicted targets, partial dependence plots (PDP) were constructed for the six most important predictors of consumer appreciation 74 , 75 .

The ‘SHAP’ package in Python (v0.41.0) was implemented to provide an alternative ranking of predictor importance and to visualize the predictors’ effects as a function of their concentration 68 .

Validation of causal chemical properties

To validate the effects of the most important model features on predicted sensory attributes, beers were spiked with the chemical compounds identified by the models and descriptive sensory analyses were carried out according to the American Society of Brewing Chemists (ASBC) protocol 90 .

Compound spiking was done 30 min before tasting. Compounds were spiked into fresh beer bottles, that were immediately resealed and inverted three times. Fresh bottles of beer were opened for the same duration, resealed, and inverted thrice, to serve as controls. Pairs of spiked samples and controls were served simultaneously, chilled and in dark glasses as outlined in the Trained panel section above. Tasters were instructed to select the glass with the higher flavor intensity for each attribute (directional difference test 92 ) and to select the glass they prefer.

The final concentration after spiking was equal to the within-style average, after normalizing by ethanol concentration. This was done to ensure balanced flavor profiles in the final spiked beer. The same methods were applied to improve a non-alcoholic beer. Compounds were the following: ethyl acetate (Merck KGaA, W241415), ethyl hexanoate (Merck KGaA, W243906), isoamyl acetate (Merck KGaA, W205508), phenethyl acetate (Merck KGaA, W285706), ethanol (96%, Colruyt), glycerol (Merck KGaA, W252506), lactic acid (Merck KGaA, 261106).

Significant differences in preference or perceived intensity were determined by performing the two-sided binomial test on each attribute.

Reporting summary

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

Data availability

The data that support the findings of this work are available in the Supplementary Data files and have been deposited to Zenodo under accession code 10653704 93 . The RateBeer scores data are under restricted access, they are not publicly available as they are property of RateBeer (ZX Ventures, USA). Access can be obtained from the authors upon reasonable request and with permission of RateBeer (ZX Ventures, USA).  Source data are provided with this paper.

Code availability

The code for training the machine learning models, analyzing the models, and generating the figures has been deposited to Zenodo under accession code 10653704 93 .

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Acknowledgements

We thank all lab members for their discussions and thank all tasting panel members for their contributions. Special thanks go out to Dr. Karin Voordeckers for her tremendous help in proofreading and improving the manuscript. M.S. was supported by a Baillet-Latour fellowship, L.C. acknowledges financial support from KU Leuven (C16/17/006), F.A.T. was supported by a PhD fellowship from FWO (1S08821N). Research in the lab of K.J.V. is supported by KU Leuven, FWO, VIB, VLAIO and the Brewing Science Serves Health Fund. Research in the lab of T.W. is supported by FWO (G.0A51.15) and KU Leuven (C16/17/006).

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These authors contributed equally: Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni.

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VIB—KU Leuven Center for Microbiology, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Michiel Schreurs, Supinya Piampongsant, Miguel Roncoroni, Lloyd Cool, Beatriz Herrera-Malaver, Florian A. Theßeling & Kevin J. Verstrepen

CMPG Laboratory of Genetics and Genomics, KU Leuven, Gaston Geenslaan 1, B-3001, Leuven, Belgium

Leuven Institute for Beer Research (LIBR), Gaston Geenslaan 1, B-3001, Leuven, Belgium

Laboratory of Socioecology and Social Evolution, KU Leuven, Naamsestraat 59, B-3000, Leuven, Belgium

Lloyd Cool, Christophe Vanderaa & Tom Wenseleers

VIB Bioinformatics Core, VIB, Rijvisschestraat 120, B-9052, Ghent, Belgium

Łukasz Kreft & Alexander Botzki

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S.P., M.S. and K.J.V. conceived the experiments. S.P., M.S. and K.J.V. designed the experiments. S.P., M.S., M.R., B.H. and F.A.T. performed the experiments. S.P., M.S., L.C., C.V., L.K., A.B., P.M., L.D., T.W. and K.J.V. contributed analysis ideas. S.P., M.S., L.C., C.V., T.W. and K.J.V. analyzed the data. All authors contributed to writing the manuscript.

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Schreurs, M., Piampongsant, S., Roncoroni, M. et al. Predicting and improving complex beer flavor through machine learning. Nat Commun 15 , 2368 (2024). https://doi.org/10.1038/s41467-024-46346-0

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Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences

Authors of this article:

Original Paper

• Masaru Kamba, PhD   ; 
• Wan Jou She, PhD   ; 
• Kiki Ferawati, MStat   ; 
• Shoko Wakamiya, PhD   ; 
• Eiji Aramaki, PhD  

Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan

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Eiji Aramaki, PhD

Division of Information Science

Graduate School of Science and Technology

Nara Institute of Science and Technology

8916-5 Takayama-cho

Ikoma, 630-0192

Phone: 81 0743 72 5250

Email: [email protected]

Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field.

Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals’ self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan.

Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei (“due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words.

Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony , cancel , school , work , and event . The top 5 verbs were disappear , go , rest , can go , and end . Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education.

Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens’ perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making.

Introduction

The spread of COVID-19 has become a global pandemic, significantly affecting social and economic sectors worldwide [ 1 ]. In the early stages of the pandemic, health authorities recommended social distancing to control the spread of the virus, reduce cases, and avoid overwhelming health care facilities [ 2 - 4 ]. Each country had its own strategy for dealing with COVID-19. A survey conducted across 6 countries illustrated the public’s perception of measures taken in response to COVID-19 [ 5 ]. Other surveys have been conducted in the United Kingdom and European countries to aid interdisciplinary research on public health, particularly regarding COVID-19 [ 6 ]. Different results were observed owing to social distancing policies, which affected several aspects of life, including economic activities [ 7 ] and consumer behavior, such as drops in mobility [ 8 ]. Concerns about cybersecurity risks were also raised, as companies might not have been prepared for adequate work-from-home options for employees [ 9 ]. The association between implementing some mitigation policies in response to COVID-19 and the outcomes regarding public mobility were noted [ 10 ], one of which was also observed in Japan.

After the government confirmed the first COVID-19 case in Japan on January 16, 2020, the number of cases quickly escalated within 3 months, leading to the declaration of a state of emergency to prevent the further spread of the infection. This measure significantly impacted the daily routines and social lives of Japanese residents, forcing them to refrain from going out, close schools, work from home, and be restricted from visiting crowded locations such as department stores and movie theaters. The first state of emergency effectively reduced the number of COVID-19 cases [ 11 ], albeit at a high cost to public mental well-being, education quality, and the economy. Furthermore, the number of cases quickly bounced back, peaking at 1762 new daily cases after the state of emergency was lifted, an increase from the peak of 701 new daily cases during the first wave [ 11 ]. These numbers suggest that the government was confronted with the dilemma of mitigating the social and economic impact of the lockdown and stopping the spread of COVID-19 [ 12 ]. Due to the fluctuations in COVID-19 cases, the government declared other states of emergency, recognizing the profound and deeply rooted impact the COVID-19 lockdown could have on societal and economic levels.

There have been various investigations into the states of emergency. For instance, studies have predicted SARS-CoV-2 infections using state-space models [ 13 ] and examined their impact on mental health [ 14 ]. In the aspect of mobility, studies have shown the suppression of social activities of the masses [ 15 ]. The tourism industry was among the hardest hit sectors, and the arrival of visitors decreased by 93% by March 2020 [ 16 ]. Statistics also show that Japan’s gross domestic product in 2020 decreased by 4.28%, indicating a substantial impact on the economy [ 17 ]. Interestingly, the unemployment rate only slightly increased to 2.8% in 2020, but started declining by 2022 (2.64%), following the gradual recovery of the gross domestic product (2.14% growth by 2021 and 1.03% by 2022) [ 17 , 18 ]. This trend of recovery indicates the strong resilience of the Japanese economy.

Furthermore, it also changed people’s behavior, such as following the advisory to stay at home, as confirmed by cell phone location data [ 19 - 21 ]. Such large-scale societal and behavioral changes warrant further investigation through various means to offer a chance to monitor and reflect the short- and long-term impacts of COVID-19 in the future.

Literature Review

The disruption caused by pandemic-related restrictions may be seen as a failure to perform planned activities, but detecting such disruptions was challenging. For example, it is difficult to obtain behavioral data on trips that individuals could not take or events they could not attend owing to the restriction. Social media, which people use to share their activities, proved to be a great source of information in such cases. Twitter (currently X) is a widely used social media platform in many countries and has a sufficiently large population for social data analysis in health care contexts [ 22 , 23 ]. Japan has a particularly high population density of Twitter users, even when compared to the major countries that use Twitter, such as the United States. Furthermore, owing to language exclusivity, it is easier to filter comments related to Japanese society using Japanese keywords [ 24 ]. Twitter has also been frequently used to help summarize peoples’ responses about the pandemic and its measures, showing the challenges experienced throughout [ 25 ]. Prior studies in Korea and Japan used Twitter to determine public opinion, showing popular words during the pandemic [ 26 ]. Because people actively share their daily lives on Twitter, the site has the potential to be a data source for investigating the impact of restrictions on the public. Using Twitter as a resource, this study aims to explore and visualize plans disrupted in Japan due to COVID-19 pandemic measures.

There are many studies on COVID-19 that investigate social media platforms, such as Twitter. Chen et al [ 27 ] investigated the levels of anxiety during the COVID-19 pandemic. The adverse effects on the mental health of the public was also one of the impacts of the pandemic, as explained in the research by Li et al [ 28 ], who analyzed COVID-19–related tweets into different emotions and investigated the mental health aspects and how they recovered from the COVID-19 crisis. Lyu et al [ 29 ] investigated the topics and sentiments in public COVID-19 vaccine–related discussions, whereas Krittanawong et al [ 30 ] investigated misinformation dissemination related to COVID-19 on Twitter. Aside from studies focused on the pandemic itself, COVID-19 vaccines have also been highly researched topics on Twitter. Ansari and Khan assessed public responses through sentiment analysis of COVID-19 vaccines using Twitter, revealing an overall negative tone in the tweets [ 31 ]. Ferawati et al [ 32 ] explored how Twitter reported vaccine-related side effects by comparing the side effects of 2 types of messenger RNA vaccines developed by Pfizer and Moderna in Japan and Indonesia, respectively. Gao et al [ 33 ] examined COVID-19 concerns in each Japanese prefecture, and Uehara et al [ 34 ] investigated the attitudes toward vaccines or vaccination during the COVID-19 pandemic in different Japanese prefectures using search queries from Yahoo! JAPAN. Our study adopts a unique approach to examine how the COVID-19 pandemic has disrupted everyday activities. Our main focus is on understanding the direct impact of the pandemic on society through the observation of expressions, life disruptions, and plans.

For research on citizen feedback, Ishida et al [ 35 ] proposed a method that uses social media data. They implemented a multitask learning framework to estimate the associated viewpoints using bidirectional encoder representations from the transformer model. However, this method requires considerable effort to label the data. This study uses search queries and validates word co-occurrence to infer the themes of topics discussed during the COVID-19 pandemic in Japan, proposing an efficient and low-resource method for social media analysis.

Objectives and Approach

We aimed to report on the impact of COVID-19 on Japanese society by analyzing public opinions extracted from social media data. Specifically, we focused on the popular term Corona no-sei (in Japanese コロナのせい, meaning “due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), which clearly conveyed complaints or concerns about life event disruptions caused by the COVID-19 pandemic. Our study used 2 types of data: the daily COVID-19 case count and Japanese tweets containing the Japanese phrase Corona no-sei posted on Twitter between February 1, 2020, and November 30, 2021. We analyzed the trends in the number of tweets and COVID-19 cases to quantitatively explore their relationship and the words frequently used in the tweets to qualitatively explore social needs in the first 2 years of the COVID-19 pandemic.

In conclusion, we critically compared our findings with those identified in other similar studies to provide an alternative evidence base for the impact of COVID-19 on Japanese society.

COVID-19 Cases

To track the daily rise in COVID-19 cases, we gathered the number of new positive cases in Japan by manually downloading data from a dedicated COVID-19 site maintained by the NHK, Japan’s national broadcaster [ 36 ]. Our aim was to investigate the correlation between the number of positive cases and the volume of tweets. A total of 1,726,943 COVID-19–positive cases were recorded between February 1, 2020, and November 30, 2021.

Tweets and Keywords Extraction

Another data set for this study includes 300,778 tweets containing the Japanese phrase Corona no-sei during the same period as the recorded COVID-19 cases (between February 1, 2020, and November 30, 2021). We chose this period because by the end of January 2020, the Japanese government had officially established the Japan Anti-Coronavirus National Task Force to actively address the pandemic. In addition, we aimed to include the maximum possible data until the initiation of this study in mid-November 2021. Furthermore, this period also included 3 emergency announcements by the Japanese government, making it a representative period for studying the impact of COVID-19 on Japanese society.

We counted the number of tweets per month and found that the maximum number of tweets was 517,688 in April 2020; the minimum number of tweets was 24,625 in November 2021; and the average number of tweets was 136,717.6. The Corona no-sei phrase is frequently used by the public in social media and everyday conversation to express the (often negative) feelings when Japanese individuals’ activities or life plans were interrupted by the COVID-19 outbreak. Although there are several expressions synonymous with Corona no-sei (eg, “because of the new coronavirus” and “because of COVID-19”), we chose Corona no-sei as a casual expression used by the public in social media and colloquial speech. The tweet data were provided by the NTT DATA Corporation, which has a real-time backup of Japanese firehose data from X Corporation (formerly known as Twitter). Data access was granted to a few collaborative research institutes, including the University of Tokyo, and one of the authors was granted permission to use the self-adaptive classification system to extract the data and keywords [ 37 ].

Although applying a clustering approach for topic modeling can be useful in grasping the topics discussed in the tweets, it does not apply to our context wherein we were targeting COVID-19 as the main subject and aiming to identify the co-occurrence of events. Instead, we extracted co-occurrence nouns and verbs from the obtained Corona no-sei tweets by applying dependency analysis implemented in the system developed by Yoshinaga et al [ 37 - 39 ]. We used the base-phrase chunker to extract all tweets containing the Corona no-sei keyword (“keyword” is bunsetsu in Japanese). The built-in classifier then extracted the relevant verbs, nouns, and verb-noun-pairs for users based on the nonstack dependency parser, which achieved 99.01% accuracy in base-phrase chunking and 92.23% accuracy in dependency parsing [ 37 ]. Researchers who did not use the system and database maintained by the University of Tokyo could use the same tool published by the laboratory Pecco and DepP [ 37 - 40 ]. To avoid overinterpretation, we omitted tweets that described a disruption of plans but did not include COVID-19–related keywords.

Analysis of the Keywords and its Correlation to the COVID-19 Pandemic Trends

The contexts following Corona no-sei , which indicate a high level of negative concern about COVID-19, frequently contain verbs in the negative form and nouns associated with them. By aggregating these nouns and verbs, we extracted information on the restrictions imposed and the events or plans canceled owing to the COVID-19 pandemic. This information enabled us to capture the potential social and psychological impact of disrupted life plans. Note that, by events or plans, we refer to the specific type of occurrences (eg, university entrance exam) rather than a certain event (eg, a pop singer’s concert in 2019). The frequency of nouns and verbs in tweets containing Corona no-sei was counted to identify the restrictions placed on people’s lives.

To investigate the correlation between tweet volumes and COVID-19 cases, we constructed transition diagrams for each. In addition, Pearson correlation coefficients were also calculated. Next, we examined the nouns and verbs co-occurring with Corona no-sei over the entire study period and specifically on the day with the highest tweet activity.

The cross-validation of the keywords and tweet contents was performed by randomly extracting 20 tweets from the top 5 verb and noun pairs and other keyword pairs that were deemed worthy of discussion by the researchers. The tweet contents were further annotated to ensure that they were aligned with the researchers’ interpretations of keywords. We then discussed the themes extracted by analyzing and cross-validating the themes and noteworthy keywords.

Ethical Considerations

This study used publicly available data and did not handle identifiable private information, meaning that it was exempt from Institutional Review Board approval according to the Ethical Guidelines for Research of the Japanese National Government [ 41 ]. The NTT DATA Corporation obtained tweets according to Twitter terms of service and approved the use of the data for this study.

Figure 1 shows the time trend of Corona no-sei tweets (blue line) compared to the trend of positive cases (red line). There were 3 states of emergency announcements within our targeted period between February 1, 2020, and November 30, 2021, which are highlighted in gray in Figure 1 . The number of areas under the state of emergency is indicated by the bar graph in the upper part of the figure because the target areas were changed during each state of emergency. The periods during which the states of emergency were imposed roughly corresponded to an increase in case numbers. Interestingly, the announcement of a state of emergency was highly effective in suppressing the number of cases. Regarding the spike caused by the Tokyo Olympics (which took place between July 23, 2021, and August 8, 2021), the case number quickly dropped to below 5000 per day within 3 months.

As the blue line indicates, the Corona no-sei tweets peaked in March 2020, roughly before the first state of emergency was announced and reached the second highest number when the first state of emergency was imposed. After the first announcement of the state of emergency, the number of tweets using Corona no-sei showed a downward trend until the end of our data collection period. There were a few instances of small increases in Corona no-sei tweets before the second and third states of emergency announcements, but overall, the number of reported plan disruptions never reached the level observed before the first state of emergency announcement. The scatter plot for case numbers and the numbers of Corona no-sei tweets is shown in Figure 2 , with Pearson correlation coefficients of 0.86, 0.93, and 0.61, respectively, for the first, second, and third states of emergency.

When compared with the number of the Corona no-sei tweets during the entire period, the correlation between COVID-19 daily cases and the Corona no-sei was not very evident. We were able to observe a slight increase of Corona no-sei tweets before the case number started rising, but the extent of increase in case numbers was disproportional to the extent of increase in Corona no-sei tweets. Even though the number of cases peaked in September 2021 during the third state of emergency, there was only a slight increase in Corona no-sei tweets compared to the high number of complaints at the very beginning of the COVID-19 pandemic. This indicates that Japanese residents might have adapted to the restrictions or disruptions caused by the COVID-19 pandemic lockdown.

We further investigated the nouns and verbs in the tweets that we sampled. Tables 1 and 2 show the number of tweets for the top 20 nouns and verbs tweeted on February 28, 2020, when the tweet number reached the highest level. Tables 3 and 4 show the top 20 words (nouns and verbs) that co-occurred with Corona no-sei tweets in descending order to highlight the most disrupted activities or plans during our data aggregation period. For nouns, here, Corona was excluded because it was a word included in the query and was clearly the most frequently detected. For nouns, the top 5 most frequently mentioned words were work , abort , home , live , and friends after excluding the words that indicate the grammatical tense. These keywords indicated that, over a longer period, Japanese individuals started developing concerns over their disrupted work and social life. For verbs, go was the most frequent, but in the actual tweets, it was sometimes used in the negative, and in the context, the verb was unlikely to be used in the affirmative, so the verb was likely used to indicate they cannot go even if it is in the affirmative in this paper ( Textbox 1 ). Hence, the top 5 mean go , can go <negation>, look , meet <negation>, and get out . The results show that there are restrictions on the actions of going, seeing, and meeting as verbs. Compared with the single-day result on February 28, 2020, the concern about work appeared as the top 5 in Tables 1 and 2 , suggesting that Japanese individuals placed clear emphasis on their work routines. In addition, the desire for live concerts increased over the long run, making live concert the fourth most frequently mentioned keyword in Table 1 . Coincidentally, concerns related to friends and missing opportunities to meet up were also observed in both tables, showing the disruption of social relationships and recreational occasions. Both studies indicated that people regarded the COVID-19 pandemic as the main cause of their disrupted plans to hang out with friends or attend large public events. In addition to activities, the keyword finding also reflected the concern of resource shortage, such as toilet paper, masks, and even money, which were critical in supporting daily lives or normal health care practices.

Because the keywords indicated both long-term and short-term concerns, we cross-validated the tweet contents by selecting keyword pairs based on the top 5 keywords related to long-term concerns and those related to short-term anxiety on material shortage. A total of 160 tweets were randomly sampled based on the following keyword pairs (20 tweets each): ライブ＋行く／行けない ( live concert + go / go <negation>); 家＋行く／行けない (home+go/go<negation>); 友達＋行く／行けない ( friends + go / go <negation>); 中止＋行く／行けない ( cancel + go / go <negation>); 友達＋会う／会える (friends+meet/can meet); 仕事＋行く／行けない ( work + go / go <negation>); トイレットペーパー＋なくなる (toilet paper+vanish); and マスク＋なくなる ( mask + vanish ). One of the authors annotated the tweets according to the themes reflected by the keywords. Key findings are discussed in the following section.

Verb and example

• Go: “Due to COVID-19, the day I've been looking forward to going out with the guy I love has been postponed... I can't help it now and I'll accept it, but I was looking forward to it.”
• Meet: “It doesn't feel like April at all due to COVID-19, but I can't wait for it to end so that we can all meet, eat, and shop together comfortably. Six years already... I want to quit my job lol.”

Principal Findings

Our findings revealed that the COVID-19 pandemic significantly disrupted daily routines in Japan, particularly in terms of work, education, social activities, and material shortages (with regard to the temporary spike of anxiety). The findings from our study correspond with numerous studies conducted in diverse countries, highlighting the extensive impact of the COVID-19 pandemic on social life, economy, public mental health, and education [ 5 ]. In this section, we discuss key findings across a temporal spectrum, focusing on 4 crucial aspects: disruption of work routines, public anxiety stemming from perceived resource shortages, concerns regarding social relationships, and interference with the curriculum.

Top Concerns

The impact of the COVID-19 pandemic on the labor market in Japan is unequivocal, mirroring the challenges faced by numerous countries. The pandemic necessitated a shift in work dynamics with the unintended pilot of remote collaboration. Notably, certain categories of Japanese workers, contingent on their employment contracts, exhibited heightened susceptibility to these alterations in work patterns. In our findings, the keyword work demonstrated associations with part-time , abort , and money , indicating that individuals expressing concerns about their work conditions may grapple with job uncertainty, stemming either from the part-time nature of their employment or an abrupt reduction in income. This discovery aligns seamlessly with prior research examining the repercussions of the COVID-19 pandemic on Japan’s labor sector. As described by Kikuchi et al [ 42 ] in their study, individuals in contingent employment, along with women and those with lower income, were notably susceptible. The shift toward teleworking and the accompanying uncertainty about long-term income during the COVID-19 pandemic had a disproportionately adverse impact on these specific demographic groups [ 42 ]. Fukai et al [ 43 ] endorsed these findings through extensive government statistical analysis. According to their research, Japanese individuals employed part-time in service industries or compelled to take leave or face job loss following the declaration of a state of emergency were identified as particularly vulnerable groups significantly affected by the COVID-19 pandemic [ 43 ]. Although the use of part-time or contingent workers has traditionally been a standard practice for Japanese companies seeking to optimize budget and resource allocation, the advent of the COVID-19 pandemic has pushed issues related to work to the forefront of public concern. Researchers caution that this could potentially exacerbate inequality for susceptible individuals unless actively addressed by government support [ 44 ].

In summary, our findings provide substantial evidence for concerns among Japanese internet users regarding job disruption, employment disparities, and inadequate financial resilience. Failing to address these issues during multiple states of emergency, the Japanese government risks compromising the equality within Japan’s labor market significantly. Interestingly, a study conducted by Chen et al [ 27 ], who sampled 6535 Reddit posts, identified strikingly identical subjects that propelled nationwide anxiety in the United States. Notably, concerns about career, finance, and the future were prevalent. However, our research suggests that health and death concerns were not as prominent in Japan, as observed in the study by Chen et al [ 27 ]. We hypothesized that the emphasis on collectivism and harmony in Japanese society could shape individuals’ concerns during crises (particularly in the case of a national crisis). For example, apprehensions about not being perceived as “useful” or causing “inconvenience” to others, possibly even relying on government subsidies, were more pronounced than concerns related to health and mortality.

Sudden and Perhaps Excessive Anxiety About Material Shortage

The scarcity of certain items, including toilet paper, masks, and tissues, as outlined in Table 1 , emerged as a significant issue in Japan. Our findings closely parallel earlier Twitter studies investigating hoarding behaviors, particularly concerning toilet paper [ 45 ]. Although initially observed in the United States, panic buying for household goods rapidly became a global phenomenon. Notably, toilet paper has emerged as a frequently hoarded item, often signaling a surge in demand during natural disasters [ 46 , 47 ]. Although the act of stockpiling toilet paper may seem irrational and has been widely ridiculed on social media, the adverse effects of bulk purchasing have not been as severe. Social scientists may view this behavior as a coping mechanism during a natural disaster [ 48 ]. Contrary to the commonly perceived overhoarding of toilet paper, the mask shortage was deemed a more severe public health crisis and a direct threat to well-being. A 2020 agent-based simulation conducted by Tatapudi et al [ 48 ] illustrated that universal mask use could potentially reduce infections by 20% [ 49 ]. At the time of the study, the total number of people infected by COVID-19 was 541 million, indicating that implementing universal mask use could potentially spare 108 million cases. Numerous studies have indicated a negative correlation between mask use and the COVID-19 infection rate [ 44 , 50 ].

However, the situation in Japan presents a slightly different scenario. The Japanese government faced criticism for a perceived slow response to the awareness of mask shortages, as the pandemic was considered relatively “under control” in its early stages. As the mask crisis unfolded, many Japanese citizens became concerned about their reliance on masks manufactured abroad, prompting the government to take actions to boost domestic mask production. Unfortunately, heightened anxiety also led to the “Abenomasks” incident, wherein the government faced backlash for stockpiling over 82 million unused masks [ 51 ]. A crucial lesson learned from this incident is that although social media serves as a critical channel for the dissemination of news and raising public awareness, the emotional contagion and overpromotion of a particular disaster can backfire, impeding the rational coping mechanisms of citizens and the decision-making processes of the government. Our findings, along with those of numerous other studies, indicate that further efforts are needed to develop effective protocols for addressing the widely contagious anxiety stemming from the dissemination of information about natural disasters on social media.

Concerns About Social Relationships

Keywords pertaining to relationships, social life, and collective events were prevalent in our analysis. For instance, the top 20 frequently occurring nouns associated with Corona no-sei included friends, family, live, events, and one person. The most frequent verbs in the context of Corona no-sei were go , can go <negation>, meet <negation>, buy , meet , can go , and play . The example in Table 4 illustrates how Japanese individuals linked go and meet to their social events. While it may appear that many tweets express concerns about social relationships, these keywords actually reflect people venting their frustration about being unable to meet and engage in activities together, rather than indicating an actual loss of relationships. Interestingly, a study by Goodwin and Takahashi [ 52 ] also yielded similar findings. Most Japanese respondents in their survey gauging perceptions of relationship quality during the COVID-19 pandemic indicated that there were no discernible changes in their perceived relationship quality. Only a few reported that their trust and relationship with communities had declined compared to the prepandemic era [ 52 ]. There was also a report indicating that students, due to reduced communication with friends, face an increased risk of mental health problems [ 53 ].

These findings suggest that events, such as the COVID-19 pandemic, may lead individuals to experience heightened anxiety and stress. While this emotional response could temporarily disrupt their social activities and coping mechanisms against trauma, it may not have a lasting impact on their perceived relationship quality. In fact, the example tweets we analyzed illustrated how individuals, despite feeling frustrated, expressed eagerness to resume their social activities after the pandemic. Hence, we argue that concerns about relationship disruption may be transient and serve as a positive signal prompting individuals in Japan to actively nurture their relationships. As suggested by the study conducted by Goodwin and Takahashi [ 52 ], dedicating additional time to communication, particularly in the context of romantic relationships, could further enhance the quality of these connections [ 42 ].

Concern for Education Discontinuation

The peak volume of tweets was recorded on February 28, 2020, coinciding with the government’s announcement of the simultaneous closure of all elementary, junior high, and senior high schools in Japan. In fact, in the most frequent nouns and verbs shown in Tables 1 and 2 , the top words related to the simultaneous closure of schools were graduation ceremony , cancel , lose , rest , and go <negation>, all of which reflected Japanese citizens’ concerns about the discontinuation of education, the cancelation of the graduation ceremony, and missing school classes. It is essential to note that in Japan, the graduation ceremony typically takes place in March and the new school and work year commences in April. Despite the Japanese government’s earnest efforts to mitigate the spread of COVID-19, as scrutinized by scientists, the decision to close schools in Japan did not yield a substantial impact on preventing the spread of COVID-19. Instead, it deprived children of valuable learning and developmental opportunities [ 54 ]. Moreover, with the closure of schools, there was a surge in the demand for digital education or internet-based learning platforms. However, many schools and student households were ill-equipped to handle this impromptu shift to an internet-based education system. As discussed in detail by Iwabuchi et al [ 55 ], the unequal distribution of resources among schools in Japan further intensified the digital learning disparities brought about by the COVID-19 pandemic–induced school closures. The more well-funded private and prefecture-sponsored schools had often already implemented or could quickly set up the necessary e-learning system to cope with the lack of face-to-face lecturing. However, most public schools were forced to send learning materials to students by mail, risking a huge learning gap between students in private and public schools. The long-term impact on students’ physical and mental development remains uncertain, given that most schools were able to resume normalcy after the lifting of the state of emergency. A study conducted by Nishimura et al [ 56 ] on medical students clearly indicated a deterioration in subjective mental well-being.

Concerns were also observed regarding web-based alternatives, with growing apprehensions that they fail to adequately substitute the essential in-person learning and hands-on field practice integral to medical education. The diverse concerns reflected in education-related keywords in Table 1 suggest that many Japanese individuals transitioned their focus from one-time events, such as graduation ceremony and school holiday , to longer-term mental and societal impacts, such as opportunity , stress , and university . This shift implies that the long-term effects would take time to manifest compared to short-term disruptions of specific incidents, such as a graduation ceremony. Further studies are crucial to monitor and unveil a complete picture of this disruption.

Long- and Short-Term Concerns and the Impact on the Society

Following the World Health Organization’s official declaration that COVID-19 was no longer considered a global health emergency on May 4, 2023, individuals who survived now faced a familiar daily life with some changes that were difficult to imagine in the prepandemic era. However, there is still an impact on society that can be challenging to trace and monitor. The economic repercussion, such as inflation and tumbling currency values in Japan, are gradually occurring. Schoolchildren who have lost education for almost 1 year are bracing for their future growth. An increasing number of companies are eager to get talent to opt in for remote working styles to attract employees who were reluctant to return to city offices. Individuals are probably no longer worried about toilet paper but will gradually sense the subtle shifting of their workstyles, social styles, and even learning styles. However, due to the limitations of our data, we were not able to speculate about the postpandemic future. Our discussion offers possible clues to further trace the causes of societal changes. The profound effects of the COVID-19 pandemic on society and public health require further investigation and monitoring.

Limitations and Future Work

It should be noted that our study had some limitations in extracting data from social platforms such as Twitter. One limitation is the lack of geolocation metadata. Although we capitalized on the language exclusivity of Japanese tweets and the domestic majority to extract representative samples of Japanese citizens, it is important to note that there may be some minor contributions from Japanese speakers residing outside Japan. This limitation should be considered when interpreting the findings of this study. Another limitation arises from the bias present on Twitter, as its use is lower among older adults compared to the younger population. To mitigate this bias, stratified analysis is necessary to account for the effects of age. However, the current system lacks age data. Consequently, the results should be interpreted with the awareness that the perspectives of the older adults are underrepresented.

Because the purpose of this study was to derive an interruption schedule, we specifically targeted verbs and nouns to better represent social connections (families and friends), locations, events, subjects, and actions, rather than using adjectives or phrases that might focus on emotional descriptions or concrete situations. This approach limited our options for sentiment-related analysis methods or topic modeling, which could reveal emotional reactions instead of generic events and the involvement of close connections. Although people’s sentiments were deemed beyond the scope of this study, in future studies, we would like to analyze how people’s sentiments have changed through sentiment analysis [ 57 ]. With the introduction of transformer-based large language models, such as bidirectional encoder representations from transformers and text-to-text transfer transformers, more contextual and in-depth understanding and analysis might be made available for researchers in social media data. This should be considered in future studies.

We also did not address concerns regarding the safety of cybersecurity during the work-from-home period during the pandemic. We noticed that in the United States, data breaches and the security of the work environment were one of the top concerns [ 58 ]; however, based on our current results, there was no direct implication on this aspect in Japan during the COVID-19 pandemic. This will be considered in our future work.

Conclusions

Overall, by adding the analysis on Corona no-sei to the conventional symptom-based monitoring, we were able to identify the underlying concerns at the peak of the disruption and across the whole-time span of the 3 announcements of state of emergency. Our findings and a comparison of the tweets against COVID-19 case numbers yielded rich insights into people’s short- and long-term concerns and potential aspects of societal impact caused by the announcements of the state of emergency. Although more studies from different fields would help to reveal the whole landscape of social and psychological impact caused by COVID-19, we believed that the keywords reflected in Corona no-sei tweets provided more nuanced descriptions of real-life problems Japanese individuals faced during the COVID-19 pandemic and revealed the development of different concerns in response to the change of policies.

Timely communication of analysis results is crucial, especially when dealing with issues of significant social impact, such as a global pandemic. A delay in delivering results can hinder decision-making processes and require substantial resources to recover from the initial losses caused by poor decisions. For policy makers, especially the Japanese government, this study reflects the opinions of citizens and should be considered when reviewing the effectiveness and suitability of a policy as well as assessing further measures to support those impacted during the pandemic.

Acknowledgments

This research was conducted as part of the COVID-19 AI & Simulation Project run by Mitsubishi Research Institute commissioned by the Cabinet Secretariat. The methodology used in this study was developed through the support from Japan Science and Technology Agency (JST) Strategic International Collaborative Research Program (SICORP) (grant JPMJSC2107) and Japan Society for the Promotion of Science (JSPS) KAKENHI (grant JP 22K12041) in Japan. The authors acknowledge Dr Naoki Yoshinaga, Dr Masashi Toyoda, and Dr Masaru Kitsuregawa for providing the tweet-analysis system.

Conflicts of Interest

None declared.

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Edited by T Mackey; submitted 06.06.23; peer-reviewed by L Chen, Y Pachankis, G Sebastian, M Elbattah; comments to author 29.06.23; revised version received 11.08.23; accepted 06.03.24; published 01.04.24.

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Reply to Discussion of “Experimental Study on Unconfined Compressive Strength of Rubberized Cemented Soil” by Abdellah Cherif Taiba, Youcef Mahmoudi, Mostefa Belkhatir

• Geotechnical Engineering
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• Published: 01 April 2024

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• Abdellah Cherif Taiba 1 ,
• Youcef Mahmoudi 2 ,
• Mostefa Belkhatir 3 ,
• Duanwei Guo 5 ,
• Dexin Song 4 ,
• Fangcheng Liu 4 ,
• Lei Zhang 4 &
• Qifeng Wen 4  

The authors appreciated the discussants for their suggestions on their recent paper regarding four key topics: the evaluation of the ductility index of rubberized cemented soil, comparative analysis between predicted models and actual results, providing guidance and suggestions for the application of rubberized cemented soil in engineering practice, and evaluating the environmental impact of rubberized cemented soil. These discussions have practical significance and are worth further research in order to contribute to the research and application of rubberized cemented soil.

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Laboratory of Material Sciences & Environment, Laboratory of Architecture, Cities and Environment, Hassiba Ben Bouali University, B.P 78C, Ouled Fares, 02180, Chlef, Algeria

Abdellah Cherif Taiba

Laboratory of Material Sciences & Environment, Hassiba Ben Bouali University, B.P 78C, Ouled Fares, 02180, Chlef, Algeria

Youcef Mahmoudi

Alexander von Humboldt Foundation Researcher, Berlin, Germany

Mostefa Belkhatir

College of Civil Engineering, Hunan University of Technology, Zhuzhou, 412007, China

Jie He, Dexin Song, Fangcheng Liu, Lei Zhang & Qifeng Wen

Guangzhou Navigation Aids Office of the South China Sea Navigation Support Center of the Ministry of Transport, Guangzhou, Guangdong, 51032, China

Duanwei Guo

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Taiba, A.C., Mahmoudi, Y., Belkhatir, M. et al. Reply to Discussion of “Experimental Study on Unconfined Compressive Strength of Rubberized Cemented Soil” by Abdellah Cherif Taiba, Youcef Mahmoudi, Mostefa Belkhatir. KSCE J Civ Eng (2024). https://doi.org/10.1007/s12205-024-2998-8

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Received : 21 September 2023

Accepted : 20 February 2024

Published : 01 April 2024

DOI : https://doi.org/10.1007/s12205-024-2998-8

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