results vs discussion research paper

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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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Research Skills

Results, discussion, and conclusion, results/findings.

The Results (or Findings) section follows the Methods and precedes the Discussion section. This is where the authors provide the data collected during their study. That data can sometimes be difficult to understand because it is often quite technical. Do not let this intimidate you; you will discover the significance of the results next.

The Discussion section follows the Results and precedes the Conclusions and Recommendations section. It is here that the authors indicate the significance of their results. They answer the question, “Why did we get the results we did?” This section provides logical explanations for the results from the study. Those explanations are often reached by comparing and contrasting the results to prior studies’ findings, so citations to the studies discussed in the Literature Review generally reappear here. This section also usually discusses the limitations of the study and speculates on what the results say about the problem(s) identified in the research question(s). This section is very important because it is finally moving towards an argument. Since the researchers interpret their results according to theoretical underpinnings in this section, there is more room for difference of opinion. The way the authors interpret their results may be quite different from the way you would interpret them or the way another researcher would interpret them.

Note: Some articles collapse the Discussion and Conclusion sections together under a single heading (usually “Conclusion”). If you don’t see a separate Discussion section, don’t worry.  Instead, look in the nearby sections for the types of information described in the paragraph above.

When you first skim an article, it may be useful to go straight to the Conclusion and see if you can figure out what the thesis is since it is usually in this final section. The research gap identified in the introduction indicates what the researchers wanted to look at; what did they claim, ultimately, when they completed their research? What did it show them—and what are they showing us—about the topic? Did they get the results they expected? Why or why not? The thesis is not a sweeping proclamation; rather, it is likely a very reasonable and conditional claim.

Nearly every research article ends by inviting other scholars to continue the work by saying that more research needs to be done on the matter. However, do not mistake this directive for the thesis; it’s a convention. Often, the authors provide specific details about future possible studies that could or should be conducted in order to make more sense of their own study’s conclusions.

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on August 30, 2022 by Tegan George . Revised on July 18, 2023.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean—any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs. discussion vs. conclusion, checklist: research results, other interesting articles, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

• Your results should always be written in the past tense.
• While the length of this section depends on how much data you collected and analyzed, it should be written as concisely as possible.
• Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like “appears” or “implies.”
• If you have other results you’d like to include, consider adding them to an appendix or footnotes.
• Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe store: first discuss the shoes as a whole, then the sneakers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

• A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
• A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
• A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

• Tables are used to communicate exact values, giving a concise overview of various results
• Graphs and charts are used to visualize trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarize or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

A two-sample t test was used to test the hypothesis that higher social distance from environmental problems would reduce the intent to donate to environmental organizations, with donation intention (recorded as a score from 1 to 10) as the outcome variable and social distance (categorized as either a low or high level of social distance) as the predictor variable.Social distance was found to be positively correlated with donation intention, t (98) = 12.19, p < .001, with the donation intention of the high social distance group 0.28 points higher, on average, than the low social distance group (see figure 1). This contradicts the initial hypothesis that social distance would decrease donation intention, and in fact suggests a small effect in the opposite direction.

Figure 1: Intention to donate to environmental organizations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

• Recurring points of agreement or disagreement
• Patterns and trends
• Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

When asked about video games as a form of art, the respondents tended to believe that video games themselves are not an art form, but agreed that creativity is involved in their production. The criteria used to identify artistic video games included design, story, music, and creative teams.One respondent (male, 24) noted a difference in creativity between popular video game genres:

“I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.”

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

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I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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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.

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

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.

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Writing your Dissertation:  Results and Discussion

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

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

There are advantages to both approaches.

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

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

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

Results Section

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

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

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

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

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

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

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

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

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

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

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

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

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

For example:

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

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

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

Discussion Section

This section has four purposes, it should:

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

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

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

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

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

Starting the Task

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

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

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

Fleshing Out the Detail

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

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

The Importance of Understanding Differences

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

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

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

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

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

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

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

Continue to: Dissertation: Conclusion and Extras Writing your Methodology

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

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Guide on how to write results and discussion in a research paper.

The results and discussion section of a research paper document what you did in the entire research. You could call them the most important sections in a research paper, although other sections are also important. To write the results and discussion in research paper, you need to have the technical know-how of writing. We will give you practical tips on starting and writing results and discussions if you keep reading.

What is the difference between results and discussion in academic writing?

Before we get into how to write these two important sections in a research paper, let’s talk about their differences. The major difference between them is what aspect of the entire research they contain. The results section objectively reports your findings as they are; no speculations on why you found the results. On the other hand, the discussion section interprets the results, putting them in context, and explaining their importance.

Both sections are sometimes combined in research, particularly in qualitative research. In quantitative research, you are expected to separate the results from the discussion – that is, each section on different pages. An excellent place to get a good idea of how two write these sections is in a results and discussion example.

How to write discussion in research paper

In the discussion section of a research paper, you’re going in-depth with your findings, discussing their meaning, importance, and relevance. You’re not including any background research; you’re instead focusing on evaluating and explaining your results. Then, you’ll indicate how it relates to your research questions or thesis statement and literature review. Below is what to include in the discussion section of a research paper t:

• Results summary : In one paragraph, reiterate the research problem and briefly discuss your major results. Avoid repeating the data you already reported in the results section; clearly state the result that directly answers your research problem.
• Interpret your results : Your aim is to ensure your readers understand your results, how they answer the research questions, and their significance to them. This section typically covers identifying patterns and correlations among the data and discussing whether or not the results supported your thesis. It also contextualizes your results with previous research, explains unexpected results and their significance, considers possible explanations, and argues your position.
• Discussing the implications : While giving your interpretation of the results, don’t forget to relate them back to the articles you used in the literature review. This shows how your results fit with existing knowledge, the insights they contribute, and their consequences for practice or theory.
• State the limitations : Every research has its limitations, even the best ones; you need to acknowledge your research’s limitations to demonstrate your credibility. You’re not necessarily listing errors; you’re giving a realistic picture of what your study can and cannot do.
• Recommend : Use your findings to recommend further research or practical implementation; this part sometimes goes with the conclusion. Instead of stating that more studies be done, show what and how future work can build on the areas your paper couldn’t address.

Practical tips on how to start a results and discussion section

The results and discussion section of a research paper can be the easiest part to write or the hardest. It all depends on you knowing what to include and not include and how to start writing. Below are helpful tips for writing the results and discussion section of a research paper:

• Please don’t repeat the results in the discussion; start with repeating the research questions and explain how the results answer them.
• Start from the simple results to the complex; you can even start with the conclusion first, but ensure it is consistent with your objectives.
• Don’t explain your results in the result section; simply state your findings as directly and simply as possible.
• Emphasize what new, different, or important things your results add to knowledge in the discussion section.
• Understand the difference between statistical significance and clinical importance.
• The tables and graphs in the results section should stand alone, with texts highlighting their importance or meaning.
• Arbitrarily present your results, with sidelights results not receiving equal weight.

Now, you can write your paper’s results and discussion section with these tips, understanding what and what not to include. We recommend that you go online and check through an example of discussion in research paper – or samples. If you see how professionals write it, you’re a step closer to being good at writing it yourself.

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

• Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
• Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

• Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
• Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
• A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
• The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
• A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

• Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
• Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
• Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
• Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
• Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
• Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
• Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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

Michael höfler.

1 Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

5 Chair of Clinical Psychology and Behavioural Neuroscience, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

2 Behavioral Epidemiology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

Sebastian Trautmann

Robert miller.

3 Faculty of Psychology, Technische Universität Dresden, Dresden, Germany

4 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

Associated Data

Not applicable.

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

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

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

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

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

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

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

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

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

• D causes LC (e.g. because smoking might constitute “self-medication” of depression symptoms)
• LC causes D (e.g. because LC patients are demoralized by their diagnosis)
• D and LC cause each other (e.g. because the arguments in both a. and b. apply)
• D and LC are the causal consequence of the same factor(s) (e.g. poor health behaviors - HB)
• D and LC only share measurement error (e.g. because a fraction of individuals that has either depression or lung cancer denies both in self-report measures).

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

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

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

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

Recommendations for writing a discussion section

Specific recommendations.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

General recommendations

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

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

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

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

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

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

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

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

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

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

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

Conclusions

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

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

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

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

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

Acknowledgements

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

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

Availability of data and materials

Abbreviations, authors’ contributions.

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

Ethics approval and consent to participate

Consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

What is the Difference Between Results and Discussion Section

An original research article typically comprises the following: Introduction, Methods, Results, and Discussion (IMRAD). However, guidelines may differ across journals. Some journals may require the results and discussion to be a  combined section whereas others may require them to be separate sections. Read more about separating the results and discussions section .

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How to Practice Academic Medicine and Publish from Developing Countries? pp 225–230 Cite as

How to Write the Discussion?

• Samiran Nundy 4 ,
• Atul Kakar 5 &
• Zulfiqar A. Bhutta 6  
• Open Access
• First Online: 24 October 2021

31k Accesses

1 Citations

Many authors, and editors, think this is the most difficult part of a paper to write well and have described it variously to be the ‘narrating the story of your research’, ‘the movie or the main scientific script’ and the ‘proof of the pudding’. The idea of a discussion is to communicate to the readers the importance of your observations and the results of all your hard work. In this section, you are expected to infer their meaning and explain the importance of your results and finally provide specific suggestions for future research [1, 2]. The discussion places the outcome into a larger context and mentions the implications of the inferences for theoretical and practical purposes [3].

That then is the first draft and you should never think of having fewer than six drafts Stephen Lock, BMJ editor in chief (1929–…)

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1 What Is the Importance of the Discussion?

Many authors, and editors, think this is the most difficult part of a paper to write well and have described it variously to be the ‘narrating the story of your research’, ‘the movie or the main scientific script’ and the ‘proof of the pudding’. The idea of a discussion is to communicate to the readers the importance of your observations and the results of all your hard work. In this section, you are expected to infer their meaning and explain the importance of your results and finally provide specific suggestions for future research [ 1 , 2 ]. The discussion places the outcome into a larger context and mentions the implications of the inferences for theoretical and practical purposes [ 3 ].

2 How Should I Structure the Discussion Section?

There are three major portions for the discussion of a manuscript.

The first paragraph should baldly state the key findings of your research. Use the same key concept you gave in the introduction. It is generally not necessary to repeat the citations which have already been used in the Introduction. According to the ‘serial position effect’, themes mentioned at the beginning and end of a paragraph are more likely to be remembered than those in the middle [ 1 ]. However, one should remember that the discussion should not look like a second introduction, and all the ancillary information which has been previously cited should not be repeated [ 4 ].

For example, in a paper on the ‘Role of sulfasalazine in the Chikungunya arthritis outbreak of 2016’, the review may start with, ‘Our key findings suggest that chikungunya arthralgia is a self-limiting disorder. Persistent arthritis was recorded in only 10% of the affected population and in those who received sulfasalazine, clinical improvement both in tender and swollen joints, was recorded in 95% of the subjects’.

The middle portion should consist of the body of the discussion. This section interprets the important results, discusses their implications and explains how your data is similar to or different from those that have been published previously.

Discuss in fair detail studies supporting your findings and group them together, against those offering a different perspective (e.g., Western experience, smaller numbers, non-randomized studies, etc.). An explanation should be offered on how your work is similar to others or how it is different from the others. This should be followed by a review of the core research papers. The results should now be divided thematically and analyzed. The discussion should also contain why the study is new, why it is true, and why it is important for future clinical practice [ 4 , 5 , 6 ].

For the above research mention the clinical features, patterns of joint involvement, how long arthritis persisted, and any role of disease-modifying agents. Have any other researchers found different findings under the same circumstances.

The final paragraph should include a ‘take home message’ (about one or two) and point to future directions for investigation that have resulted from this study.

The discussion can be concluded in two ways:

By again mentioning the response to the research question [ 5 , 7 ]

By indicating the significance of the study [ 2 , 4 ]

You can use both methods to end this section. Most importantly you should remember that the last paragraph of the discussion should be ‘strong, clear, and crisp’ and focus on the main research question addressed in the manuscript. This should be strengthened by the data which clearly states whether or not your findings support your initial hypothesis [ 1 , 5 , 8 , 9 , 10 ].

3 What Are the General Considerations for Writing a Discussion? [ 3 , 10 , 11 ]

Start the discussion with the ‘specific’ problems and move to the ‘general’ implications (Fig. 21.1 ).

The discussion should not look like a mass of unrelated information. Rather, it should be easy to understand and compare data from different studies.

Include only recent publications on the topic, preferably from the last 10 years.

Make certain that all the sources of information are cited and correctly referenced.

Check to make sure that you have not plagiarized by using words quoted directly from a source.

The written text written should be easily understood, crisp, and brief. Long descriptive and informal language should be avoided.

The sentences should flow smoothly and logically.

You need not refer to all the available literature in the field, discuss only the most relevant papers.

How a discussion should look. First arrow—Mention your key results/findings; Second arrow—Discuss your results with their explanations\step by step; Third Arrow—Enumerate your studies limitations and strengths; Last arrow—Suggest future directions for investigation

4 Discussion Is Not a War of Words

5 How Long Should the Discussion in the Manuscript Be?

Most journals do not mention any limits for discussion as long as it is brief and relevant (Fig. 21.2 ). As a rule, ‘The length of the discussion section should not exceed the sum of other parts-introduction, materials and methods, and results’. [ 3 ] In any good article, the discussion section is 3–4 pages, 6–7 paragraphs, or approximately 10 paragraphs, and 1000–1500 words [ 1 , 5 , 8 , 12 ].

Discussion pyramid

6 What Should Be Written in the Conclusion Section?

The conclusion is the last paragraph and has the carry-home message for the reader. It is the powerful and meaningful end piece of the script. It states what change has the paper made to science and it also contains recommendations for future studies.

7 Conclusions

Discussion is not a stand-alone section, it intertwines the objectives of the study with how and what was achieved.

The major results are described and compared with other studies.

The author’s own work is critically analysed in comparison with that of others.

The limitations and strengths of the study are highlighted.

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Nundy, S., Kakar, A., Bhutta, Z.A. (2022). How to Write the Discussion?. In: How to Practice Academic Medicine and Publish from Developing Countries?. Springer, Singapore. https://doi.org/10.1007/978-981-16-5248-6_21

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

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

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

• Factual statements supported by evidence. Short and sweet without excess words
• Present representative data rather than endlessly repetitive data
• Discuss variables only if they had an effect (positive or negative)
• Use meaningful statistics
• Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

• Present the results of the paper, in logical order, using tables and graphs as necessary.
• Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
• Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
• Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
• Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
• It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
• You must refer in the text to each figure or table you include in your paper.
• Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
• Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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Discussion vs Conclusion: Researcher's Compact Guide

Table of Contents

If you are a researcher or a student, understanding the difference between a discussion and a conclusion is crucial especially when you are working on your academic projects. These two sections play distinct roles in your paper, and knowing how to approach each one can greatly improve the quality of your work.

This guide will delve into the nuances of both sections, providing a comprehensive overview of their purposes, structures, and writing strategies.

Understanding the discussion section of a research paper

The discussion section of a research paper is where you interpret and explain your research findings. It's a section for you to explore the implications of your results, compare them to previous research, and address any limitations in your study.

One of the main purposes of the discussion section is to answer the research question. You should provide a detailed explanation of the results and how they relate to your hypothesis or research question. This part of the paper is your opportunity to show that you have made a valuable contribution to your field of study.

Structure of the Discussion Section

The structure of the discussion section can vary depending on the nature of the research and the guidelines of the publication. However, a typical structure might include the following elements:

• Restatement of the research problem
• Summary of the main findings
• Interpretation of the results
• Comparison with previous research
• Explanation of any unexpected findings or discrepancies
• Discussion of the implications of the results
• Identification of limitations and suggestions for prospective research

Tips for Writing Discussion Section

When writing the discussion section, it's important to stay focused on your research question and avoid talking about unrelated areas. Be sure to interpret your findings in the context of the research question and the existing literature in your field.

It's also crucial to be honest about the limitations of your study. Acknowledging these limitations not only enhances the credibility of your research but also provides valuable information for budding researchers.

Understanding the Conclusion Section

The conclusion section of a research paper is where you summarize your research and its implications. Unlike the discussion section, the conclusion is not the place for a detailed analysis of your results. Instead, it's where you wrap up your argument and leave the reader with a clear understanding of your research and its significance.

The conclusion should provide a succinct summary of your research question, methods, results, and main findings. It should also discuss the broader implications of your research and suggest areas for future study.

Structure of the Conclusion Section

The structure of the conclusion section is typically more straightforward than that of the discussion section. A typical conclusion might include the following elements:

• Restatement of the research question
• Discussion of the implications of the research
• Suggestions for future research

Tips for Writing Conclusion Section

When writing the conclusion section, it's important to be concise and to the point. Try not to introduce new information or arguments in the conclusion section. Instead, simply focus on summarizing your research and highlighting its significance.

It's also important to make your conclusion engaging and indelible. Consider ending with a strong statement that emphasizes the importance of your research and leaves a lasting impression on the reader.

Discussion Vs. Conclusion: Key Differences

While the discussion and conclusion sections of a research paper have some similarities, they serve different purposes and should be approached differently. The discussion section is where you interpret and analyze your results, while the conclusion is where you summarize your research and highlight its significance.

Another key difference is the level of detail. The discussion section typically includes a detailed analysis of the results, while the conclusion provides a concise summary of the research. Let’s take a look at the differences between the discussion and conclusion sections in various aspects

Whether you're writing a discussion section or a conclusion, it's important to choose the right approach for your research. Consider the nature of your study, the guidelines of the publication, and the expectations of your audience when deciding how to structure and write these sections.

Remember, the goal of both sections is to communicate your research effectively and make a meaningful contribution to your field. By understanding the differences between a discussion and a conclusion, you can ensure that your research paper is clear, coherent, and impactful.

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Results & Discussion

Characteristics of results & discussion.

• Results section contains data collected by scientists from experiments that they conducted.
• Data can be measurements, numbers, descriptions and/or observations.
• Scientific data is typically described using graphs, tables, figures, diagrams, maps, charts, photographs and/or equations.
• Discussion section provides an interpretation of the data, especially in context to previously published research.

The Results and Discussion sections can be written as separate sections (as shown in Fig. 2 ), but are often combined in a poster into one section called Results and Discussion.   This is done in order to (1) save precious space on a poster for the many pieces of information that a scientist would like to tell their audience and (2) by combining the two sections, it becomes easier for the audience to understand the significance of the research.   Combining the Results section and Discussion section in a poster is different for what is typically done for a scientific journal article.   In most journal articles, the Results section is separated from the Discussion section.   Journal articles are different from posters in that a scientist is not standing next to their journal article explaining it to a reader.   Therefore, in a journal article, an author needs to provide more detailed information so that the reader can understand the research independently.   Separating the Results section and Discussion section allows an author the space necessary to write a lengthier description of the research. Journal articles typically contain more text and more content (e.g., figures, tables) than posters.

The Results and Discussion section should contain data, typically in the form of a graph, histogram, chart, image, color-coded map or table ( Figs. 1 & 4 ).   Very often data means numbers that scientists collect from making measurements.   These data are typically presented to an audience in the form of graphs and charts to show a reader how these numbers change over time, space or experimental conditions ( Fig. 7 ).   Numbers can increase, decrease or stay the same and a graph, or another type of figure, can be effectively used to convey this information to a reader in a visual format ( Fig. 7 ).      

Figure 7. Example of a Graph

An audience will be attracted to a poster because of its figures and so it is very important for the author to pay particular attention to the creation, design and placement of the figures in a poster ( Figs. 1 & 4 ).   A good figure is one that is informative, easy to comprehend and allows the reader to understand the significance of the data and experiment.   Very often an author will use color to draw attention to a figure.      

The Discussion section should state the importance of the research that is presented in the poster.   It should provide an interpretation of the results, especially in context to previously published research.   It may propose future experiments that need to be conducted as a result of the research presented in the poster.   It should clearly illustrate the significance of the research with regards to new knowledge, understanding and/or discoveries that were made as part of the research.

Scientific Posters: A Learner's Guide Copyright © 2020 by Ella Weaver; Kylienne A. Shaul; Henry Griffy; and Brian H. Lower is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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How to Write a Results Section for a Dissertation or Research Paper: Guide & Examples

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A results section is a crucial part of a research paper or dissertation, where you analyze your major findings. This section goes beyond simply presenting study outcomes. You should also include a comprehensive statistical analysis and interpret the collected data in detail.

Without dissertation research results, it is impossible to imagine a scientific work. Your task here is to present your study findings. What are qualitative or quantitative indicators? How to use tables and diagrams? How to describe data? Our article answers all these questions and many more. So, read further to discover how to analyze and describe your research indexes or contact or professionals for dissertation help from StudyCrumb.

What Is a Results Section of Dissertation?

The results section of a dissertation is a data statement from your research. Here you should present the main findings of your study to your readers. This section aims to show information objectively, systematically, concisely. It is allowed using text supplemented with illustrations.  In general, this section's length is not limited but should include all necessary data. Interpretations or conclusions should not be included in this section. Therefore, in theory, this is one of your shortest sections. But it can also be one of the most challenging sections.  The introduction presents a research topic and answers the question "why?". The Methods section explains the data collection process and answers "how?". Meanwhile, the result section shows actual data gained from experiments and tells "what?" Thus, this part plays a critical role in highlighting study's relevance. This chapter gives reader study relevance with novelty. So, you should figure out how to write it correctly. Here are main tasks that you should keep in mind while writing:

• Results answer the question "What was found in your research?"
• Results contain only your study's outcome. They do not include comments or interpretations.
• Results must always be presented accurately & objectively.
• Tables & figures are used to draw readers' attention. But the same data should never be presented in the form of a table and a figure. Don't repeat anything from a table also in text.

Dissertation: Results vs Discussion vs Conclusion

Results and discussion sections of a dissertation are often confused among researchers. Sometimes both these parts are mixed up with a conclusion for thesis . Figured out what is covered in each of these important chapters. Your readers should see that you notice how different they are. A clear understanding of differences will help you write your dissertation more effectively. 5 differences between Results VS Discussion VS Conclusion:

Wanna figure out the actual difference between discussion vs conclusion? Check out our helpful articles about Dissertation Discussion or Dissertation Conclusion.

Present Your Findings When Writing Results Section of Dissertation

Now it's time to understand how to arrange the results section of the dissertation. First, present most general findings, then narrow it down to a more specific one. Describe both qualitative & quantitative results. For example, imagine you are comparing the behavior of hamsters and mice. First, say a few words about the behavioral type of mammals that you studied. Then, mention rodents in general. At end, describe specific species of animals you carried out an experiment on.

Qualitative Results Section in Dissertation

In your dissertation results section, qualitative data may not be directly related to specific sub-questions or hypotheses. You can structure this chapter around main issues that arise when analyzing data. For each question, make a general observation of what data show. For example, you may recall recurring agreements or differences, patterns, trends. Personal answers are the basis of your research. Clarify and support these views with direct quotes. Add more information to the thesis appendix if it's needed.

Quantitative Results Section in a Dissertation

The easiest way to write a quantitative dissertation results section is to build it around a sub-question or hypothesis of your research. For each subquery, provide relevant results and include statistical analysis . Then briefly evaluate importance & reliability. Notice how each result relates to the problem or whether it supports the hypothesis. Focus on key trends, differences, and relationships between data. But don't speculate about their meaning or consequences. This should be put in the discussion vs conclusion section. Suppose your results are not directly related to answering your questions. Maybe there is additional information that helps readers understand how you collect data. In that case, you can include them in the appendix. It is often helpful to include visual elements such as graphs, charts, and tables. But only if they accurately support your results and add value.

Tables and Figures in Results Section in Dissertation

We recommend you use tables or figures in the dissertation results section correctly. Such interpretation can effectively present complex data concisely and visually. It allows readers to quickly gain a statistical overview. On the contrary, poorly designed graphs can confuse readers. That will reduce the effectiveness of your article.  Here are our recommendations that help you understand how to use tables and figures:

• Make sure tables and figures are self-explanatory. Sometimes, your readers may look at tables and figures before reading the entire text. So they should make sense as separate elements.
• Do not repeat the content of tables and figures in text. Text can be used to highlight key points from tables and figures. But do not repeat every element.
• Make sure that values ​​or information in tables and text are consistent. Make sure that abbreviations, group names, interpretations are the same as in text.
• Use clear, informative titles for tables and figures. Do not leave any table or figure without a title or legend. Otherwise, readers will not be able to understand data's meaning. Also, make sure column names, labels, figures are understandable.
• Check accuracy of data presented in tables and figures. Always double-check tables and figures to make sure numbers converge.
• Tables should not contain redundant information. Make sure tables in the article are not too crowded. If you need to provide extensive data, use Appendixes.
• Make sure images are clear. Make sure images and all parts of drawings are precise. Lettering should be in a standard font and legible against the background of the picture.
• Ask for permission to use illustrations. If you use illustrations, be sure to ask copyright holders and indicate them.

Tips on How to Write a Results Section

We have prepared several tips on how to write the results section of the dissertation!  Present data collected during study objectively, logically, and concisely. Highlight most important results and organize them into specific sections. It is an excellent way to show that you have covered all the descriptive information you need. Correct usage of visual elements effectively helps your readers with understanding. So, follow main 3 rules for writing this part:

• State only actual results. Leave explanations and comments for Discussion.
• Use text, tables, and pictures to orderly highlight key results.
• Make sure that contents of tables and figures are not repeated in text.

In case you have questions about a  conceptual framework in research , you will find a blog dedicated to this issue in our database.

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Original research article, study on the impact of social capital on the rural residents’ conscious interpersonal waste separation behavior: evidence from jiangxi province, china.

• 1 College of Business, Jiangxi Normal University, Nanchang, China
• 2 College of Finance, Jiangxi Normal University, Nanchang, China
• 3 College of Economics and Management, Nanchang Hangkong University, Nanchang, China

Guiding rural residents to implement interpersonal waste separation in their daily lives consciously is crucial for controlling solid waste pollution in developing countries. This paper utilizes survey data from Jiangxi Province which is one of the national pilot zones for ecological conservation in China to analyze the impact of the social capital that includes social networks, social trust, and social norms on the rural residents’ conscious interpersonal waste separation behavior. The empirical results indicate that social capital has a positive effect on the rural residents’ conscious interpersonal waste separation behavior, wherein the effects of social networks and social trust are significant. Among the three dimensions of social capital, social networks and social norms are substitutable, while social trust and social norms have a complementary effect on each other. Moreover, the ecological cognition and subjective norm play a significant mediating role in the relationship between social network, social trust, and social norms and the rural residents’ conscious interpersonal waste separation behavior, while the government policies plays a significant moderating effect.

1 Introduction

Proper disposal of waste has become an urgent problem all over the world. The World Bank’s report points out that global waste will increase by 70% by 2050 without any intervention ( Kaza et al., 2018 ). In China, the representative of developing countries, the amount of waste produced has increased continuously with economic growth in recent years, and the annual growth rate of the rural residents’ per capita solid waste has reached 8% to 10 ( Moh and Manaf, 2013 ). Thus, the proper disposal of waste in China is not only an important way to improve the living environment and achieve green development for itself, but also provides policy enlightenment for other developing countries worldwide.

There are several dimensions of social capital, and the existing research has explored the impact of different dimensions of social capital such as social network, social prestige and social engagement on residents’ waste separation behavior ( Cao et al., 2023 ), and it has been confirmed that the impact is significant by researchers ( Ling and Xu, 2020 ; Hua et al., 2021 ; Wang and Zhang, 2022 ; Wang et al., 2023 ). However, the mechanism is not clear yet. Moreover, these studies consider the dimensions of social capital to be independent of each other, but some researchers have suggested that the dimensions of social capital have interactive effects ( Hsu and Hung, 2013 ; Zhang et al., 2023 ; Saleem and Zhang, 2024 ). Existing research has not yet focused on the interaction between the dimensions of social capital on residents’ waste separation behavior. Additionally, urban residents’ waste separation behavior can be categorized into habitual separation behavior, decision-making separation behavior, interpersonal separation behavior, and civic separation behavior and the factors affecting the waste separation behavior vary significantly among different types ( Chen, 2018 ). The residents’ interpersonal waste separation behavior represents their participation in the activity. However, few researchers have primarily addressed the influence of social capital on the residents’ interpersonal waste separation behavior. In the context of rural China, where social relations are complex, the life behavior of residents is deeply embedded in the social capital, with social networks, social trust and social norms as the core elements ( Bian, 1997 ). It remains to be further studied the impact of different dimensions of social capital on rural residents’ conscious interpersonal waste separation behavior.

Therefore, this paper discusses the interaction and the mechanism of social capital on the rural residents’ conscious interpersonal household waste separation behavior, with a focus on the division of social capital into social networks, social trust, and social norms. To explain the mechanism, this paper introduces the mediating effect test of ecological cognition and subjective norm. The aim is to offer a reference that guides rural residents in consciously motivating others to participate in waste separation during interpersonal activities, thereby enhancing the human ecological environment.

The remaining sections are as follows. Section 2 presents the research hypothesis. Section 3 describes the methods, including the research area, the survey design, the samples and data collection, as well as the regression model. Section 4 reports the empirical results. Section 5 discusses the results and provides the policy implications. The last section summarizes the conclusions.

2 Theoretical analysis and research hypothesis

2.1 social capital and rural residents’ conscious interpersonal household waste separation behavior.

According to social capital theory, social capital has an important impact on individual behavior and decision-making ( Putnam, 1993 ). The existing studies have also shown that social capital will affect farmers’ pro-environmental behavior decisions. For example, Yang et al. (2020) find that social capital helps farmers overcome the difficulties of applying organic fertilizer. The social capital includes the social network, social trust and social norms ( Putnam, 1993 ). The social network encompasses the interconnectedness of individuals, while social trust denotes the level of trust and confidence individuals have towards each other, and social norms represent the shared rules, standards, and expectations that govern social behavior within the society. Accordingly, social capital may affect the rural residents’ conscious interpersonal waste separation behavior through these three dimensions.

The social embedding theory argues that individuals are not completely independent when making decisions of their behaviors, and the social network embedded by the individuals will change their perception and affect their behavior and decision-making by providing them with information ( Killworth and Bernard, 1974 ). Moreover, because the rural residents are in a rural society linked by consanguinity, kinship, and business relationship ( Fei, 2012 ), they can get timely and accurate information and government policies on waste separation when they communicate with others. When the rural residents have more contact with others, they will get more comprehensive information about the waste separation and then have a better understanding of the ecological benefits brought by the waste separation, which is conducive to stimulating their inner awareness of environmental protection. Driven by environmental awareness, rural residents may take the initiative to appeal to others to separate waste in their lives to protect the ecological environment.

The trust theory suggests that when individuals interact with others based on trust, the sharing of knowledge and the exchange of information will affect the individual’s thoughts and attitude, which leads to the change in the individual’s behavior decision ( Uslaner and Conley, 2003 ; Jing et al., 2017 ; Harring et al., 2019 ). Accordingly, the rural residents communicate and interact with family and friends in their daily lives out of trust towards them ( Granovetter, 1985 ). Then, they can easily acquire the relevant knowledge of waste separation in communication and interaction, which will help them fully realize the important value of waste separation to the ecological environment, and affirm the necessity and rationality of waste separation for rural residents. As a result, the rural residents may actively suggest their relatives and friends to participate in waste separation in their lives.

The normative focus theory points out that when a certain social norm gains the focus of individual attention, the social norm will guide the occurrence of individual behavior by changing intrinsic values ( Cialdini et al., 1990 ). Many scholars have confirmed the important role of social norms in activating individual pro environmental behavior ( Sparkman and Walton, 2017 ; Bergquist et al., 2019 ). According to this, when the majority of the rural residents support the view that everyone should participate in waste separation, others may be affected by their imperceptible influence to realize that waste separation by everyone is highly respected in rural society, and that any violation will be met with isolation and unfriendly attitudes. As the rural residents are the members of the rural “acquaintance society”, who are concerned about the attitudes of others towards them, they will adopt the environmental protection idea that everyone should participate in the waste separation, which is similar or consistent with those of the majority in rural areas, in order to avoid being isolated or receiving unfriendly attitudes from others. Thus, the rural residents will take the initiative to remind, stop, and persuade others to change their behavior and participate in waste separation together. Based on this, this paper proposes the following hypothesis:

Hypothesis 1:. Social capital has a direct impact on rural residents’ conscious interpersonal waste separation behavior.

Hypothesis 1a:. Social networks have a direct impact on rural residents’ conscious interpersonal waste separation behavior.

Hypothesis 1b:. Social trust has a direct impact on rural residents’ conscious interpersonal waste separation behavior.

Hypothesis 1c:. The social norms have a direct impact on rural residents’ conscious interpersonal waste separation behavior.

2.2 The interaction of the dimensions of social capital

Previous studies have shown that the pairwise interactions of various dimensions of social capital affect individual behavioral decision-making. For example, Zou et al. (2020) find that the interaction between social networks and social norms will encourage rural residents to withdraw from homestead. Zhang et al. (2023) found that village level trust and various dimensions of social capital jointly affect the farmers’ climate-related disaster adaptation behavior. Similarly, the rural residents’ conscious interpersonal waste separation is also a kind of behavioral decision. Then the pairwise interaction between the dimensions of the social capital may also play a role in the process, which leads to the following hypotheses:

Hypothesis 2:. The pairwise interactions between the dimensions of the social capital affect the rural residents’ conscious interpersonal waste separation behavior.

2.3 The mediating effect of social cognition

The social cognitive theory presents that the environment affects individual cognition ( Putnam, 1993 ). Accordingly, social capital, as a special form of the social environment, can also influence the rural residents’ conscious interpersonal waste separation. Existing studies also show that social capital affects residents’ ecological cognition ( Xiao et al., 2021 ). From the view of social networks, rural residents can exchange experiences on waste separation with their neighbors, relatives, and friends, which helps them accumulate knowledge about waste separation to improve their ecological awareness. From the view of social trust, the rural residents’ understanding of waste separation and their ecological awareness will be developed in the process of sharing and exchanging the knowledge and policies of waste separation out of their trust in their family and friends. From the view of social norms, when the rural residents actively consider and accept the idea of waste separation to integrate into the group in which the majority promotes this pro-environment activity, their ecological cognition of environmental protection behavior such as waste separation will be formed. Therefore, the following hypotheses are proposed:

Hypothesis 3:. Social capital has a positive effect on rural residents’ ecological cognition.

Hypothesis 3a:. Social networks have a positive effect on rural residents’ ecological cognition.

Hypothesis 3b:. Social trust has a positive effect on rural residents’ ecological cognition.

Hypothesis 3c:. The social norms have a positive impact on rural residents’ ecological cognition.

The behavioral economic theory suggests that cognition is the prerequisite for individual behavioral decision-making, so the individual’s perception of things will affect their behavioral preferences and intentions ( Ajzen, 1991 ). Accordingly, when the rural residents realize that the failure of the waste separation will cause water and soil pollution and lead to ecological imbalance, while the proper implementation of waste separation can lead to the reutilization of resources and protect the ecological environment, they may have a positive attitude towards the waste separation and form the environmental protection concepts of waste separation. With these environmental protection concepts, rural residents may take the initiative to publicize the ecological benefits of waste separation to their relatives and friends and guide others to carry out the waste separation in their daily lives. Notably, previous studies also indicate that individuals’ ecological cognition can influence their pro-environmental behavior ( Kotchen and Reiling, 2000 ; Halkos and Matsiori, 2014 ; Paço and Lavrador, 2017 ; Chen et al., 2022 ). Therefore, this paper proposes the following hypothesis:

Hypothesis 4:. Ecological cognition has a positive impact on the rural residents’ conscious interpersonal waste separation behavior.

2.4 The mediating effect of subjective norm

Subjective norms refer to the social pressure perceived by individuals when implementing a certain behavior, mainly derived from external information and human factors ( Mayer et al., 1995 ). In terms of rural environment, social capital measures the collection of resources obtained by rural residents in the process of social interaction and connection with others, playing as one of the sources of subjective norms for garbage separation formed by rural residents. In terms of social networks, frequent communication and discussion among family and friends about household waste separation may lead rural residents to understand the healthy, environmental, and economic benefits related to household waste separation, which can help stimulate their belief in the need for household waste separation. In terms of social trust, the lack of separation of household waste can lead to environmental pollution and health threats, which can have adverse effects on oneself and others. In an environment where people are familiar with each other and mutually benefit, when rural residents are aware of the consequences of not classifying garbage, they may feel the need to sort it in their daily lives. In terms of social norms, when most rural residents carry out garbage separation in their daily lives and the government vigorously promotes garbage separation policies, it sends a signal of responsibility to rural residents to carry out garbage separation, helping them establish the concept of garbage separation. In addition, research by scholars has confirmed that social capital may affect the subjective norms of residents ( He et al., 2022 ; Xu et al., 2024 ). Based on this, the following assumptions are proposed:

Hypothesis 5:. Social capital has a positive impact on the ecological cognition of rural residents.

Hypothesis 5a:. Social networks have a positive impact on the ecological cognition of rural residents.

Hypothesis 5b:. Social trust has a positive impact on the ecological cognition of rural residents.

Hypothesis 5c:. Social norms have a positive impact on the ecological cognition of rural residents.

The Theory of Planned Behavior suggests that subjective norms can influence individual behavioral decisions ( Ajzen, 1991 ; Wang et al., 2018 ). Many studies have shown that subjective norms have a positive impact on guiding rural residents to implement household waste classification behavior. In rural areas, residents perceive a strong expectation from people around them, such as family and neighbors, to classify their household waste. They often choose to follow the expectations of those around them, generate a strong willingness to classify waste, actively participate in household waste classification, and actively advise others to reduce waste pollution in their daily lives. Therefore, the following assumptions are proposed:

Hypothesis 6:. Subjective norms have a positive impact on the conscious interpersonal garbage classification behavior of rural residents.

2.5 The moderating effect of government policy

In theory, the impact of social capital on the behavior of rural residents will vary depending on differences in government policies. On the one hand, government policies may help social capital guide rural residents to consciously implement interpersonal waste sorting behavior. The government’s promotion of garbage classification policies has expanded the channels for rural residents to acquire knowledge of household garbage classification. The garbage classification facilities provided by the government can provide convenience for rural residents to carry out garbage classification. In this context, rural residents are more likely to quickly learn about household waste classification policies through social interactions with others, and the information they obtain about household waste classification may be more comprehensive and accurate. Therefore, they have a stronger sense of identification and understanding of household waste classification policies, which is conducive to actively discussing household waste classification with their surrounding relatives and friends, and actively teaching household waste classification skills. On the other hand, government policies may also lead to negative impacts of social capital on the conscious interpersonal garbage sorting behavior of rural residents. Government policies regulate the behavior of residents through behavior guidance and value guidance ( He et al., 2022 ), and the promotion of household waste classification policies in rural areas cannot do without the support and support of rural acquaintance society ( Jiang and Li, 2023 ). The government’s promotion of the four-classification system for household waste poses significant behavioral challenges for rural residents, or if administrative measures are too strict, it may lead to fear or resistance towards household waste classification behavior among rural residents. Rural residents with large social networks and a core position in their relationship networks may lead others to make negative evaluations of government policies, resulting in poor enthusiasm and participation in household waste classification, and even hinder others from doing so. At this point, the implementation of government policies may lead to a negative impact of social capital on the conscious interpersonal garbage sorting behavior of rural residents. Based on the above analysis, this article proposes the following assumptions:

Hypothesis 7:. Government policies has a moderating effect on the relationship between social capital and the conscious interpersonal garbage separation behavior of rural residents.

Based on the above analysis, this paper constructs a theoretical analysis model of the impact of social capital on the rural residents’ conscious interpersonal waste separation behavior ( Figure 1 ).

FIGURE 1 . The theoretical analysis model.

3 Materials and methods

3.1 regression model.

This paper utilizes a regression model for analysis. Considering that the dependent variable, rural residents’ conscious interpersonal waste separation behavior, is measured by the average score of three items and treated as a continuous variable, we construct the following regress model to analyze whether social capital has an impact on rural residents’ conscious interpersonal waste separation behavior:

In this equation, Y stands for the rural residents’ conscious interpersonal waste separation behavior, Z B i denotes the social capital, including social network, social trust and social norms. C o n is a collection of a series of control variables, such as ecological values, gender, age and income, etc., and ε 1 is a random error term.

Given that the impact of each dimension of social capital on individual behavior is interdependent ( Zou et al., 2020 ), this study introduces pairwise interaction terms among the three dimensions of social network, social trust, and social norms in the baseline regression model, to investigate the interactive effects of each dimension. The constructed model is as follows:

Where, the variable, W L , X R and G F denote the social network, social trust and social norms respectively. The interaction terms of social network and social trust, social network and social norms, and social trust and social norms are shown as W L * X R 、 W L * G F , X R * G F respectively. The meanings of other variables are consistent with Eq. 1 .

Moreover, this paper examines the mediation role of the ecological cognition in the relationship between social capital and the rural residents’ conscious interpersonal waste separation behavior with the aim to examine whether social networks, social trust, and social norms can indirectly influence rural residents’ conscious interpersonal waste separation behavior through ecological cognition. Based on the research of Wen and Ye (2014) , the mediating mechanism of ecological cognition is tested using the stepwise regression method, and the model is constructed as follows:

Where the variable M i   i , 2 respectively denote ecological cognition and subjective norm, and the meanings of the other variables are consistent with Eq. 2 .

3.2 Study area

This research takes Jiangxi Province as a study area which is located in the southeast of China. Selected as the first batch of national pilot zones for ecological conservation with a good ecological foundation in 2014, Jiangxi Province has achieved comprehensive implementation of household waste separation in rural areas. As of March 2022, 14 counties (cities, districts) in Jiangxi Province have started the pilot of rural household waste separation, including four national demonstration counties such as Graungfeng City in Shangrao City, Ruichang City in Jiujiang city, Chongyi county in Ganzhou city and Jing’an county in Yichun city. However, the key to implement the normalization of waste separation effectively is the initiative of rural residents to conduct waste separation. At present, the embedding of social capital into rural environmental governance has become an important method to alleviate rural environmental pollution ( Cao et al., 2023 ; Xu et al., 2024 ). It is very important for rural residents to actively promote others to separate waste at its source in their daily lives through interpersonal activities, which is crucial for protecting and consolidating the existing achievements of ecological civilization construction. Therefore, considering its representativeness, this paper selects Jiangxi as the study area to explore the impact of social capital on the rural residents’ conscious interpersonal household waste separation behavior.

3.3 Survey design

The survey questionnaire of this study includes the following three parts. The first part concerns the rural residents’ conscious interpersonal waste separation behavior. Refer to Chen et al. (2017) , we designed three questions such as “ I will take the initiative to persuade my relatives and friends around me to carry out waste separation”, which are measured by a 5-point Likert scale that 1 represents for strongly disagree and 5 for strongly agree. The second part includes questions related to social capital and its dimensions, ecological cognition, as well as ecological values. According to Putnam (1993) , we consider three dimensions of social capital: social network, social trust and social norm, which are measured by the questions that “ I often talk to my friends and family about environmental issues”, " I have a high level of trust in environmental laws and regulations”, “ People around me are ashamed of destroying the ecological environment” respectively. The social capital is measured as the mean of these three dimensions. Ecological cognition is measured by the question “I have a high level of knowledge about rural ecological protection” referring to Halkos and Matsiori (2014) . The ecological values are measured by three questions such as “I would like to protect the environment in my daily life” according to Stern et al. (1999) . Subjective norm is measured using the Cordano and Frieze (2000) scale, which included three questions: “Classifying household waste is more in line with my status”, and 5-point Likert scale is used for measurement. The government policy draws on the research of Chen (2018) and selects “whether there are facilities for sorting and disposing of household waste in your village (such as garbage bins for sorting and disposing)" for measurement, where 0 = none and 1 = yes. The third part regards the respondents’ socio-demographic characteristics including gender, age, income, and education level.

3.4 Sample and data collection

To ensure the quality of the survey, the survey steps are as follows: First, we selected the samples for pre-survey using the stratified random sampling method according to the economic development level of the regions in Jiangxi Province and the characteristics of the distribution of pilot projects on the rural households’ waste separation. A total of 160 samples were collected through an online questionnaire survey system and then preliminary tests on the data were conducted to assess their reliability and validity. After eliminating variables with unsatisfactory reliability and validity, and trimming unnecessary questions, the formal questionnaire was formed. Secondly, we adopted various methods such as random household visits, group interviews, random visits, and online questionnaires to conduct the investigation. A total of 774 samples were collected. Finally, we excluded the invalid samples and there are 712 valid samples obtained, with a validity rate of 92%.

4.1 Demographic profile of the respondents

Table 1 displays the descriptive statistics of the sample. The respondents are primarily male, making up 54.78% of the total. The overall income level is relatively low, with 56.17% of the samples having an annual per capita income of less than 30,000 yuan, 35.23% of the samples between 30,000 and 80,000 yuan, and 8.60% of the samples more than 80,000 yuan. Compared with the Jiangxi Statistical Yearbook 2022 which states that the male population accounted for 51.70% of the total population and the average annual income of rural residents was 23,400 yuan in Jiangxi Province in 2021, it is evident that the gender distribution and income structure of the samples align with the actual situation in Jiangxi Province, which suggests that the survey data is somewhat representative.

TABLE 1 . Description of the basic characteristics of the rural population of the sample.

4.2 The common method biases test

To prevent common method bias from affecting the research results, this paper employs the Harman single-factor test method for testing. Using Stata 16.0, we conducted an exploratory factor analysis on all items in the questionnaire. The load value of the first principal component, which was obtained without rotation, was used to represent the degree of data homogeneity bias. The test results reveal that there are a total of four factors. Furthermore, the load value of the first factor is below the standard value, indicating the absence of significant common method bias.

4.3 Reliability and validity test

To ensure the reliability of the scale, we utilize Stata 16.0 software for reliability and validity analysis, using Cronbach’s alpha coefficient (α) and composite reliability (CR) values to test the reliability of the scale. The results show that the Cronbach’s alpha coefficient (α) and composite reliability (CR) values for each latent variable exceeded the critical value of 0.7, thereby indicating a high level of reliability and excellent stability for the scale.

The validity analysis encompassed convergent validity and discriminant validity. The convergent validity analysis was conducted using standardized factor loadings and average variance extraction (AVE) values. The results demonstrate that the standardized factor loadings for each latent variable were greater than 0.5, and the AVE values were all above 0.8, indicating good convergent validity for each latent variable. For the discriminant validity test, it is observed that the square root of the AVE value for each latent variable was greater than its correlation coefficient with other latent variables, suggesting a high discriminative power between each of these latent variables. In addition, with the help of SPSS, discriminant validity was tested by calculating the Heterogeneity to Elemental Ratio (HTMT). As shown in Table 2 , the majority of HTMT values are within the standard range (i.e., less than 0.85), indicating that the scale has good discriminant validity.

TABLE 2 . Results of HTMT test.

4.4 Contingency table analysis

In order to analyze the heterogeneous characteristics of rural residents’ conscious interpersonal garbage classification behavior from the perspective of social capital sub dimensions, this section selects social capital (social network, social norms, social trust) as the differentiation indicator and applies a contingency table with the conscious interpersonal garbage classification behavior of rural residents. The results are shown in Table 3 .

TABLE 3 . Contingency table analysis.

According to the data in Table 3 , there is a consistent positive correlation between the mean dimensions of social capital such as social network, social norms, and social network, and the performance of rural residents in their conscious interpersonal garbage classification behavior. This indicates that the wider the social network of rural residents, the stronger the constraints on household waste classification among the villagers, and the higher the level of trust among the villagers, the more likely rural residents are to actively persuade or assist others in garbage classification in their daily lives. Among the three elements of social capital, social trust plays the strongest role.

4.5 Baseline regression results

To ensure accurate model estimation, a multicollinearity test was conducted using Stata 16.0 software on the explanatory variables involved in the study. The results indicate that the average variance inflation factor (VIF) for each explanatory variable is 1.68, which is below the standard value of 2.0. This suggests that there is no serious issue of multicollinearity among the explanatory variables. Table 4 displays the regression outcomes derived from Eq. 1 . Specifically, in Model (1), social capital serves as the independent variable, and the results indicate a significant positive effect of social capital on the rural residents’ conscious interpersonal waste separation behavior. Model (2) distinguishes social capital into social networks, social trust, and social norms, and the results show that social networks and social trust have a significant positive impact on the rural residents’ conscious interpersonal waste separation behavior, while the impact of social norms is not significant.

TABLE 4 . Estimation results of baseline regression.

4.6 Results of interaction effects

According to Eq. 2 , we introduce two-way interactions between social networks, social trust, and social norms to analyze the interactive effects among dimensions of social capital. The results are shown in Table 5 . It can be observed that there is a significant negative interaction effect between social networks and social norms, indicating a substitution relationship between these two dimensions. However, there is a significant positive interaction between social trust and social norms, indicating that social trust and social norms act as amplifiers for each other. Hence, Hypothesis H3 is accepted.

TABLE 5 . Results of interaction effects between dimensions of social capital.

4.7 Results of mediating effects

According to Eqs 3−5 , the mediating variable of ecological cognition and subjective norm are introduced to examine whether social trust, social networks, and social norms affect the rural residents’ conscious interpersonal waste separation behavior through ecological cognition. The estimation results of the mediating effect model of ecological cognition are presented in Table 6 . As demonstrated in Model (1), social networks and social trust have a significant have a significant positive impact on rural residents’ conscious interpersonal waste separation behavior. Model (2) indicates that social networks, social trust, and social norms have a significant positive influence on rural residents’ ecological cognition, leading to the acceptance of Hypotheses H3, H3a, H3b, and H3c. Model (3) demonstrates that ecological cognition has a significant positive impact on rural residents’ conscious interpersonal waste separation behavior and Hypothesis H4 is henceforth accepted. By combining the results of models (1), (2), and (3), it can be concluded that ecological cognition plays a partial mediating role in the influence of social networks and social trust on rural residents’ conscious interpersonal waste separation behavior, while it fully mediates the effect of social norms on rural residents’ conscious interpersonal waste separation behavior.

TABLE 6 . Estimation result of the mediating effects.

Furthermore, as shown in Model (4), the three dimensions of social capital (social network, social trust, and social norms) have a significant promoting effect on the subjective norms of rural residents, indicating the validity of hypotheses H5, H5a, H5b, and H5c. Model (5) indicates that the stronger the subjective norms of rural residents, the more likely they are to consciously implement interpersonal garbage classification behavior in their daily lives, i.e., accept hypothesis H6. Based on the estimation results of the comprehensive models (1), (4), and (5), it can be found that subjective norms play a partial mediating role in the influence of social networks and social trust on the conscious interpersonal garbage classification behavior of rural residents, and there is a complete mediating role in the influence of social norms on the conscious interpersonal garbage classification behavior of rural residents.

To examine the significance of the mediating effect of ecological cognition and subjective norm, the Bootstrap interval analysis was conducted and the results are shown in Table 7 . It demonstrates that for the three pathways are “social networks-ecological cognition-conscious interpersonal waste separation behavior”, “social trust-ecological cognition-conscious interpersonal waste separation behavior” and “social norms-ecological cognition-conscious interpersonal waste separation behavior,” the confidence intervals for the mediating effect of ecological cognition do not include 0. This indicates that there is a significant mediating effect of ecological cognition in the relationship between social networks, social trust, social norms, and rural residents’ conscious interpersonal waste separation behavior. In terms of the mediating effect of subjective norms, among the three mediating paths of “social network subjective norms conscious interpersonal garbage sorting behavior”, “social trust subjective norms conscious interpersonal garbage sorting behavior”, and “social norms subjective norms conscious interpersonal garbage sorting behavior”, the confidence interval of the mediating effect of subjective norms does not include 0. This indicates that subjective norms in social networks, social trust. There is a significant mediating effect between social norms and conscious interpersonal garbage sorting behavior among rural residents.

TABLE 7 . Results of the Bootstrap test for the mediating effect.

4.8 Result of moderating effect

In order to verify the moderating effect of government policies on the conscious interpersonal garbage separation behavior of rural residents in terms of social capital, the samples were divided into groups with and without garbage separation facilities based on the values of government policy variables. Regression was performed on Eq. 1 , and the results are shown in Table 8 . Comparing models (1) and (2) in Table 8 , it can be seen that regardless of whether the village is equipped with garbage sorting bins, the impact of social capital on the conscious interpersonal garbage sorting behavior of rural residents is significantly positive. However, the regression coefficient of social capital is larger in the group without garbage sorting facilities (0.606 > 0.443), which means that in areas without garbage sorting facilities, social capital has a greater impact on guiding rural residents to consciously implement interpersonal waste sorting behavior. This indicates that government policies play a moderating role in the relationship between social capital and the conscious interpersonal garbage classification behavior of rural residents, confirming hypothesis H7.

TABLE 8 . Results of the moderating effect.

5 Discussion and policy implication

5.1 discussion.

This study indicates that social capital plays a significant role in the rural residents’ conscious interpersonal waste separation behavior, which is consistent with existing literature. According to existing literature, enhancing social capital is an effective way to encourage residents to carry out household waste separation behavior ( Kithia, 2015 ; Hua et al., 2021 ). This study confirms that this influence is also relevant in the realm of conscious interpersonal waste separation behavior among rural residents.

This paper further examines the interactive influence of the three dimensions of social capital, social networks, social trust, and social norms, on the rural residents’ conscious interpersonal waste separation behavior. In particular, this paper discovers that social networks and social norms are substitutive, while social trust and norms are complementary in guiding rural residents to consciously engage in interpersonal waste separation behavior. This result further expands the research of Hsu and Hung (2013) , which argues that the dimensions of social capital are not independent, but may have interactive effects. The results of this paper once again confirm this viewpoint.

This paper also finds that the effects of the dimensions of social capital are different. Social trust and social networks not only have a direct impact on the rural residents’ conscious interpersonal waste separation behavior but also have an indirect impact through ecological cognition and subjective norm. This contradicts the conclusion of Wang and Zhang (2022) , which argues that social trust and social networks do not have a direct impact on residents’ waste separation behavior but can only indirectly drive residents to carry out waste separation by shaping environmental norms. One potential explanation for this is that within the context of China’s commitment to ecological civilization construction, rural residents have a constant flow of information and resources about waste separation during their interactions with family and friends. People often believe that the surrounding residents are aware and committed to carrying out waste separation. Social trust and networks have a direct impact on motivating rural residents to participate in household waste separation through interpersonal activities.

Moreover, this paper discovers that ecological cognition completely mediates the influence of social norms on the rural residents’ conscious interpersonal waste separation behavior, supporting the findings of Xiao et al. (2021) who conducted a study on the rational fertilization behavior of Chinese farmers, and reveals that the social norms play a significant role in shaping farmers’ ecological cognition. The possible reason could be that amidst a long-term social environment that advocates for “everyone to participate in waste separation”, rural residents may come to realize that protecting the ecological environment necessitates collective participation. This in turn enhances their awareness of the importance of ecological environment protection. Driven by ecological awareness, rural residents may proactively share government policies and knowledge related to waste separation in their daily interpersonal activities, encouraging surrounding residents to participate in waste separation.

Finally, it is found that government policies have a significant moderating effect on the relationship between social capital and the conscious interpersonal garbage classification behavior of rural residents. It can be seen that government policies play an important role in the classification of rural household waste in China. This indirectly confirms the views of many scholars at home and abroad that government policies are an important factor affecting residents’ pro environmental behavior ( Chen, 2018 ; Knickmeyer, 2020 ; Li et al., 2022 ; Cheng et al., 2023 ). This conclusion indicates that in guiding rural residents to actively participate in the process of household waste separation, it is necessary to reasonably equip household waste classification facilities and avoid the mixing of waste classification, collection, and treatment environment.

5.2 Policy implication

The above results draw the following policy implications: First and foremost, fostering the social capital of rural residents must be emphasized to promote rural residents’ conscious interpersonal waste separation behavior as the above results indicate that social capital plays an important role in it. The government can utilize WeChat, TikTok, and other Internet platforms to establish a communication group for villagers, which will bring together both those who work outside and domestic farmers. This initiative will not only strengthen the ties between rural residents but also foster the sharing of environmental protection knowledge and policies such as waste separation. The group chat will also address the concerns of rural residents, encouraging active participation in exchanges and interactions. This approach facilitates better understanding and trust between rural residents, ensuring they accurately receive environmental protection information and policies in their daily lives. It also helps to raise awareness about the environmental benefits that result from practices like waste separation to promote the initiative of rural residents to encourage others to get involved in the process of separating household waste.

Second, it is necessary to establish an environmental governance system that combines social trust and social norms considering of the complementary effect. By organizing the grassroots work teams, which mainly consist of village cadres, highly respected rural residents in the village, and village group leaders, the government can organize various environmental protection knowledge training, strengthen communication and learning among villagers, and enhance the level of social trust among them. Simultaneously, a new rural style that advocates civilized environmental protection can be established through various environmental protection awards and commendations, such as “Green Family” and “Beautiful Courtyard”, creating a virtuous cycle where social trust and social norms mutually promote each other. The government can take multiple measures to promote rural residents’ active engagement in waste separation and interpersonal behavior in their daily lives.

Third, the government should strengthen publicity and education efforts to heighten the ecological cognition and subjective norm of rural residents by utilizing both online and offline platforms considering the mediating effects. Online, it can actively employ official microblogs, WeChat official accounts, TikTok video numbers, and other online communication platforms to conduct environmental education and promotion. Offline, it can place and hang environmental slogans and propaganda posters on outdoor walls in rural areas, and delegate village cadres and respected rural residents to conduct oral propaganda on waste separation and other environmental protection behaviors in daily life, effectively disseminating environmental protection knowledge.

6 Conclusion

Social capital is a crucial way to promote rural residents to carry out conscious interpersonal waste separation behavior in their daily lives, which contributes to the establishment of a beautiful China. By utilizing the survey data of 712 rural residents in Jiangxi Province, the main findings are as follows.

Social capital has a facilitating impact on the rural residents’ conscious interpersonal waste separation behavior in their daily lives. Specifically, social network and social trust both contribute to rural residents’ active participation in waste separation but social norms have no significant influence. The interaction between social network and social norms negatively affects rural residents’ conscious interpersonal household waste separation behavior, whereas the interaction between social trust and social norms positively influences it. There is no interaction effect between social trust and social network. Moreover, the ecological cognition and subjective norm play a significant mediating role in the relationship between social network, social trust, and social norms and the rural residents’ conscious interpersonal waste separation behavior, while the government policies plays a significant moderating effect. The aforementioned findings contain policy implications for enhancing the rural residents’ conscious interpersonal waste separation behavior within China and other developing countries.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

YT: Formal Analysis, Methodology, Resources, Writing–review and editing. NL: Formal Analysis, Methodology, Writing–review and editing. JY: Data curation, Visualization, Writing–original draft. YL: Data curation, Visualization, Writing–review and editing. CL: Conceptualization, Supervision, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was supported by the National Natural Science Foundation of China (Grant No. 72064030, 72264015, and 71864018), and the Humanities and Social Sciences Foundation of Universities in Jiangxi Province (Grant No. GL21132).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: social capital, conscious interpersonal waste separation behavior, rural residents, ecological cognition, China

Citation: Teng Y, Li N, Yang J, Liu Y and Liu C (2024) Study on the impact of social capital on the rural residents’ conscious interpersonal waste separation behavior: evidence from Jiangxi province, China. Front. Environ. Sci. 12:1363240. doi: 10.3389/fenvs.2024.1363240

Received: 17 January 2024; Accepted: 28 February 2024; Published: 18 March 2024.

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*Correspondence: Changjin Liu, [email protected]