difference between analysis and discussion in dissertation

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Dissertations 5: findings, analysis and discussion: home.

• Results/Findings

Alternative Structures

The time has come to show and discuss the findings of your research. How to structure this part of your dissertation? 

Dissertations can have different structures, as you can see in the dissertation  structure  guide.

Dissertations organised by sections

Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too. 

A) More common for scientific dissertations and quantitative methods:

- Results chapter 

- Discussion chapter

Example: 

• Introduction
• Literature review
• Methodology
• (Recommendations)

if you write a scientific dissertation, or anyway using quantitative methods, you will have some  objective  results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.  

B) More common for qualitative methods

- Analysis chapter. This can have more descriptive/thematic subheadings.

- Discussion chapter. This can have more descriptive/thematic subheadings.

• Case study of Company X (fashion brand) environmental strategies 
• Successful elements
• Lessons learnt
• Criticisms of Company X environmental strategies 
• Possible alternatives

C) More common for qualitative methods

- Analysis and discussion chapter. This can have more descriptive/thematic titles.

• Case study of Company X (fashion brand) environmental strategies 

If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s). 

Thematic dissertations

If the structure of your dissertation is thematic ,  you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles. 

• Background on the conflict in Yemen (2004-present day)
• Classification of the conflict in international law  
• International law violations
• Options for enforcement of international law
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What is the difference between results, discussion, and conclusions in writing a research paper?

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Popular answers (1)

• Results section : In this section simply present what you found,
• Discussion section : (a) Try to explain "What do your results mean?", and (b) how they relate to the literature,
• Conclusions section : Re-state the main points in a new concise way that you want your readers to remember.

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All Answers (158)

• Results : empirical findings of your research method used.
• Discussion : explanation or interpretation of your above results / findings e.g. why these relationships are in/significant, weak / strong etc.  Sometimes further literature review might be required as part of the explanation in discussion section.  Some researchers also include answering their research questions posed at the beginning of the research in this section too.
• Conclusions : summary of your research.  Some researchers also include: knowledge contribution, research limitation & future research recommendation in conclusions section.

• Annesley, T. M. (2010) The discussion section: your closing argument, Clinical Chemistry, 56 , 11, pp. 1671-1674.
• Assan, J. 'Writing the Conclusion Chapter: the Good, the Bad and the Missing', Development Studies Association Annual Postgraduate Workshop , University of East Anglia (UEA), Norwich, UK, 1-3 June, 2009: University of Liverpool, 1-8.
• Bavdekar, S. B. (2015) Writing the discussion section: Describing the significance of the study findings, Journal of the Association of Physicians of India, 63 , 11, pp. 40-42.
• Bunton, D. (2005) The structure of Ph.D conclusion chapters, Journal of English for Academic Purposes, 4 , 3, pp. 207-224.
• Gilgun, J. F. (2005) "Grab" and good science: Writing up the results of qualitative research, Qualitative health research, 15 , 2, pp. 256-262.
• Mukherjee, A. and Lodha, R. (2016) Writing the results, Indian pediatrics, 53 , 5, pp. 409-415.
• Perry, C. (1998) A structured approach to presenting phd theses: notes for candidates and their supervisors, Australasian Marketing Journal, 6 , 1, pp. 1-57.

• DISCUSSION_D.Prokopowicz_....Discussions, polemics, analysis of literature, results of scientific research and conclusions...published scientific paper s..jpg 263 kB

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Writing the Dissertation - Guides for Success: Results and Discussion

• Writing the Dissertation Homepage
• Overview and Planning
• Research Question
• Literature Review
• Methodology
• Results and Discussion
• The Difference
• What to Avoid

Overview of writing the results and discussion

The results and discussion follow on from the methods or methodology chapter of the dissertation. This creates a natural transition from how you designed your study, to what your study reveals, highlighting your own contribution to the research area.

Disciplinary differences

Please note: this guide is not specific to any one discipline. The results and discussion can vary depending on the nature of the research and the expectations of the school or department, so please adapt the following advice to meet the demands of your project and department. Consult your supervisor for further guidance; you can also peruse our  Writing Across Subjects guide .

Guide contents

As part of the Writing the Dissertation series, this guide covers the most common conventions of the results and discussion chapters, giving you the necessary knowledge, tips and guidance needed to impress your markers! The sections are organised as follows:

• The Difference  - Breaks down the distinctions between the results and discussion chapters.
• Results  - Provides a walk-through of common characteristics of the results chapter.
• Discussion - Provides a walk-through of how to approach writing your discussion chapter, including structure.
• What to Avoid  - Covers a few frequent mistakes you'll want to...avoid!
• FAQs  - Guidance on first- vs. third-person, limitations and more.
• Checklist  - Includes a summary of key points and a self-evaluation checklist.

Training and tools

• The Academic Skills team has recorded a Writing the Dissertation workshop series to help you with each section of a standard dissertation, including a video on writing the results and discussion   (embedded below).
• The dissertation planner tool can help you think through the timeline for planning, research, drafting and editing.
• iSolutions offers training and a Word template to help you digitally format and structure your dissertation.

Introduction

The results of your study are often followed by a separate chapter of discussion. This is certainly the case with scientific writing. Some dissertations, however, might incorporate both the results and discussion in one chapter. This depends on the nature of your dissertation and the conventions within your school or department. Always follow the guidelines given to you and ask your supervisor for further guidance.

As part of the Writing the Dissertation series, this guide covers the essentials of writing your results and discussion, giving you the necesary knowledge, tips and guidance needed to leave a positive impression on your markers! This guide covers the results and discussion as separate – although interrelated – chapters, as you'll see in the next two tabs. However, you can easily adapt the guidance to suit one single chapter – keep an eye out for some hints on how to do this throughout the guide.

Results or discussion - what's the difference?

To understand what the results and discussion sections are about, we need to clearly define the difference between the two.

The results should provide a clear account of the findings . This is written in a dry and direct manner, simply highlighting the findings as they appear once processed. It’s expected to have tables and graphics, where relevant, to contextualise and illustrate the data.

Rather than simply stating the findings of the study, the discussion interprets the findings  to offer a more nuanced understanding of the research. The discussion is similar to the second half of the conclusion because it’s where you consider and formulate a response to the question, ‘what do we now know that we didn’t before?’ (see our Writing the Conclusion   guide for more). The discussion achieves this by answering the research questions and responding to any hypotheses proposed. With this in mind, the discussion should be the most insightful chapter or section of your dissertation because it provides the most original insight.

Across the next two tabs of this guide, we will look at the results and discussion chapters separately in more detail.

Writing the results

The results chapter should provide a direct and factual account of the data collected without any interpretation or interrogation of the findings. As this might suggest, the results chapter can be slightly monotonous, particularly for quantitative data. Nevertheless, it’s crucial that you present your results in a clear and direct manner as it provides the necessary detail for your subsequent discussion.

Note: If you’re writing your results and discussion as one chapter, then you can either:

1) write them as distinctly separate sections in the same chapter, with the discussion following on from the results, or...

2) integrate the two throughout by presenting a subset of the results and then discussing that subset in further detail.

Next, we'll explore some of the most important factors to consider when writing your results chapter.

How you structure your results chapter depends on the design and purpose of your study. Here are some possible options for structuring your results chapter (adapted from Glatthorn and Joyner, 2005):

• Chronological – depending on the nature of the study, it might be important to present your results in order of how you collected the data, such as a pretest-posttest design.
• Research method – if you’ve used a mixed-methods approach, you could isolate each research method and instrument employed in the study.
• Research question and/or hypotheses – you could structure your results around your research questions and/or hypotheses, providing you have more than one. However, keep in mind that the results on their own don’t necessarily answer the questions or respond to the hypotheses in a definitive manner. You need to interpret the findings in the discussion chapter to gain a more rounded understanding.
• Variable – you could isolate each variable in your study (where relevant) and specify how and whether the results changed.

Tables and figures

For your results, you are expected to convert your data into tables and figures, particularly when dealing with quantitative data. Making use of tables and figures is a way of contextualising your results within the study. It also helps to visually reinforce your written account of the data. However, make sure you’re only using tables and figures to supplement , rather than replace, your written account of the results (see the 'What to avoid' tab for more on this).

Figures and tables need to be numbered in order of when they appear in the dissertation, and they should be capitalised. You also need to make direct reference to them in the text, which you can do (with some variation) in one of the following ways:

Figure 1 shows…

The results of the test (see Figure 1) demonstrate…

The actual figures and tables themselves also need to be accompanied by a caption that briefly outlines what is displayed. For example:

Table 1. Variables of the regression model

Table captions normally appear above the table, whilst figures or other such graphical forms appear below, although it’s worth confirming this with your supervisor as the formatting can change depending on the school or discipline. The style guide used for writing in your subject area (e.g., Harvard, MLA, APA, OSCOLA) often dictates correct formatting of tables, graphs and figures, so have a look at your style guide for additional support.

Using quotations

If your qualitative data comes from interviews and focus groups, your data will largely consist of quotations from participants. When presenting this data, you should identify and group the most common and interesting responses and then quote two or three relevant examples to illustrate this point. Here’s a brief example from a qualitative study on the habits of online food shoppers:

Regardless of whether or not participants regularly engage in online food shopping, all but two respondents commented, in some form, on the convenience of online food shopping:

"It’s about convenience for me. I’m at work all week and the weekend doesn’t allow much time for food shopping, so knowing it can be ordered and then delivered in 24 hours is great for me” (Participant A).

"It fits around my schedule, which is important for me and my family” (Participant D).

"In the past, I’ve always gone food shopping after work, which has always been a hassle. Online food shopping, however, frees up some of my time” (Participant E).

As shown in this example, each quotation is attributed to a particular participant, although their anonymity is protected. The details used to identify participants can depend on the relevance of certain factors to the research. For instance, age or gender could be included.

Writing the discussion

The discussion chapter is where “you critically examine your own results in the light of the previous state of the subject as outlined in the background, and make judgments as to what has been learnt in your work” (Evans et al., 2014: 12). Whilst the results chapter is strictly factual, reporting on the data on a surface level, the discussion is rooted in analysis and interpretation , allowing you and your reader to delve beneath the surface.

Next, we will review some of the most important factors to consider when writing your discussion chapter.

Like the results, there is no single way to structure your discussion chapter. As always, it depends on the nature of your dissertation and whether you’re dealing with qualitative, quantitative or mixed-methods research. It’s good to be consistent with the results chapter, so you could structure your discussion chapter, where possible, in the same way as your results.

When it comes to structure, it’s particularly important that you guide your reader through the various points, subtopics or themes of your discussion. You should do this by structuring sections of your discussion, which might incorporate three or four paragraphs around the same theme or issue, in a three-part way that mirrors the typical three-part essay structure of introduction, main body and conclusion.

Figure 1: The three-part cycle that embodies a typical essay structure and reflects how you structure themes or subtopics in your discussion.

This is your topic sentence where you clearly state the focus of this paragraph/section. It’s often a fairly short, declarative statement in order to grab the reader’s attention, and it should be clearly related to your research purpose, such as responding to a research question.

This constitutes your analysis where you explore the theme or focus, outlined in the topic sentence, in further detail by interrogating why this particular theme or finding emerged and the significance of this data. This is also where you bring in the relevant secondary literature.

This is the evaluative stage of the cycle where you explicitly return back to the topic sentence and tell the reader what this means in terms of answering the relevant research question and establishing new knowledge. It could be a single sentence, or a short paragraph, and it doesn’t strictly need to appear at the end of every section or theme. Instead, some prefer to bring the main themes together towards the end of the discussion in a single paragraph or two. Either way, it’s imperative that you evaluate the significance of your discussion and tell the reader what this means.

A note on the three-part structure

This is often how you’re taught to construct a paragraph, but the themes and ideas you engage with at dissertation level are going to extend beyond the confines of a short paragraph. Therefore, this is a structure to guide how you write about particular themes or patterns in your discussion. Think of this structure like a cycle that you can engage in its smallest form to shape a paragraph; in a slightly larger form to shape a subsection of a chapter; and in its largest form to shape the entire chapter. You can 'level up' the same basic structure to accommodate a deeper breadth of thinking and critical engagement.

Using secondary literature

Your discussion chapter should return to the relevant literature (previously identified in your literature review ) in order to contextualise and deepen your reader’s understanding of the findings. This might help to strengthen your findings, or you might find contradictory evidence that serves to counter your results. In the case of the latter, it’s important that you consider why this might be and the implications for this. It’s through your incorporation of secondary literature that you can consider the question, ‘What do we now know that we didn’t before?’

Limitations

You may have included a limitations section in your methodology chapter (see our Writing the Methodology guide ), but it’s also common to have one in your discussion chapter. The difference here is that your limitations are directly associated with your results and the capacity to interpret and analyse those results.

Think of it this way: the limitations in your methodology refer to the issues identified before conducting the research, whilst the limitations in your discussion refer to the issues that emerged after conducting the research. For example, you might only be able to identify a limitation about the external validity or generalisability of your research once you have processed and analysed the data. Try not to overstress the limitations of your work – doing so can undermine the work you’ve done – and try to contextualise them, perhaps by relating them to certain limitations of other studies.

Recommendations

It’s often good to follow your limitations with some recommendations for future research. This creates a neat linearity from what didn’t work, or what could be improved, to how other researchers could address these issues in the future. This helps to reposition your limitations in a positive way by offering an action-oriented response. Try to limit the amount of recommendations you discuss – too many can bring the end of your discussion to a rather negative end as you’re ultimately focusing on what should be done, rather than what you have done. You also don’t need to repeat the recommendations in your conclusion if you’ve included them here.

What to avoid

This portion of the guide will cover some common missteps you should try to avoid in writing your results and discussion.

Over-reliance on tables and figures

It’s very common to produce visual representations of data, such as graphs and tables, and to use these representations in your results chapter. However, the use of these figures should not entirely replace your written account of the data. You don’t need to specify every detail in the data set, but you should provide some written account of what the data shows, drawing your reader’s attention to the most important elements of the data. The figures should support your account and help to contextualise your results. Simply stating, ‘look at Table 1’, without any further detail is not sufficient. Writers often try to do this as a way of saving words, but your markers will know!

Ignoring unexpected or contradictory data

Research can be a complex process with ups and downs, surprises and anomalies. Don’t be tempted to ignore any data that doesn’t meet your expectations, or that perhaps you’re struggling to explain. Failing to report on data for these, and other such reasons, is a problem because it undermines your credibility as a researcher, which inevitably undermines your research in the process. You have to do your best to provide some reason to such data. For instance, there might be some methodological reason behind a particular trend in the data.

Including raw data

You don’t need to include any raw data in your results chapter – raw data meaning unprocessed data that hasn’t undergone any calculations or other such refinement. This can overwhelm your reader and obscure the clarity of the research. You can include raw data in an appendix, providing you feel it’s necessary.

Presenting new results in the discussion

You shouldn’t be stating original findings for the first time in the discussion chapter. The findings of your study should first appear in your results before elaborating on them in the discussion.

Overstressing the significance of your research

It’s important that you clarify what your research demonstrates so you can highlight your own contribution to the research field. However, don’t overstress or inflate the significance of your results. It’s always difficult to provide definitive answers in academic research, especially with qualitative data. You should be confident and authoritative where possible, but don’t claim to reach the absolute truth when perhaps other conclusions could be reached. Where necessary, you should use hedging (see definition) to slightly soften the tone and register of your language.

Definition: Hedging refers to 'the act of expressing your attitude or ideas in tentative or cautious ways' (Singh and Lukkarila, 2017: 101). It’s mostly achieved through a number of verbs or adverbs, such as ‘suggest’ or ‘seemingly.’

Q: What’s the difference between the results and discussion?

A: The results chapter is a factual account of the data collected, whilst the discussion considers the implications of these findings by relating them to relevant literature and answering your research question(s). See the tab 'The Differences' in this guide for more detail.

Q: Should the discussion include recommendations for future research?

A: Your dissertation should include some recommendations for future research, but it can vary where it appears. Recommendations are often featured towards the end of the discussion chapter, but they also regularly appear in the conclusion chapter (see our Writing the Conclusion guide   for more). It simply depends on your dissertation and the conventions of your school or department. It’s worth consulting any specific guidance that you’ve been given, or asking your supervisor directly.

Q: Should the discussion include the limitations of the study?

A: Like the answer above, you should engage with the limitations of your study, but it might appear in the discussion of some dissertations, or the conclusion of others. Consider the narrative flow and whether it makes sense to include the limitations in your discussion chapter, or your conclusion. You should also consult any discipline-specific guidance you’ve been given, or ask your supervisor for more. Be mindful that this is slightly different to the limitations outlined in the methodology or methods chapter (see our Writing the Methodology guide vs. the 'Discussion' tab of this guide).

Q: Should the results and discussion be in the first-person or third?

A: It’s important to be consistent , so you should use whatever you’ve been using throughout your dissertation. Third-person is more commonly accepted, but certain disciplines are happy with the use of first-person. Just remember that the first-person pronoun can be a distracting, but powerful device, so use it sparingly. Consult your lecturer for discipline-specific guidance.

Q: Is there a difference between the discussion and the conclusion of a dissertation?

A: Yes, there is a difference. The discussion chapter is a detailed consideration of how your findings answer your research questions. This includes the use of secondary literature to help contextualise your discussion. Rather than considering the findings in detail, the conclusion briefly summarises and synthesises the main findings of your study before bringing the dissertation to a close. Both are similar, particularly in the way they ‘broaden out’ to consider the wider implications of the research. They are, however, their own distinct chapters, unless otherwise stated by your supervisor.

The results and discussion chapters (or chapter) constitute a large part of your dissertation as it’s here where your original contribution is foregrounded and discussed in detail. Remember, the results chapter simply reports on the data collected, whilst the discussion is where you consider your research questions and/or hypothesis in more detail by interpreting and interrogating the data. You can integrate both into a single chapter and weave the interpretation of your findings throughout the chapter, although it’s common for both the results and discussion to appear as separate chapters. Consult your supervisor for further guidance.

Here’s a final checklist for writing your results and discussion. Remember that not all of these points will be relevant for you, so make sure you cover whatever’s appropriate for your dissertation. The asterisk (*) indicates any content that might not be relevant for your dissertation. To download a copy of the checklist to save and edit, please use the Word document, below.

• Results and discussion self-evaluation checklist

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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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• 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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Dissertation findings and discussion sections

(Last updated: 2 March 2020)

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Granted that at some point in the discussion you are going to have to link back to this previous research. But you still have the opportunity to demonstrate how you have met that coveted gap in the research and generally made a useful contribution to knowledge.

There are many ways to write up both your findings and discussion. In shorter dissertations, it might make sense to have both of these comprise one section. In longer pieces of work, these chapters are usually separate.

Information contained in this section will highlight the finer details of writing up your findings and discussion sections. We will use the model of Description – Analysis – Synthesis , which are typically the three components readers expect to see in these two sections.

Preparing to write

We also assume that you have used some sort of software program to help you with the organisation of your findings. If you have not completed this process, you must do so before beginning to write. If not, your findings chapter may end up a confusing and unorganised mess of random information. If you need help in this area, make sure to seek it out before beginning to put your findings down on paper.

One of the main issues that students tend to encounter when writing up their findings is the amount of data to include. By the end of the research process, you've probably collected very large amounts of data . Not all of this can possibly appear in your dissertation without completely overwhelming the reader. As a result, you need to be able to make smart decisions about what to include and what to leave out.

One of the easiest ways to approach this task is to create an outline. In approaching the outline, it is in your best interest to focus on two key points. Firstly, you need to focus on answering your research questions. Secondly, you must include any particularly interesting findings that have cropped up as you completed your research.

An outline will give you the structure you need, and should make the whole process of presenting your findings easier. We realise that it is going to be a difficult process to pick and choose pieces of data to include. But you must be diligent in the work that you cut out. A findings chapter that is long and confusing is going to put the reader off reading the rest of your work.

Introducing your findings

It can be up to 40% of the total word count within your dissertation writing . This is a huge chunk of information, so it's essential that it is clearly organised and that the reader knows what is supposed to be happening. One of the ways you can achieve this is through a logical and organised introduction.

There are four main components that your introduction should include:

Reminding the reader of what you set out to do

A brief description of how you intend approaching the write up of the results

Placing the research in context

Letting the reader know where they can find the research instruments (i.e. the Appendix)

With a findings chapter, there should be no suspense for the reader. You need to tell them what they need to know right from the beginning. This way, they'll have a clear idea about what is still to come. A good introduction will start by telling the reader where you have come from in the research process and what the outcome was (in a couple of paragraphs or less).

You need to highlight the structure of the chapter (as you generally will do with all chapters) and where the reader might find any further information (e.g. in the appendices).

Organisation of data

This is really going to depend on the type of project you have created .

For example, if you have completed a qualitative research project, you might have identified some key themes within the software program you used to organise your data. In this case, highlighting these themes in your findings chapter may be the most appropriate way to proceed. Not only are you using information that you have already documented, you are telling a story in each of your sections (which can be useful in qualitative research).

But what if you undertook a more quantitative type study? You might be better off structuring your findings chapter in relation to your research questions or your hypotheses. This assumes, of course, that you have more than one research question or hypothesis. Otherwise you would end up just having one really long section.

This brings us to our next student mistake – trying to do too much within one section.

Subheadings are ultimately going to be your friend throughout your dissertation writing . Not only do they organise your information into logical pieces, they give the reader guidelines for where your research might be going. This is also a break for the reader. Looking at pages and pages of text without any breaks can be daunting and overwhelming for a reader. You don't want to overwhelm someone who is going to mark your work and who is responsible for your success (or failure).

When writing your introduction, be clear, organised and methodical. Tell the reader what they need to know and try to organise the information in a way that makes the most sense to you and your project. If in doubt, discuss this with your supervisor before you start writing.

Presentation of qualitative data

If you have conducted things like interviews or observations, you are likely to have transcripts that encompass pages and pages of work.

Putting this all together cohesively within one chapter can be particularly challenging. This is true for two reasons. First, it is always difficult to determine what you are going to cut and/or include. Secondly, unlike quantitative data, it can often be difficult to represent qualitative data through figures and tables, so condensing the information into a visual representation is simply not possible. As a writer, it is important to address both these challenges.

When considering how to present your qualitative data, it may be helpful to begin with the initial outline you have created (and the one described above). Within each of your subsections, you are going to have themes or headings that represent impactful talking points that you want to focus on.

Once you have these headings, it might be helpful to go back to your data and highlight specific lines that can/might be used as examples in your writing. If you have used multiple different instruments to collect data (e.g. interviews and observations), you are going to want to ensure that you are using both examples within each section (if possible). This is so that you can demonstrate to more well-rounded perspective of the points you are trying to make. Once you have identified some key examples for each section, you might still have to do some further cutting/editing.

Once you have your examples firmly selected for each subsection, you want to ensure that you are including enough information. This way, the reader will understand the context and circumstances around what you are trying to ‘prove’. You must set up the examples you have chosen in a clear and coherent way.

Students often make the mistake of including quotations without any other information. It is important that you embed your quotes/examples within your own thoughts. Usually this means writing about the example both before and after. So you might say something like, “One of the main topics that my participants highlighted was the need for more teachers in elementary schools. This was a focal point for 7 of my 12 participants, and examples of their responses included: [insert example] by participant 3 and [insert example] by participant 9. The reoccurring focus by participants on the need for more teachers demonstrates [insert critical thought here]. By embedding your examples in the context, you are essentially highlighting to the reader what you want them to remember.

Aside from determining what to include, the presentation of such data is also essential. Participants, when speaking in an interview might not do so in a linear way. Instead they might jump from one thought to another and might go off topic here and there.

It is your job to present the reader with information on your theme/heading without including all the extra information. So the quotes need to be paired down to incorporate enough information for the reader to be able to understand, while removing the excess.

Finding this balance can be challenging. You have likely worked with the data for a long time and so it might make sense to you. Try to see your writing through the eyes of someone else, which should help you write more clearly.

Presentation of quantitative data

Something to consider first with numeric data is that presentation style depends what department you are submitting to. In the hard sciences, there is likely an expectation of heavy numeric input and corresponding statistics to accompany the findings. In the arts and humanities, however, such a detailed analysis might not be as common. Therefore as you write out your quantitative findings, take your audience into consideration.

Just like with the qualitative data, you must ensure that your data is appropriately organised. Again, you've likely used a software program to run your statistical analysis, and you have an outline and subheadings where you can focus your findings. There are many software programs available and it is important that you have used one that is most relevant to your field of study.

For some, Microsoft Excel may be sufficient for basic analysis. Others may rely on SPSS, Stata, R, or any of the other programs available through your institution or online. Whatever program you have used, make sure that you document what you have done and the variables that have affected your analysis.

One common mistake found in student writing is the presentation of the statistical analysis. During your analysis of the data, you are likely to have run multiple different analyses from regressions to correlations. Often, we see students presenting multiple different statistical analyses without any real understanding of what the tests mean.

Presentation of quantitative data is more than just about numbers and tables. You must explain your findings and justify why you have run/presented the tests that you have. You could also explain how they relate to the research question. However, depending on how you have organised your work, this might end up in the discussion section.

Students who are not confident with statistical analysis often have a tendency to revert back to their secondary school mathematics skills. They commonly document the mean, median, and mode for all of their results. Now, these three outcomes can be important. But having a good understanding of why you are proceeding with this strategy of analysis is going to be essential in a primarily quantitative study.

That noted, there are different expectations for an undergraduate dissertation and a PhD thesis, so knowing what these expectations are can be really helpful before you begin.

Presentation of graphs, tables, and figures

The first is the use of colour and/or variables. Depending on the presentation of your dissertation, you may be required to print out a final copy for the marker(s). In many cases, this final copy must be printed in black and white. This means that any figures or graphs that you create must be readable in a black and white (or greyscale) format.

This can be challenging because there are only so many distinct shades of grey. In a pie chart, you might show one section as purple and the other as green. Yet when printed, both the purple and the green translate to approximately the same shade of grey, making your graph suddenly unreadable.

Another common error is overwhelming the reader with graphs and tables. Let's think about your outline and subheadings. If you're including a table under each subheadings, it needs to be relevant to the information that is being discussed in that chapter. There is no correct or incorrect number of graphs that should exist within the section, but you should use your judgement about what looks appropriate.

The final mistake we see is the duplication of writing (or absence of writing) when presenting a graph. Some students will present their findings in a graph or table and then write out this information again below the graph. This defeats the entire purpose of using the graph in the first place. So avoid this at all times.

Conversely, other students sometimes include a graph or figure but nothing else. Doing this denies the reader of context or purpose of said graph or figure. At some point, a balance needs to be struck where the reader has the information they require to really understand the point being made within the section.

Analysis and synthesis in a discussion

The purpose of a discussion chapter.

The structure of your discussion chapter is really going to depend on what you are trying to do and how you have structured your findings. If you chose to structure your findings by theme, it might make sense to continue this into the analysis chapter.

Other people might structure it according to the research questions. This clearly indicates to the reader how you have addressed your study. Marking a dissertation usually requires the marker to comment on the extent to which the research questions have been addressed. So by structuring a dissertation that lays out each research question for the marker, you are making their job easier. Needless to say, this a great thing.

Like any other chapter in your thesis, an introduction is an essential component of your discussion. By this point, the reader has gone through your findings and is now looking for your interpretation. Therefore, at the end of your discussion introduction you should highlight the content that each of the subsections will cover.

A conclusion to your discussion section (or a chapter summary) is also going to be beneficial. The length of the analysis chapter is usually quite long, so a wrap up of the key points at the end can help the reader digest your work. It can also help ensure that the reader actually understands the points you are trying to highlight within your project.

Critical thinking

Without any critical thinking, you are really doing yourself a disservice. It will affect the mark that you obtain on your overall dissertation. This is why the analysis chapter is usually weighted quite heavily on the marking rubric.

We tell students about critical thinking and the importance of it on a daily basis. And yet, there does seem to be a general confusion about what critical thinking entails, i.e. what constitutes critical thinking versus what is a simple description.

Critical thinking asks you to provide your own opinion on your topic, which can be daunting at first. For much of your academic career, you've likely been asked to use research to justify a position that has already been set. Unlike critical thinking, this requires you to use other people’s ideas. But even if you're new to it, try and get to grips with what critical thinking entails and use it in your work.

Creating sub-sections

Subheadings need to be informative but not too long. It is possible to layer your subheadings, so you might have a Chapter 2, a Section 2.1 and then a 2.1.1 and 2.2.2. Usually anything after 3 numerical points does not get a number and would not appear in your table of contents.

When creating titles for your subheadings, consider how they are going to look in the table of contents. They need to fit on one line, ideally, so putting your research question as the subheading might end up being too long. Conversely, one- or two-word subheadings usually doesn't give enough information about the purpose of the section.

Finding this balance is important. But remember you can always edit your subheadings retrospectively.

Linking to previous chapters

Ideally, you will be able to concisely and effectively link your research to what has been researched previously. But this can be a challenge. You don't want to repeat what has been said in your literature review or the findings . But you need to pull examples from both of these sections in order to make the points that you need to.

So, how do you tackle this?

One way is by referring the reader back to previous chapters, sections, or subsections. This process can generally be done at the end. You can put in a place holder until you know how your sections will be numbered. For example you might write: “In Section XYZ, the theme of … was discussed. Findings from this study indicate…. (see Section XYZ for details)”. While ‘XYZ’ is obviously not going to be the same section, by using the same abbreviation, you can then search ‘XYZ’ after you have completed writing and replace each term with the appropriate number. This also makes the proofreading process easier.

If you are submitting an electronic version of this document, you may also consider hyperlinks to take the reader to the different sections. But be aware that this can be considerably more work, so you should allow for this in your timescale if it's something you wish to implement.

Let's outline the main takeaway points:

It is essential that you keep in mind the ‘describe, analyse, synthesise’ model.

The findings chapter is essentially the describe part. You need to ensure that you have clearly identified data that relates to your research questions, hypotheses, or themes of your study.

For the ‘describe’ component, you are not looking to support your work with other research, but rather to present your contribution. It is also important to consider your data in the ‘describe’ section. If you have qualitative data, ensure that you have edited the quotes and examples to a reasonable length. Pick quotes that accurately represent your theme. Try not to focus solely on one or two participants (if possible). Ensure that you are demonstrating links between multiple instruments, if you used them.

If you are using quantitative data, be careful about how many statistical tests you run. Make sure you can justify why you chose one particular test over another. When presenting graphs, use a colour scheme that's appropriate for the reader when printing in black and white. Ensure that graphs and tables are appropriately explained, but that the information provided is not duplicated.

From the ‘describe’ element, you move into the 'analysis' and 'synthesis'. These parts usually appear in the discussion and ask you to employ your critical thinking skills to demonstrate how your research fits into the bigger picture. It is often the case that your analysis holds the most weight in the marking scheme. So you should spend considerable time ensuring this section is appropriate. It needs to demonstrate how you have attempted to answer your research questions.

Finally, create an outline before you begin. While this might seem tedious at first, filling in the sections with the appropriate information will mean that you are not writing things over and over again. It'll also make sure you do not go wildly off topic. It is always beneficial to have a second set of eyes assess your work for any errors or omissions. Many students choose to contact professional editors to help with this as they hold the relevant expertise to guide you on the correct path to creating a perfect discussion section that is ready for submission.

In terms of presentation, both the findings and discussion chapters will benefit from a clear and logical introduction and chapter summary. Remember that both of these chapters are meant to inform. You are leading the reader on a journey, so make sure they stay on the path and arrive at the final destination with you!

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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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How to write the analysis and discussion chapters in qualitative (SSAH) research

By charlesworth author services.

• Charlesworth Author Services
• 11 November, 2021

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

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

Look for emerging themes and patterns

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

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

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

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

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

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

Let your data guide you

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

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

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

Work closely with the literature

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

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

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

In conclusion

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

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

Sep 11, 2023

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

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

What is the purpose of a PhD Discussion Chapter?

• Suggested outlines for a discussion chapter:

Advice for improving your discussion chapter

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Each week we send two short, thought-provoking emails that will make you think differently about what it means to be a PhD student. It is designed to be read in thirty seconds and thought about all day.

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

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

 Let’s dig down into each of these things.

Summarising and explaining the research

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

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

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

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

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

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

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

To mitigate this, consider employing the following strategies:

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

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

Interpreting and Contextualising Results 

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

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

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

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

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

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

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

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

Evaluating Existing Theories and Models  

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

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

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

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

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

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

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

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

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

Discuss Unexpected Results

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

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

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

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

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

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

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

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

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

Answering the “so what?” Question

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

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

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

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

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

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

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

Limitations and Future Research

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

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

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

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

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

Your PhD Thesis. On one page.

Example outline for a discussion chapter:.

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

Introduction

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

Summary of Key Findings

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

Interpretation of Findings

• Contextualisation of Results
• Significance and Implications of the Findings

Evaluation of Existing Theories and Models

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

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

The Power of a Well-Structured Narrative

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

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

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

Data Visualisation Tools

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

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

Integrate Feedback Actively

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

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

Highlight the Broader Implications

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

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

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

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

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

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

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

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

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

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

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Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

Ask yourself these questions:

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

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

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

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

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

Here are a few common possibilities:

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

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

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

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

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

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

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Palliative procedures for advanced obstructive colorectal cancer: a systematic review and meta-analysis

• Open access
• Published: 23 September 2024
• Volume 39 , article number  148 , ( 2024 )

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• Bingqing Ma 1 ,
• Tianxing Ren 1 ,
• Chengjun Cai 1 ,
• Biao Chen 1 &
• Jinxiang Zhang 1  

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Advanced obstructive colorectal cancer (AOCC) presents surgical challenges. Consideration must be given to alleviating symptoms and also quality of life and survival time. This study compared prognostic efficacies of palliative self-expanding metal stents (SEMSs) and surgery to provide insights into AOCC treatment.

PubMed, Web of Science, MEDLINE, and Cochrane Library were searched for studies that met inclusion criteria. Using a meta-analysis approach, postoperative complications, survival rates, and other prognostic indicators were compared between patients treated with SEMSs and those treated surgically. Network meta-analysis was performed to compare prognoses between SEMS, primary tumor resection (PTR), and stoma/bypass (S/B).

Twenty-one studies were selected (1754 patients). The odds ratio (OR) of SEMS for clinical success compared with surgery was 0.32 (95% confidence interval [CI] 0.15, 0.65). The ORs for early and late complications were 0.34 (95% CI 0.19, 0.59) and 2.30 (95% CI 1.22, 4.36), respectively. The ORs for 30-day mortality and stoma formation were 0.65 (95% CI 0.42, 1.01) and 0.11 (95% CI 0.05, 0.22), respectively. Standardized mean difference in hospital stay was − 2.08 (95% CI − 3.56, 0.59). The hazard ratio for overall survival was 1.24 (95% CI 1.08, 1.42). Network meta-analysis revealed that SEMS had the lowest incidence of early complications and rate of stoma formation and the shortest hospital stay. PTR ranked first in clinical success rate and had the lowest late-complication rate. The S/B group exhibited the lowest 30-day mortality rate.

Among palliative treatments for AOCC, SEMSs had lower early complication, stoma formation, and 30-day mortality rates and shorter hospital stays. Surgery had higher clinical success and overall survival rates and lower incidence of late complications. Patient condition/preferences should be considered when selecting AOCC treatment.

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Avoid common mistakes on your manuscript.

Colorectal cancer (CRC) is the third most common cancer in the USA and ranks second in cancer-related mortality. In 2019, approximately 60% of newly diagnosed cases were in advanced stages, and this proportion has been gradually increasing [ 1 ]. With the recent enhanced standardization of CRC screening, an increasing number of younger patients have been diagnosed. Moreover, advanced-stage cases are more prevalent in this demographic group. Obstruction is the most common complication of CRC; approximately 30% of patients exhibit symptoms of obstruction, which often correlates with a poor prognosis [ 2 , 3 ]. The condition of patients with advanced obstructive colorectal cancer (AOCC) is particularly complex. These patients typically require urgent decompression to prevent severe abdominal distension, electrolyte imbalance, septic shock, or even death [ 4 ].

The placement of self-expanding metal stents (SEMSs) has increasingly become the standard treatment for relieving the symptoms of CRC obstruction. Initially designed to alleviate obstruction symptoms in patients with advanced stage disease, SEMS placement can serve as a bridge to surgery. For patients with AOCC, SEMS placement undoubtedly offers benefits such as minimal invasiveness, rapid relief, and high patient tolerance. However, clinicians must consider potential complications such as reobstruction, perforation, stent migration, and cancer cell dissemination. In addition, considering the long-term complications caused by SEMS, the SEMS is not convincing for the long-term prognosis of patients. In patients with AOCC eligible for chemotherapy and a long life expectancy, palliative treatments other than SEMS should be considered [ 5 ].

Traditionally, surgery has been a palliative treatment for AOCC. Procedures may include primary tumor resection and anastomosis, simultaneous stoma creation, simple stoma surgery, Hartmann’s procedure, or bypass surgery. Surgical decisions, including the choice of procedure, are typically made by the surgeon based on the intraoperative findings and the patient’s overall condition. Some studies have suggested that resection of the primary tumor can lead to better quality of life and improved overall survival rates in patients with advanced-stage disease [ 6 , 7 ]. However, these benefits warrant further study and detailed discussion, particularly for patients exhibiting obstruction symptoms. Additionally, guidelines and studies investigating direct prognostic comparisons between various surgical approaches for patients with AOCC are lacking.

Several previous meta-analyses [ 8 , 9 , 10 , 11 ] have compared the palliative effects of SEMS and surgery for malignant colorectal obstruction (MCO). However, some of these studies [ 8 , 9 , 10 ] included patients with obstructions caused by other malignancies such as gynecological and urological cancers. Moreover, some studies [ 8 , 11 ] used mean survival time to compare patient survival duration. We believe that hazard ratios (HR) are more convincing in comparing treatment approaches. Additionally, to our knowledge, no meta-analysis has directly compared the palliative efficacies of different surgical and other treatment methods for AOCC. To address these gaps in research, we conducted a meta-analysis involving a comprehensive search of the most recent comparative literature.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used to direct this meta-analysis (Online Resource Table  1 ). The protocol of this meta-analysis had been registered on INPLASY registration. The registration number is INPLASY202470114.

Inclusion criteria

The following are the inclusion criteria:

Patients with advanced, incurable colorectal cancer.

Patients with obstructive colorectal cancer.

Patients who underwent SEMS or surgical procedures, including tumor resection with or without stoma creation, Hartmann’s procedure, simple stoma creation, and bypass, among others, and in whom at least one outcome of interest was comparatively analyzed.

The study objective(s) addressed alleviating patient symptoms and facilitating prompt systemic treatment.

Exclusion criteria

The following are the exclusion criteria:

SEMS or surgical procedures intended for bridging to surgery (BTS).

Patients without obstructive symptoms.

Obstruction caused by malignancies other than AOCC.

Studies that did not compare outcomes or single-arm trials.

Search strategy

The following databases were searched: PubMed, Web of Science, MEDLINE, and Cochrane Library. There were no restrictions regarding the publication date or language, and all studies that met the inclusion criteria were included. Our search strategy was formulated based on the following keywords: “colorectal cancer,” “advanced,” “incurable,” “obstruction,” “palliative,” “stent,” “surgery,” “stoma,” and several related phrases (the search strategy is detailed in Online Resource Table  2 ). Two authors (BQ-M and CJ-C) independently conducted the literature search, eliminating duplicate publications and culminating in a database of studies for review.

Data extraction

Initially, two independent reviewers (BQ-M and CJ-C) individually read the titles and abstracts of the studies in the aforementioned databases and excluded literature that clearly did not meet the inclusion criteria. Subsequently, the two reviewers read the full texts to determine whether the remaining studies met the inclusion criteria. Two separate lists of eligible studies were produced using this process. Studies common to both lists were included; any discrepancies were resolved by a third reviewer (TX-R), and the final inclusion was decided through discussion. The following information was extracted from the studies: first author, year of publication, number of patients, tumor location, type and occurrence of complications, and study type. Specifically, for survival data, we compared the overall survival (OS) using the HR value and its 95% confidence interval (CI). We directly extracted studies in which HR data was included. If not available directly, Engauge Digitizer software was used to analyze survival curves; a method detailed by Tierney et al. [ 12 ] was applied for computation. For continuous outcomes (length of hospital stay), the means and standard deviations were extracted. If outcomes could not be directly obtained, the method described by Hozo et al. [ 13 ] was used for computation. If the original study used nonparametric testing or if the data did not follow a normal distribution, the results were excluded.

Outcomes of interest

The following are the outcomes of interest:

Clinical success

Early complications: complications within 30 days after intervention

Late complications: complications 30 days after intervention

30-day mortality

Stoma formation rate

Hospital stay

Overall survival rate

Quality assessment

The Newcastle–Ottawa scale (NOS) was used to assess the quality of nonrandomized controlled trials (non-RCTs), while the Cochrane tools (risk of bias tool, Rob tool) were used for evaluating RCTs.

Statistical analysis: SEMS vs. surgery

All analyses were conducted using the meta package [ 14 ] in R software, version 4.3.1. The chi-square test and Student’s t -test were used to compare differences between the two groups, with a p value < 0.05 indicating statistical significance. For the binary variable results, the odds ratio (OR) or risk ratio (RR) and their respective 95% CIs were used for meta-analysis and comparison. Continuous variables were compared using standardized mean difference (SMD) and 95% CI. If the 95% CI of the OR and RR did not cross 1 and the 95% CI of SMD did not cross 0, the results were deemed statistically significant. The combined outcomes are shown in forest figures.

Heterogeneity between studies was assessed using the I 2 statistic and Q -test. I 2  > 50% was considered indicative of heterogeneity. To mitigate the risk of bias, results with notable heterogeneity were combined using a random-effects model. Results without discernible heterogeneity were aggregated using a fixed-effects model. For results that exhibited heterogeneity, subgroup analyses were performed based on the publication year, tumor location, and type of study. Sensitivity analyses were also performed to evaluate the heterogeneity.

Funnel plots and Egger’s test were used to evaluate publication bias. A p value < 0.05 was considered indicative of potential publication bias.

Statistical analysis: SEMS vs. PTR vs. S/B

Surgical interventions were further stratified into two categories: primary tumor resection (PTR) and stoma creation/bypass (S/B). The objective of this analysis was to discern any differences in the prognosis of AOCC between these two procedures and compare them with that of SEMS placement.

We used Bayesian Network Analysis to analyze the outcomes of these three interventions. All analyses were performed using R software (version 4.3.1), specifically the BUGSnet package [ 15 ]. For analysis of the binary and continuous data, outcomes were compared through ln (OR) and mean difference (MD) with their respective 95% CIs. If the 95% CI did not exceed 0, the difference was considered statistically significant. Each outcome set was aggregated using a random-effects model.

For each outcome type, we constructed network plots and developed ranking diagrams to show the results. Leverage plots were used to assess the fit of the models.

We identified 1605 studies using the described search strategy. Initially, 774 duplicate studies were excluded. After screening titles and examining abstracts, we excluded 784 irrelevant studies. Of the remaining 47 articles that underwent a full-text assessment, 26 were excluded because they did not meet the inclusion criteria. Among these, 9 included patients without obstructive symptoms, 8 were reviews or meta-analyses, 3 were single-arm studies, 3 pertained to BTS research, and 3 involved patients with cancers other than colorectal. Finally, 21 [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ] studies were included in this meta-analysis (Fig.  1 ). This included 2 RCT [ 21 , 26 ] and 19 non-RCT studies [ 16 , 17 , 18 , 19 , 20 , 22 , 23 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. Among them, 20 studies [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 , 36 ] compared SEMS placement to surgery (with 3 [ 27 , 34 , 36 ] focusing on SEMS vs. PTR and 5 [ 17 , 19 , 26 , 30 , 35 ] focusing on SEMS vs. S/B), and 1 [ 33 ] study compared PTR with S/B.

PRISMA flow diagram

Characteristics of selected studies

The details of the 21 studies are shown in Table  1 . Twenty-one studies from the years 2003–2022 were considered for analysis, encompassing 1754 patients. Of these, 865 patients underwent SEMS placement, and 889 underwent surgical intervention. The average age of patients in the SEMS group was 67.74 years, and those in the surgical group had an average age of 63.43 years.

Seven studies [ 16 , 18 , 19 , 21 , 22 , 26 , 36 ] specifically investigated left-sided colorectal cancer, whereas 10 studies [ 20 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 33 , 35 ] included cecal tumors. In the SEMS group, the most frequently reported outcome was stent-related complications. Seventeen studies [ 16 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 32 , 34 , 35 ] provided individual reports on these complications, including reobstruction, perforation, migration, narrowing, and fracture. In the surgical group, the most common outcome was complications related to infection, with wound infections being predominant, as highlighted in 15 studies [ 16 , 17 , 22 , 23 , 24 , 25 , 27 , 28 , 29 , 30 , 31 , 33 , 34 , 35 , 36 ]. In the non-RCTs, the NOS scores ranged from 5 to 8, with 13 studies scoring 6 or above. Hooft et al.’s RCT was rated as low risk, and Fiori et al.’s prospective randomized trial was rated as moderate risk (some concerns). The detailed information of the NOS/Rob assessments can be found in Online Resource Table  3 and Online Resource Fig.  1 .

Twelve studies [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 30 , 31 , 34 , 35 , 36 ] reported on the clinical relief success rates. A total of 1105 patients out of 1142 (96.8%) patients successfully achieved symptom relief. The SEMS group had a lower clinical success rate than the surgical group (94.8% vs. 98.8%, p value < 0.001). The meta-analysis revealed an OR of 0.32 (95% CI 0.15, 0.65), indicating a statistically significant difference (Fig.  2 A).

Forest plots of each outcome. A Clinical success; B early complications; C late complications; D 30-day mortality; E stoma formation; F hospital stay; G overall survival

Early complications (within 30 days)

Eleven studies [ 21 , 22 , 23 , 24 , 25 , 26 , 28 , 31 , 34 , 35 , 36 ] reported the incidence of early complications. Of the 1124 patients, 219 (19.5%) experienced early complications. The SEMS group had a lower incidence of early complications than the surgical group (11.3% vs. 28.1%, p value < 0.001). The meta-analysis revealed an OR of 0.34 (95% CI 0.19, 0.59), showing a statistically significant difference (Fig.  2 B).

Late complications (after 30 days)

Ten studies [ 21 , 22 , 23 , 24 , 25 , 26 , 28 , 31 , 34 , 35 ] reported the incidence of late complications. This included 202 of 1060 (19.1%). The SEMS group exhibited a higher incidence of late complications than the surgical group (24.0% vs. 13.9%, p value < 0.001). Meta-analysis showed an OR of 2.30 (95% CI 1.22, 4.36), demonstrating a statistically significant difference (Fig.  2 C).

Thirty-day mortality

Seventeen studies [ 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 28 , 31 , 32 , 34 , 35 , 36 ] reported the 30-day mortality rate. Of the 1503 patients included, 86 (5.7%) died. The SEMS group had a lower mortality rate than the surgical group (4.5% vs. 7.0%, p value = 0.057). The meta-analysis revealed an OR of 0.65 (95% CI 0.42, 1.01), and the difference was not statistically significant (Fig.  2 D).

Stoma formation

Fifteen studies [ 16 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 31 , 32 , 34 ] reported on the stoma formation rate. Out of the 1227 patients included, 392 had stoma formation, translating to 31.9% of the study population. The stoma formation rate was lower in the SEMS group than in the surgical group (11.7% vs. 53.0%, p value < 0.001). According to the meta-analysis, the OR was 0.11 (95% CI 0.05, 0.22), and the difference was statistically significant (Fig.  2 E).

Nine studies [ 19 , 20 , 25 , 26 , 27 , 28 , 30 , 31 , 36 ] compared the duration of hospital stay. A total of 796 hospital stays were analyzed. The mean length of hospital stay was significantly shorter in the SEMS group than in the surgical group. The SMD in the meta-analysis was − 2.08 (95% CI − 3.56, 0.59), indicating a statistically significant difference (Fig.  2 F).

Overall survival

Thirteen studies [ 17 , 18 , 20 , 22 , 23 , 24 , 25 , 26 , 29 , 31 , 32 , 34 , 36 ] included a comparison of patient survival rates. Of these, all 13 met the previously described data extraction criteria. Ultimately, the meta-analysis found a lower OS rate in the SEMS group than in the surgical group, with an HR of 1.24 (95% CI 1.08, 1.42). This difference was statistically significant (Fig.  2 G).

Heterogeneity and publication bias

Heterogeneity and publication bias for various outcomes are summarized in Table  2 . The funnel plots are shown in Online Resource Fig.  2 .

From the mentioned outcomes, “early complications,” “late complications,” “stoma formation,” and “hospital stay” showed evidence of heterogeneity across the different research studies. The P egger (s) for “stoma formation” and “hospital stay” were < 0.001 and 0.010, respectively, indicating that the heterogeneity may be due to publication bias. Conversely, the P egger (s) for “early” and “late” complications were 0.629 and 0.133, respectively, indicating no significant publication bias. Thus, heterogeneity may arise from other factors not related to publication bias.

Regardless of the observed heterogeneity, we performed a subgroup analysis of the results that showed heterogeneity to provide further insights.

Subgroup analysis

For the four results that exhibited heterogeneity, we conducted a subgroup analysis based on publication year, tumor location, and study type. The results are summarized in Online Resource Table  4 . Subgroups with no significant heterogeneity and statistically significant conclusions are highlighted in bold font.

Early complications: Nine studies, published after 2010, comprised this subgroup. The SEMS group showed a lower probability of early complications than the surgical group ( I 2  = 41%, OR 0.35, 95% CI 0.19, 0.63).

Of the 7 retrospective studies, a similar conclusion was drawn ( I 2  = 40%, OR 0.33, 95% CI 0.17, 0.65).

Late complications: Among the 6 retrospective studies, the SEMS group was more likely to experience late complications than the surgical group ( I 2  = 0%, OR 2.73, 95% CI 1.87, 3.99).

Stoma formation: Of the 6 studies focusing on left-sided CRC, the SEMS group showed a lower rate of stoma formation than the surgical group ( I 2  = 35%, OR 0.11, 95% CI 0.03, 0.34).

A similar conclusion was drawn from the 6 studies published before 2010 ( I 2  = 4%, OR 0.11, 95% CI 0.04, 0.28).

Hospital stay: We could not identify the factors explaining the heterogeneity between studies for this outcome.

Sensitivity analysis

For the above four results with heterogeneity, we conducted sensitivity analyses. Sensitivity analyses were conducted by removing each individual study to evaluate whether any single study had a significant impact on combined estimates. There was no significance detected for the outcomes of early complications, stoma formation, and hospital stay. Notably, we found that the study of Pattarajierapan et al. [ 35 ] had a large impact on combined estimate of late complications ( I 2  = 0 without this study). The results are detailed in Online Resource Fig.  3 .

Network meta -analysis

Using a network meta-analysis, multiple treatment interventions were compared by assessing the direct and indirect evidence from multiple studies. These results provide more comprehensive insights than those of pairwise meta-analyses.

Ten studies that compared three treatment strategies (SEMS, PTR, and S/B) were included. The breakdown of study inclusion was as follows: SEMS vs. PTR in 3 studies [ 27 , 34 , 36 ] and SEMS vs. S/B in 5 studies [ 17 , 19 , 26 , 30 , 35 ]. Two studies [ 20 , 33 ] included comparisons between PTR and S/B. The treatment-ranking probabilities for the various outcomes are shown in Fig.  3 .

Results of network meta-analysis. The (1) ranking diagrams, (2) leverage plots, and (3) network plots between SEMS, PTR, and S/B. A Clinical success; B early complications; C late complications; D 30-day mortality; E stoma formation; F hospital stay

Interpretation of network meta -analysis results

Clinical success: A higher rank indicated a better rate of clinical symptom relief. PTR appeared to be superior in clinical success in alleviating symptoms.

Complications: A higher rank implied fewer complications and a shorter duration of hospital stay. The PTR group showed the lowest rate of late complications. The S/B group had the lowest early mortality rate.

Advantages of SEMS: SEMS placement resulted in fewer early complications, shorter hospital stays, and a lower rate of stoma formation.

Further detailed pairwise comparisons of the three treatment strategies are shown in Table  3 .

The clinical success rate of SEMS is 82–94.4% [ 37 , 38 ]; our study showed a 94.8% success rate. The key reasons for failure were constipation and postoperative stent-related complications [ 39 ]. Kwon et al.’s research [ 38 ] identified peritoneal metastatic cancer as a risk factor for failure of clinical relief (OR, 0.33; 95% CI 0.17, 0.65). Both peritoneal cancer and a stent expansion > 90% on day one predicted postoperative reintervention due to the risk of stent migration. Another study confirmed that extrinsic colonic compression and stent length are the key factors influencing the success rate of stent relief [ 40 ]. This finding suggests that the efficacy of the application of stents to relieve extrinsic compression requires further investigation. Moreover, the stent placement technique matters; direct endoscopic vision resulted in a higher success (81% vs. 77%) and fewer complications (20% vs. 38%) than radiographic guidance [ 41 ].

Complications are a significant factor affecting the application of SEMS for CRC obstruction. Particularly when SEMS placement is used as a palliative treatment, complications can lead to a poor prognosis and are predictive factors for lower postoperative survival rates [ 41 , 42 ]. A study of 434 patients found no notable differences in the clinical outcomes between SEMS placement for palliative care or as a BTS [ 43 ]. Our study indicates that there are fewer early complications caused by SEMS placement than those caused by surgery (11.3% vs. 28.1%). However, in the long term, the rate of complications was higher than that of surgery (24.0% vs. 13.9%). This conclusion, which is consistent with that of previous studies, underscores the need for a comprehensive understanding of SEMS applications based on both immediate and extended postoperative timelines.

Reobstruction is the most common complication of palliative treatment with SEMS and is more often a late-stage complication. The median patency period for palliation is between 55 and 343 days [ 42 , 44 , 45 ]. Early obstructions are associated with stent placement failure and stent occlusion, whereas late obstructions are mainly caused by tumor growth into the stent, which can reobstruct the area. Suh et al. [ 46 ] used a Cox regression analysis to explore the causes of stent occlusion. The results showed that a stent expansion of less than 70% within 48 h is a significant factor for stent occlusion (OR 12.55, 95% CI 2.52, 62.48). Tumor ingrowth occurs over time. For patients with AOCC, using covered stents may minimize tumor ingrowth; however, this approach presents an increased migration risk [ 47 , 48 , 49 ]. Endoscopic electrocoagulation therapy or secondary stent placement can be performed for reobstruction. For patients in palliative care, secondary stent placement, including the replacement/placement of a new stent inside the existing stent, is an effective treatment method. The success rate is 75–86%, and patency is generally maintained until the end of life [ 44 ].

Perforation is the most severe complication of SEMS placement and can lead to severe peritonitis. This complication often requires emergency surgical intervention and may require procedures such as ostomy or Hartmann’s [ 45 ]. Perforation is more frequent in patients with AOCC, particularly in those with poor bowel preparation and significant narrowing attributed to guidewire or catheter damage. Thus, thorough bowel cleaning, preventive antibiotic use, combined endoscopy, and radiology are crucial for precise tumor location assessment [ 48 ]. Analgesics and sedatives are beneficial during the procedure and an expert endoscopist should guide the process [ 50 ]. Late-stage perforations are mainly caused by stent-associated tumor erosion of the intestinal wall and ischemic necrosis of the intestinal tissue. While traditional stents made from nickel-titanium and stainless steel offer strong support, they may also cause foreign body reactions, leading to perforation. Newer biodegradable and polymeric stents aim to combat this problem. However, their mechanical strength and support require further optimization.

Comprehensive chemotherapy is the primary treatment method for patients with AOCC. Compared with surgery, SEMS can significantly shorten the time before initiating chemotherapy because of its minimal invasiveness and quick recovery of patients, as verified in multiple studies [ 25 , 31 , 32 , 34 , 35 ]. Although there are concerns regarding increased complications when pairing SEMS placement with chemotherapy, this approach prolongs patient survival [ 42 ]. However, the combination of SEMS and targeted drugs, such as bevacizumab (a monoclonal antibody), remains controversial. Given the role of bevacizumab in hindering blood vessel growth and the potential of SEMSs to erode the intestinal walls, there is an increased risk of ischemic perforation [ 51 ]. A systematic study including 682 patients showed that in patients with AOCC with SEMS, combining chemotherapy and bevacizumab increased the risk of perforation (63.4% vs. 25.7%). However, survival increased: 12.8–43 months vs. 18–20 months [ 52 ]. We suggest that, for patients with AOCC undergoing chemotherapy, SEMS should not be ruled out entirely given its alleviation ability and potential survival benefits.

The surgical treatment of CRC obstruction considers factors such as patient condition, tumor type, stage, personal preferences, and hospital facilities. For patients with AOCC, who typically have poor prognoses, surgical decisions demand caution [ 53 ]. For example, in advanced asymptomatic CRC, PTR does not offer significant benefits for patients compared with those for patients who only receive chemotherapy [ 54 , 55 ]. However, this conclusion requires further investigation in patients with AOCC. Our study found that the patients who underwent surgery had better clinical success rates, fewer long-term complications, and higher survival rates. Although surgery shows only partial advantages over SEMS placement, it provides a basis for individualized treatment.

The optimal selection of surgical approaches is currently conflicting, and there is a lack of refined strategies for selecting the optimal surgical method. According to our meta-analysis, the selection of the surgical method was largely based on the intraoperative conditions and the surgeon’s individual preferences [ 16 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 28 , 29 , 31 , 32 ]. This introduces significant unpredictability into patient prognosis. In our network meta-analysis, PTR exhibited the best clinical success rate and decreased late complications, revealing the benefits of tumor removal. This effectively diminishes the uncertainty introduced by the continued tumor growth in patients with AOCC. Regarding early complications and short-term mortality, the S/B stands out for its rapid procedure, minimal invasiveness, and quick recovery (Fig.  3 ). Thus, from different postoperative perspectives, PTR and S/B each have their advantages.

Surgery for CRC obstruction has shown promise in extending survival rates compared with those of SEMS placement, despite the inherent risks of complications and postoperative mortality. Our study’s HR of 1.24 (95% CI 1.08, 1.42) further highlights this potential advantage, challenging previous meta-analyses [ 8 , 10 , 11 ], yet being consistent with several other studies [ 23 , 26 , 31 , 32 , 34 ]. Although stents effectively alleviate obstruction symptoms, they do not affect the growth or metastasis of primary tumors. Surgical removal decreases the tumor size and reduces the number of tumors, thereby delaying tumor growth and spread. Additionally, selection bias in patients is a factor; those with better health and longer life expectancies are more likely to choose surgery (after consulting with their physicians for treatment options). Thus, the decision between SEMS and surgery should be collaborative, with full consideration of the impact of both options on the survival duration.

In terms of heterogeneity, we found significant heterogeneity ( I 2  > 50%) in four outcomes: early complications, late complications, stoma formation, and length of hospital stay. Subgroup and sensitivity analyses were conducted accordingly. In subgroup analysis, we identified publication year, tumor location, and study type as potential sources of heterogeneity. Left CRC are more prone to obstruction, and SEMS in left CRC is more recommended based on previous studies. Conversely, in other sites of the colon, SEMS is less common due to the higher risks of stent migration and technical difficulties. Regarding publication year and study type, heterogeneity is an inevitable factor.

In sensitivity analyses, we found that the study by Pattarajierapan et al. [ 35 ] had a significant impact on the outcomes of late complications, as reflected in the combined result forest plot (Fig.  2 C). This study compared SEMS versus S/B procedures. In network analysis, S/B procedures showed the highest rate of late complications (Fig.  3 C1), while PTR procedures showed the lowest, which could be a source of heterogeneity. Additionally, since the study population was Asian, racial differences may also be a contributing factor.

Our study had the following limitations:

Heterogeneity was observed among the included studies. Although we attempted to explain some of the heterogeneity among the research results through a subgroup analysis, some heterogeneities remain unexplained.

RCTs are lacking in the literature. In our meta-analysis, only two met the inclusion criteria. Future research requires more RCTs to provide high-quality evidence.

Regarding survival rate studies, higher-quality research is needed. This entails balancing the differences in baseline patient status and tumor conditions across groups. Long-term follow-up is also essential to validate our conclusions.

In conclusion, our meta-analysis showed that SEMS placement has advantages in terms of the incidence of early postoperative complications, postoperative mortality, stoma formation rate, and postoperative hospitalization time in patients with AOCC. However, surgical treatment is superior to SEMS placement in clinical symptom remission, late complication rates, and overall survival rates. PTR can improve the rate of remission of clinical symptoms and decrease the incidence of late complications when selecting specific surgical methods. Patients who underwent S/B had a lower incidence of early complications and 30-day mortality after surgery. These results emphasize that patient condition, patient/physician preferences, and risk factors should be considered when selecting AOCC treatment.

Data availability

No datasets were generated or analysed during the current study.

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Ma, B., Ren, T., Cai, C. et al. Palliative procedures for advanced obstructive colorectal cancer: a systematic review and meta-analysis. Int J Colorectal Dis 39 , 148 (2024). https://doi.org/10.1007/s00384-024-04724-6

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