qualitative findings dissertation example

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How to Write the Dissertation Findings or Results – Steps & Tips

Published by Grace Graffin at August 11th, 2021 , Revised On June 11, 2024

Each  part of the dissertation is unique, and some general and specific rules must be followed. The dissertation’s findings section presents the key results of your research without interpreting their meaning .

Theoretically, this is an exciting section of a dissertation because it involves writing what you have observed and found. However, it can be a little tricky if there is too much information to confuse the readers.

The goal is to include only the essential and relevant findings in this section. The results must be presented in an orderly sequence to provide clarity to the readers.

This section of the dissertation should be easy for the readers to follow, so you should avoid going into a lengthy debate over the interpretation of the results.

It is vitally important to focus only on clear and precise observations. The findings chapter of the  dissertation  is theoretically the easiest to write.

It includes  statistical analysis and a brief write-up about whether or not the results emerging from the analysis are significant. This segment should be written in the past sentence as you describe what you have done in the past.

This article will provide detailed information about  how to   write the findings of a dissertation .

When to Write Dissertation Findings Chapter

As soon as you have gathered and analysed your data, you can start to write up the findings chapter of your dissertation paper. Remember that it is your chance to report the most notable findings of your research work and relate them to the research hypothesis  or  research questions set out in  the introduction chapter of the dissertation .

You will be required to separately report your study’s findings before moving on to the discussion chapter  if your dissertation is based on the  collection of primary data  or experimental work.

However, you may not be required to have an independent findings chapter if your dissertation is purely descriptive and focuses on the analysis of case studies or interpretation of texts.

• Always report the findings of your research in the past tense.
• The dissertation findings chapter varies from one project to another, depending on the data collected and analyzed.
• Avoid reporting results that are not relevant to your research questions or research hypothesis.

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1. Reporting Quantitative Findings

The best way to present your quantitative findings is to structure them around the research  hypothesis or  questions you intend to address as part of your dissertation project.

Report the relevant findings for each research question or hypothesis, focusing on how you analyzed them.

Analysis of your findings will help you determine how they relate to the different research questions and whether they support the hypothesis you formulated.

While you must highlight meaningful relationships, variances, and tendencies, it is important not to guess their interpretations and implications because this is something to save for the discussion  and  conclusion  chapters.

Any findings not directly relevant to your research questions or explanations concerning the data collection process  should be added to the dissertation paper’s appendix section.

Use of Figures and Tables in Dissertation Findings

Suppose your dissertation is based on quantitative research. In that case, it is important to include charts, graphs, tables, and other visual elements to help your readers understand the emerging trends and relationships in your findings.

Repeating information will give the impression that you are short on ideas. Refer to all charts, illustrations, and tables in your writing but avoid recurrence.

The text should be used only to elaborate and summarize certain parts of your results. On the other hand, illustrations and tables are used to present multifaceted data.

It is recommended to give descriptive labels and captions to all illustrations used so the readers can figure out what each refers to.

How to Report Quantitative Findings

Here is an example of how to report quantitative results in your dissertation findings chapter;

Two hundred seventeen participants completed both the pretest and post-test and a Pairwise T-test was used for the analysis. The quantitative data analysis reveals a statistically significant difference between the mean scores of the pretest and posttest scales from the Teachers Discovering Computers course. The pretest mean was 29.00 with a standard deviation of 7.65, while the posttest mean was 26.50 with a standard deviation of 9.74 (Table 1). These results yield a significance level of .000, indicating a strong treatment effect (see Table 3). With the correlation between the scores being .448, the little relationship is seen between the pretest and posttest scores (Table 2). This leads the researcher to conclude that the impact of the course on the educators’ perception and integration of technology into the curriculum is dramatic.

Paired Samples

Paired Samples Correlation

Paired Samples Test

Also Read: How to Write the Abstract for the Dissertation.

2. Reporting Qualitative Findings

A notable issue with reporting qualitative findings is that not all results directly relate to your research questions or hypothesis.

The best way to present the results of qualitative research is to frame your findings around the most critical areas or themes you obtained after you examined the data.

In-depth data analysis will help you observe what the data shows for each theme. Any developments, relationships, patterns, and independent responses directly relevant to your research question or hypothesis should be mentioned to the readers.

Additional information not directly relevant to your research can be included in the appendix .

How to Report Qualitative Findings

Here is an example of how to report qualitative results in your dissertation findings chapter;

The last question of the interview focused on the need for improvement in Thai ready-to-eat products and the industry at large, emphasizing the need for enhancement in the current products being offered in the market. When asked if there was any particular need for Thai ready-to-eat meals to be improved and how to improve them in case of ‘yes,’ the males replied mainly by saying that the current products need improvement in terms of the use of healthier raw materials and preservatives or additives. There was an agreement amongst all males concerning the need to improve the industry for ready-to-eat meals and the use of more healthy items to prepare such meals. The females were also of the opinion that the fast-food items needed to be improved in the sense that more healthy raw materials such as vegetable oil and unsaturated fats, including whole-wheat products, to overcome risks associated with trans fat leading to obesity and hypertension should be used for the production of RTE products. The frozen RTE meals and packaged snacks included many preservatives and chemical-based flavouring enhancers that harmed human health and needed to be reduced. The industry is said to be aware of this fact and should try to produce RTE products that benefit the community in terms of healthy consumption.

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What to Avoid in Dissertation Findings Chapter

• Avoid using interpretive and subjective phrases and terms such as “confirms,” “reveals,” “suggests,” or “validates.” These terms are more suitable for the discussion chapter , where you will be expected to interpret the results in detail.
• Only briefly explain findings in relation to the key themes, hypothesis, and research questions. You don’t want to write a detailed subjective explanation for any research questions at this stage.

The Do’s of Writing the Findings or Results Section

• Ensure you are not presenting results from other research studies in your findings.
• Observe whether or not your hypothesis is tested or research questions answered.
• Illustrations and tables present data and are labelled to help your readers understand what they relate to.
• Use software such as Excel, STATA, and SPSS to analyse results and important trends.

Essential Guidelines on How to Write Dissertation Findings

The dissertation findings chapter should provide the context for understanding the results. The research problem should be repeated, and the research goals should be stated briefly.

This approach helps to gain the reader’s attention toward the research problem. The first step towards writing the findings is identifying which results will be presented in this section.

The results relevant to the questions must be presented, considering whether the results support the hypothesis. You do not need to include every result in the findings section. The next step is ensuring the data can be appropriately organized and accurate.

You will need to have a basic idea about writing the findings of a dissertation because this will provide you with the knowledge to arrange the data chronologically.

Start each paragraph by writing about the most important results and concluding the section with the most negligible actual results.

A short paragraph can conclude the findings section, summarising the findings so readers will remember as they transition to the next chapter. This is essential if findings are unexpected or unfamiliar or impact the study.

Our writers can help you with all parts of your dissertation, including statistical analysis of your results . To obtain free non-binding quotes, please complete our online quote form here .

Be Impartial in your Writing

When crafting your findings, knowing how you will organize the work is important. The findings are the story that needs to be told in response to the research questions that have been answered.

Therefore, the story needs to be organized to make sense to you and the reader. The findings must be compelling and responsive to be linked to the research questions being answered.

Always ensure that the size and direction of any changes, including percentage change, can be mentioned in the section. The details of p values or confidence intervals and limits should be included.

The findings sections only have the relevant parts of the primary evidence mentioned. Still, it is a good practice to include all the primary evidence in an appendix that can be referred to later.

The results should always be written neutrally without speculation or implication. The statement of the results mustn’t have any form of evaluation or interpretation.

Negative results should be added in the findings section because they validate the results and provide high neutrality levels.

The length of the dissertation findings chapter is an important question that must be addressed. It should be noted that the length of the section is directly related to the total word count of your dissertation paper.

The writer should use their discretion in deciding the length of the findings section or refer to the dissertation handbook or structure guidelines.

It should neither belong nor be short nor concise and comprehensive to highlight the reader’s main findings.

Ethically, you should be confident in the findings and provide counter-evidence. Anything that does not have sufficient evidence should be discarded. The findings should respond to the problem presented and provide a solution to those questions.

Structure of the Findings Chapter

The chapter should use appropriate words and phrases to present the results to the readers. Logical sentences should be used, while paragraphs should be linked to produce cohesive work.

You must ensure all the significant results have been added in the section. Recheck after completing the section to ensure no mistakes have been made.

The structure of the findings section is something you may have to be sure of primarily because it will provide the basis for your research work and ensure that the discussions section can be written clearly and proficiently.

One way to arrange the results is to provide a brief synopsis and then explain the essential findings. However, there should be no speculation or explanation of the results, as this will be done in the discussion section.

Another way to arrange the section is to present and explain a result. This can be done for all the results while the section is concluded with an overall synopsis.

This is the preferred method when you are writing more extended dissertations. It can be helpful when multiple results are equally significant. A brief conclusion should be written to link all the results and transition to the discussion section.

Numerous data analysis dissertation examples are available on the Internet, which will help you improve your understanding of writing the dissertation’s findings.

Problems to Avoid When Writing Dissertation Findings

One of the problems to avoid while writing the dissertation findings is reporting background information or explaining the findings. This should be done in the introduction section .

You can always revise the introduction chapter based on the data you have collected if that seems an appropriate thing to do.

Raw data or intermediate calculations should not be added in the findings section. Always ask your professor if raw data needs to be included.

If the data is to be included, then use an appendix or a set of appendices referred to in the text of the findings chapter.

Do not use vague or non-specific phrases in the findings section. It is important to be factual and concise for the reader’s benefit.

The findings section presents the crucial data collected during the research process. It should be presented concisely and clearly to the reader. There should be no interpretation, speculation, or analysis of the data.

The significant results should be categorized systematically with the text used with charts, figures, and tables. Furthermore, avoiding using vague and non-specific words in this section is essential.

It is essential to label the tables and visual material properly. You should also check and proofread the section to avoid mistakes.

The dissertation findings chapter is a critical part of your overall dissertation paper. If you struggle with presenting your results and statistical analysis, our expert dissertation writers can help you get things right. Whether you need help with the entire dissertation paper or individual chapters, our dissertation experts can provide customized dissertation support .

FAQs About Findings of a Dissertation

How do i report quantitative findings.

The best way to present your quantitative findings is to structure them around the research hypothesis or research questions you intended to address as part of your dissertation project. Report the relevant findings for each of the research questions or hypotheses, focusing on how you analyzed them.

How do I report qualitative findings?

The best way to present the qualitative research results is to frame your findings around the most important areas or themes that you obtained after examining the data.

An in-depth analysis of the data will help you observe what the data is showing for each theme. Any developments, relationships, patterns, and independent responses that are directly relevant to your research question or hypothesis should be clearly mentioned for the readers.

Can I use interpretive phrases like ‘it confirms’ in the finding chapter?

No, It is highly advisable to avoid using interpretive and subjective phrases in the finding chapter. These terms are more suitable for the discussion chapter , where you will be expected to provide your interpretation of the results in detail.

Can I report the results from other research papers in my findings chapter?

NO, you must not be presenting results from other research studies in your findings.

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How To Write the Findings Chapter for Qualitative Studies

Writing the findings chapter for qualitative studies is a critical step in the research process. This chapter allows researchers to present their findings, analyze the data collected, and draw conclusions based on the study’s objectives. In this blog post, I’lll explore the purpose, key elements, preparation, writing, and presentation of the findings chapter in qualitative studies.

Introduction: Contextualizing Your Findings

The introductory section serves as the gateway to your qualitative findings chapter. Begin by reiterating your problem statement and research questions, setting the stage for the data that will be presented. Highlighting the purpose of your research is crucial at this point to give context to the reader.

Example: The primary aim of this case study is to explore how educators perceive the integration of artificial intelligence (AI) tools in K-12 education settings. This research seeks to understand the perceptions and experiences of educators who have implemented AI technologies in their classrooms, framed within the context of Technological Pedagogical Content Knowledge (TPACK). In this chapter, data are systematically organized into three overarching themes, each comprising sub-themes that provide a nuanced understanding of the participants’ perspectives. The research questions guiding this investigation were: (1) What are educators’ perceptions of the role of AI tools in K-12 classrooms? (2) How do educators navigate the challenges of integrating AI tools into their teaching practices? The answers to these questions are integral to understanding the complex dynamics of AI implementation in educational contexts.  

Overview of Findings

Following this, offer a brief overview of your main findings. While you should not delve into the details here, giving the reader an idea of what to expect can be helpful. Explain the overall structure of your results chapter and how you’ve organized it to maintain coherence and logical flow.

The Heart of the Matter: The Body of Your Chapter

Presentation.

The body of the chapter is where you lay out your data for the reader. In qualitative research, this usually means dividing your data into themes or categories, which should be clearly described and substantiated with quotes and examples from your dataset. These themes provide the skeletal framework upon which your narrative is built.

Structure and Flow

When planning your qualitative findings chapter, carefully outline the sections and subsections to maintain the flow of the writing and improve readability. You can structure your chapter based on themes, which is often the case in qualitative research, but other formats like chronological or framework-based structures may be more appropriate depending on your specific research design.

✅ Consistency is Key: Make sure each portion of findings adheres to a standardized structure. This enhances consistency and enables the reader to follow your line of reasoning.

Objective and Descriptive Language

While your narrative might touch upon individual experiences and perspectives, remember to maintain an objective tone. Your task is to describe, not interpret—that comes later, in the Discussion chapter. Thus, avoid phrases that suggest interpretation, such as “suggests” or “implies.”

Visual Aids

Tables, figures, and other visual aids can add another layer of comprehension and break up the text, but make sure they can be understood independently of your body text. Label them clearly and use color coding judiciously to indicate differences or hierarchy.

Data Analysis and Interpretation

As you delve into the data, aim to narrate a coherent story. Interpret your findings in light of the literature in the field and your theoretical framework, but remember to clearly differentiate between your descriptions and your interpretations.

✅ References and Appendices: When using quotes or data excerpts, reference them appropriately. Use appendices to present additional data and ensure that you cite them according to the referencing style prescribed by your institution (e.g., APA, Harvard).

Bringing It All Together: The Concluding Summary

This is the section where you summarize your key findings in a concise manner, reiterating points that directly relate to your research questions. It serves as a stepping stone to the Discussion chapter, providing the reader with the essential takeaways. As a rule of thumb, this section should contain no new information.

Additional Tips and Tricks

– Write in the past tense, as you present findings that have already been gathered.

– Review your work multiple times, ensuring each theme or finding is backed by sufficient data.

– Use Microsoft Word’s “heading styles” for consistency.

Final Thoughts

With the right approach, writing the Findings chapter can be an enriching experience that showcases your research and prepares you for discussions and conclusions that follow. The tips and guidelines presented here are meant to make this crucial chapter as clear and impactful as possible, helping you make a valuable contribution to your field of study.

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Author:  Jessica Parker, EdD

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• How to Write a Results Section | Tips & Examples

How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

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, 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 analysed, 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 shop: first discuss the shoes as a whole, then the trainers, 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 visualise 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. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Figure 1: Intention to donate to environmental organisations 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 .

‘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 .

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.

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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What is a theoretical framework?

Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need ‘fixing’ or a gap that your research may begin to fill.

The theoretical framework is your toolbox . In the toolbox are your handy tools: a set of theories, concepts, ideas and hypotheses that you will use to build a solution to the research problem or gap you have identified.

The methodology is the instruction manual: the procedure and steps you have taken, using your chosen tools, to tackle the research problem.

Why do I need a theoretical framework?

Developing a theoretical framework shows that you have thought critically about the different ways to approach your topic, and that you have made a well-reasoned and evidenced decision about which approach will work best. theoretical frameworks are also necessary for solving complex problems or issues from the literature, showing that you have the skills to think creatively and improvise to answer your research questions. they also allow researchers to establish new theories and approaches, that future research may go on to develop., how do i create a theoretical framework for my dissertation.

First, select your tools. You are likely to need a variety of tools in qualitative research – different theories, models or concepts – to help you tackle different parts of your research question.  

When deciding what tools would be best for the job of answering your research questions or problem, explore what existing research in your area has used. You may find that there is a ‘standard toolbox’ for qualitative research in your field that you can borrow from or apply to your own research.

You will need to justify why your chosen tools are best for the job of answering your research questions, at what stage they are most relevant, and how they relate to each other. Some theories or models will neatly fit together and appear in the toolboxes of other researchers. However, you may wish to incorporate a model or idea that is not typical for your research area – the ‘odd one out’ in your toolbox. If this is the case, make sure you justify and account for why it is useful to you, and look for ways that it can be used in partnership with the other tools you are using.

You should also be honest about limitations, or where you need to improvise (for example, if the ‘right’ tool or approach doesn’t exist in your area).

This video from the Skills Centre includes an overview and example of how you might create a theoretical framework for your dissertation:

How do I choose the 'right' approach?

When designing your framework and choosing what to include, it can often be difficult to know if you’ve chosen the ‘right’ approach for your research questions. One way to check this is to look for consistency between your objectives, the literature in your framework, and your overall ethos for the research. This means ensuring that the literature you have used not only contributes to answering your research objectives, but that you also use theories and models that are true to your beliefs as a researcher.

Reflecting on your values and your overall ambition for the project can be a helpful step in making these decisions, as it can help you to fully connect your methodology and methods to your research aims.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this.  Positionality  acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional. It's also important to reflect on your positionality if you belong to the same community as your participants where this is the grounds for their involvement in the research (ie. you are a mature student interviewing other mature learners about their experences in higher education). 

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

• How might my personal history influence how I approach the topic?
• How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
• How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
• Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
• Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

One option for qualitative projects is to write an extended literature review. This type of project does not require you to collect any new data. Instead, you should focus on synthesising a broad range of literature to offer a new perspective on a research problem or question.  

The main difference between an extended literature review and a dissertation where primary data is collected, is in the presentation of the methodology, results and discussion sections. This is because extended literature reviews do not actively involve participants or primary data collection, so there is no need to outline a procedure for data collection (the methodology) or to present and interpret ‘data’ (in the form of interview transcripts, numerical data, observations etc.) You will have much more freedom to decide which sections of the dissertation should be combined, and whether new chapters or sections should be added.

Here is an overview of a common structure for an extended literature review:

Introduction

• Provide background information and context to set the ‘backdrop’ for your project.
• Explain the value and relevance of your research in this context. Outline what do you hope to contribute with your dissertation.
• Clarify a specific area of focus.
• Introduce your research aims (or problem) and objectives.

Literature review

You will need to write a short, overview literature review to introduce the main theories, concepts and key research areas that you will explore in your dissertation. This set of texts – which may be theoretical, research-based, practice-based or policies – form your theoretical framework. In other words, by bringing these texts together in the literature review, you are creating a lens that you can then apply to more focused examples or scenarios in your discussion chapters.

Methodology

As you will not be collecting primary data, your methodology will be quite different from a typical dissertation. You will need to set out the process and procedure you used to find and narrow down your literature. This is also known as a search strategy.

Including your search strategy

A search strategy explains how you have narrowed down your literature to identify key studies and areas of focus. This often takes the form of a search strategy table, included as an appendix at the end of the dissertation. If included, this section takes the place of the traditional 'methodology' section.

If you choose to include a search strategy table, you should also give an overview of your reading process in the main body of the dissertation.  Think of this as a chronology of the practical steps you took and your justification for doing so at each stage, such as:

• Your key terms, alternatives and synonyms, and any terms that you chose to exclude.
• Your choice and combination of databases;
• Your inclusion/exclusion criteria, when they were applied and why. This includes filters such as language of publication, date, and country of origin;
• You should also explain which terms you combined to form search phrases and your use of Boolean searching (AND, OR, NOT);
• Your use of citation searching (selecting articles from the bibliography of a chosen journal article to further your search).
• Your use of any search models, such as PICO and SPIDER to help shape your approach.
• Search strategy template A simple template for recording your literature searching. This can be included as an appendix to show your search strategy.

The discussion section of an extended literature review is the most flexible in terms of structure. Think of this section as a series of short case studies or ‘windows’ on your research. In this section you will apply the theoretical framework you formed in the literature review – a combination of theories, models and ideas that explain your approach to the topic – to a series of different examples and scenarios. These are usually presented as separate discussion ‘chapters’ in the dissertation, in an order that you feel best fits your argument.

Think about an order for these discussion sections or chapters that helps to tell the story of your research. One common approach is to structure these sections by common themes or concepts that help to draw your sources together. You might also opt for a chronological structure if your dissertation aims to show change or development over time. Another option is to deliberately show where there is a lack of chronology or narrative across your case studies, by ordering them in a fragmentary order! You will be able to reflect upon the structure of these chapters elsewhere in the dissertation, explaining and defending your decision in the methodology and conclusion.

A summary of your key findings – what you have concluded from your research, and how far you have been able to successfully answer your research questions.

• Recommendations – for improvements to your own study, for future research in the area, and for your field more widely.
• Emphasise your contributions to knowledge and what you have achieved.

Alternative structure

Depending on your research aims, and whether you are working with a case-study type approach (where each section of the dissertation considers a different example or concept through the lens established in your literature review), you might opt for one of the following structures:

Splitting the literature review across different chapters:

This structure allows you to pull apart the traditional literature review, introducing it little by little with each of your themed chapters. This approach works well for dissertations that attempt to show change or difference over time, as the relevant literature for that section or period can be introduced gradually to the reader.

Whichever structure you opt for, remember to explain and justify your approach. A marker will be interested in why you decided on your chosen structure, what it allows you to achieve/brings to the project and what alternatives you considered and rejected in the planning process. Here are some example sentence starters:

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions. 

Here's an example of how your data could be presented in paragraph format in this section:

Example from  'Reporting and discussing your findings ', Monash University .

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• 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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Presenting your qualitative analysis findings: tables to include in chapter 4.

The earliest stages of developing a doctoral dissertation—most specifically the topic development  and literature review  stages—require that you immerse yourself in a ton of existing research related to your potential topic. If you have begun writing your dissertation proposal, you have undoubtedly reviewed countless results and findings sections of studies in order to help gain an understanding of what is currently known about your topic. 

In this process, we’re guessing that you observed a distinct pattern: Results sections are full of tables. Indeed, the results chapter for your own dissertation will need to be similarly packed with tables. So, if you’re preparing to write up the results of your statistical analysis or qualitative analysis, it will probably help to review your APA editing  manual to brush up on your table formatting skills. But, aside from formatting, how should you develop the tables in your results chapter?

In quantitative studies, tables are a handy way of presenting the variety of statistical analysis results in a form that readers can easily process. You’ve probably noticed that quantitative studies present descriptive results like mean, mode, range, standard deviation, etc., as well the inferential results that indicate whether significant relationships or differences were found through the statistical analysis . These are pretty standard tables that you probably learned about in your pre-dissertation statistics courses.

But, what if you are conducting qualitative analysis? What tables are appropriate for this type of study? This is a question we hear often from our dissertation assistance  clients, and with good reason. University guidelines for results chapters often contain vague instructions that guide you to include “appropriate tables” without specifying what exactly those are. To help clarify on this point, we asked our qualitative analysis experts to share their recommendations for tables to include in your Chapter 4.

Demographics Tables

As with studies using quantitative methods , presenting an overview of your sample demographics is useful in studies that use qualitative research methods. The standard demographics table in a quantitative study provides aggregate information for what are often large samples. In other words, such tables present totals and percentages for demographic categories within the sample that are relevant to the study (e.g., age, gender, job title). 

If conducting qualitative research  for your dissertation, however, you will use a smaller sample and obtain richer data from each participant than in quantitative studies. To enhance thick description—a dimension of trustworthiness—it will help to present sample demographics in a table that includes information on each participant. Remember that ethical standards of research require that all participant information be deidentified, so use participant identification numbers or pseudonyms for each participant, and do not present any personal information that would allow others to identify the participant (Blignault & Ritchie, 2009). Table 1 provides participant demographics for a hypothetical qualitative research study exploring the perspectives of persons who were formerly homeless regarding their experiences of transitioning into stable housing and obtaining employment.

Participant Demographics

Tables to Illustrate Initial Codes

Most of our dissertation consulting clients who are conducting qualitative research choose a form of thematic analysis . Qualitative analysis to identify themes in the data typically involves a progression from (a) identifying surface-level codes to (b) developing themes by combining codes based on shared similarities. As this process is inherently subjective, it is important that readers be able to evaluate the correspondence between the data and your findings (Anfara et al., 2002). This supports confirmability, another dimension of trustworthiness .

A great way to illustrate the trustworthiness of your qualitative analysis is to create a table that displays quotes from the data that exemplify each of your initial codes. Providing a sample quote for each of your codes can help the reader to assess whether your coding was faithful to the meanings in the data, and it can also help to create clarity about each code’s meaning and bring the voices of your participants into your work (Blignault & Ritchie, 2009).

Table 2 is an example of how you might present information regarding initial codes. Depending on your preference or your dissertation committee’s preference, you might also present percentages of the sample that expressed each code. Another common piece of information to include is which actual participants expressed each code. Note that if your qualitative analysis yields a high volume of codes, it may be appropriate to present the table as an appendix.

Initial Codes

Tables to Present the Groups of Codes That Form Each Theme

As noted previously, most of our dissertation assistance clients use a thematic analysis approach, which involves multiple phases of qualitative analysis  that eventually result in themes that answer the dissertation’s research questions. After initial coding is completed, the analysis process involves (a) examining what different codes have in common and then (b) grouping similar codes together in ways that are meaningful given your research questions. In other words, the common threads that you identify across multiple codes become the theme that holds them all together—and that theme answers one of your research questions.

As with initial coding, grouping codes together into themes involves your own subjective interpretations, even when aided by qualitative analysis software such as NVivo  or MAXQDA. In fact, our dissertation assistance clients are often surprised to learn that qualitative analysis software does not complete the analysis in the same ways that statistical analysis software such as SPSS does. While statistical analysis software completes the computations for you, qualitative analysis software does not have such analysis capabilities. Software such as NVivo provides a set of organizational tools that make the qualitative analysis far more convenient, but the analysis itself is still a very human process (Burnard et al., 2008).

Because of the subjective nature of qualitative analysis, it is important to show the underlying logic behind your thematic analysis in tables—such tables help readers to assess the trustworthiness of your analysis. Table 3 provides an example of how to present the codes that were grouped together to create themes, and you can modify the specifics of the table based on your preferences or your dissertation committee’s requirements. For example, this type of table might be presented to illustrate the codes associated with themes that answer each research question. 

Grouping of Initial Codes to Form Themes

Tables to Illustrate the Themes That Answer Each Research Question

Creating alignment throughout your dissertation is an important objective, and to maintain alignment in your results chapter, the themes you present must clearly answer your research questions. Conducting qualitative analysis is an in-depth process of immersion in the data, and many of our dissertation consulting  clients have shared that it’s easy to lose your direction during the process. So, it is important to stay focused on your research questions during the qualitative analysis and also to show the reader exactly which themes—and subthemes, as applicable—answered each of the research questions.

Below, Table 4 provides an example of how to display the thematic findings of your study in table form. Depending on your dissertation committee’s preference or your own, you might present all research questions and all themes and subthemes in a single table. Or, you might provide separate tables to introduce the themes for each research question as you progress through your presentation of the findings in the chapter.

Emergent Themes and Research Questions

Bonus Tip! Figures to Spice Up Your Results

Although dissertation committees most often wish to see tables such as the above in qualitative results chapters, some also like to see figures that illustrate the data. Qualitative software packages such as NVivo offer many options for visualizing your data, such as mind maps, concept maps, charts, and cluster diagrams. A common choice for this type of figure among our dissertation assistance clients is a tree diagram, which shows the connections between specified words and the words or phrases that participants shared most often in the same context. Another common choice of figure is the word cloud, as depicted in Figure 1. The word cloud simply reflects frequencies of words in the data, which may provide an indication of the importance of related concepts for the participants.

As you move forward with your qualitative analysis and development of your results chapter, we hope that this brief overview of useful tables and figures helps you to decide on an ideal presentation to showcase the trustworthiness your findings. Completing a rigorous qualitative analysis for your dissertation requires many hours of careful interpretation of your data, and your end product should be a rich and detailed results presentation that you can be proud of. Reach out if we can help  in any way, as our dissertation coaches would be thrilled to assist as you move through this exciting stage of your dissertation journey!

Anfara Jr., V. A., Brown, K. M., & Mangione, T. L. (2002). Qualitative analysis on stage: Making the research process more public.  Educational Researcher ,  31 (7), 28-38. https://doi.org/10.3102/0013189X031007028

Blignault, I., & Ritchie, J. (2009). Revealing the wood and the trees: Reporting qualitative research.  Health Promotion Journal of Australia ,  20 (2), 140-145. https://doi.org/10.1071/HE09140

Burnard, P., Gill, P., Stewart, K., Treasure, E., & Chadwick, B. (2008). Analysing and presenting qualitative data.  British Dental Journal ,  204 (8), 429-432. https://doi.org/10.1038/sj.bdj.2008.292

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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.

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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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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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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Thank you. I will try my best to write my results.

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this was great explaination

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