Key Takeaways: The results chapter is where your data becomes evidence. Your job is to report findings clearly and consistently so a reader can follow what you tested or explored, what you found, and how the results connect back to your research question(s). The fastest way to improve your results is to structure them around your research question(s) or hypotheses, present numbers and/or themes with the right level of detail and use tables and/or figures to make the story easy to scan.
Table of Contents
Introduction
Your results chapter is not where you argue what your findings mean. It is where you report what you found within the scope of your investigations, clearly, accurately, and in a way that readers can verify. In a bachelor’s or master’s thesis, as well as in a CAS, DAS, MAS, or MBA thesis, the results chapter is where you demonstrate that your analysis was carried out properly and that your findings are presented transparently.
At Delta Lektorat, we regularly see high-quality research weakened in the results chapter, making it difficult to follow, presenting inconsistent findings, or overloaded with interpretation. Likewise, we encounter high-quality analyses whose key messages get lost in the presentation, for example, because important results are hidden in long paragraphs or presented in unclear tables. This guide consolidates our experience from editing into a practical framework for writing a results chapter that is clear, well-substantiated, and aligned with academic standards. By the end, you will know which elements to include, which common mistakes to avoid, and how to structure your results chapter so that it highlights your central findings and supports the discussion, rather than raising new questions.
Writing the Results: Key Elements and Common Pitfalls
Before you start writing, keep this guiding question in mind: What did I find within the scope of my investigation and how can I present it so the reader can follow it without my interpretation?
A strong results chapter typically includes the following key elements (the exact structure depends on your research design, discipline, and university requirements):
- Brief reminder of the analytical logic: A concise introduction that reminds readers what you analysed (e.g., variables/themes, dataset, groups/conditions) and how the results chapter is structured.
- Descriptive results: Sample description and key descriptive statistics (quantitative) or an overview of the dataset and participants (qualitative), as well as, where relevant, results of data preparation (e.g., exclusions).
- Main results aligned with research question(s) or hypotheses: Presentation of results in the same order as your research question(s) or hypotheses.
- Tables and figures: Clearly designed visualisations that support the text (properly labeled and referenced in the text).
- Reporting standards and consistency: Uniform terminology, units of measurement, rounding, and labels across text, tables, and figures.
- Robustness or quality checks (if applicable): e.g., checking assumptions, reliability tests, or measures to ensure trustworthiness, reported briefly and neutrally.
- Neutral mini-summary: A summary of the key findings at the end of the chapter (still without interpretation).
It’s worth noting that examiners often assess a results chapter by how clearly each finding answers a specific research question or hypothesis. Here’s our pro tip:
Engaging Readers from Start to Finish: Question to Answer Strategy
Pro tip: Treat each subsection in the results chapter as a direct answer to a specific research question or hypothesis. Begin the subsection by clearly stating in one sentence what you tested. Then, present only the relevant findings and conclude the subsection with a precise statement of the result for the respective research question or hypothesis, without any interpretation beyond what is supported by the analysis.
Mini memory aid for students
Results: report, state, clarify, visualise.
Discussion: contextualise, interpret, evaluate.
(1) Stating the question you are answering (Subsection opening):
At the start of each results subsection, make it immediately clear:
- which research question (RQ) or hypothesis (H) is addressed in this subsection,
- which variables/constructs are being examined (quantitative research only), and
- which analysis was used to answer the question.
Example 1: Qualitative Study
To answer RQ1, the study examined how students perceive the influence of digital learning platforms on their learning motivation. The interviews were analyzed using Mayring’s qualitative content analysis (N = 12). Central themes of flexibility, self-regulation, and overload were identified, appearing in varying degrees across the participants’ responses.
Example 2: Quantitative Study
To test H1, the study examined whether there is a relationship between the intensity of digital learning platform use and learning motivation. The analysis was conducted using linear regression (N = 214). A significant positive relationship was found (β = 0.42; 95% CI [0.31, 0.53]; p < 0.001), confirming H1.
(2) Reporting the answer clearly (Subsection body)
In the main section, present the results as a sequence of statements in terms of “what was found”:
- begin with the main result that directly answers the research question/hypothesis,
- include secondary metrics (additional statistical details) only if they clarify the main result (e.g., direction, strength, uncertainty) (quantitative research only),
- keep the wording descriptive (avoid terms like “suggests,” “implies,” “important,” or “significant” unless they are statistically defined).
Example 1: Qualitative Research
Regarding RQ1, it was found that students primarily described digital learning platforms as tools for increasing temporal flexibility in their learning. The interviews identified the themes of flexibility, self-regulation, and technical overload, with flexibility mentioned in most statements (9 out of 12 interviews). Additionally, differences in the perceived degree of self-regulation between participants became apparent.
Example 2: Quantitative Research
To test H1, a positive relationship was observed between the intensity of digital learning platform use and learning motivation (b = 0.38; 95% CI [0.27, 0.49]; p < 0.001). The regression model explained 29% of the variance in learning motivation (R² = 0.29).
(3) Closing the loop (Subsection closing)
End each subsection with a one-sentence “answer statement” that explicitly links the finding back to the RQ/H, and, if relevant, states the key boundary conditions factually (e.g., sample, design, measurement scope).
Example 1: Qualitative Research
Overall, the analysis shows that the surveyed students predominantly describe digital learning platforms as tools for increasing the temporal flexibility of their learning, thus answering RQ1 based on 12 interviews.
Example 2: Quantitative Research
Overall, the analysis shows a positive relationship between the intensity of digital learning platform use and learning motivation, providing evidence for H1, within the studied sample (N = 214) and based on a linear regression model.
The examples are illustrative only. Use them as orientation and adapt the wording to your research question, design, discipline, and institutional guidelines.
At the end, you should follow these to do’s and avoid common pitfalls:
Do’s: | Don’ts: | • Report findings in the same order as your RQ/hypotheses.
• Use tables/figures to reduce text and improve clarity. • State what the result is, then point to the evidence. • Use consistent labels, units, and rounding • Report exclusions and sample sizes transparently. | • Jumping between topics and forcing the reader to hunt.
• Copy-pasting huge output tables or figures without explanation. • Interpreting, explaining causes, or adding implications. • Changing variable names or reporting formats across sections. • Hiding missing data handling or dropping cases without stating it. |
|---|
Use this checklist below to ensure that your introduction covers all essential elements:
Checklist for your Results
A Step-by-Step Guide with Example
A strong results chapter follows a clear internal logic. The overview below shows how the key elements can be structured into three sequential steps.
| Step | Content Elements | Purpose |
|---|---|---|
|
Step 1: Engaging readers Guiding question: What data and structure should the reader expect? |
|
Orient readers by making clear what was analysed and how the results chapter is structured, so that the results are easily comprehensible. |
|
Step 2: Report the main findings Guiding question: What was found for each research question or hypothesis? |
|
Report clearly, consistently, and without interpretation results with which each research question is answered or each hypothesis is tested; the underlying empirical findings (e.g., key parameters, decision rules) are described in a comprehensible manner and, where necessary, presented clearly through tables and figures. |
|
Step 3: Close the chapter neutrally Guiding question: What are the key results (without interpretation)? |
|
Summarise the key results without interpretation and guide readers purposefully into the discussion chapter. |
Ready to see this in action? Below is a condensed example that demonstrates the flow, and which you can adapt to your discipline.
Concrete Example of a Results Chapter (Quantitative Research)
Step 1: Engaging readers – What data and structure should be expected?
Naming the object of analysis
The study examined the relationship between students' perceived usefulness of a university online learning platform and their intention to continue using it.
Explaining the chapter structure
The results are structured according to the study's hypotheses (H1–H3). First, key descriptive results are reported, followed by the presentation of inferential statistical analyses to test the hypotheses.
Presenting the descriptive baseline
In total, 371 valid questionnaires were included in the analysis after incomplete datasets had been excluded. The sample consisted of [X%] [group] and [Y%] [group]; the average age was M = [M] years (SD = [SD]). Table 1 provides an overview of the key sample characteristics.
Step 2: Report the main findings – What was found for each research question or hypothesis?
Establishing the results structure
The main results are reported below hypothesis by hypothesis (H1–H3). Each subsection begins with the respective main result and is supplemented by the key empirical findings.
Formulating the main result (hypothesis test: H1)
To test H1, we examined whether perceived usefulness was associated with the intention to continue using the platform.
Specifying empirical findings
The multiple regression analysis showed a positive relationship between perceived usefulness and intention to use. The key parameters of the estimation are shown in Table 2 (including regression coefficients, confidence intervals, and model metrics).
Visualising results
Table 2 summarises the results of the regression analysis for testing H1.
(Further subsections on H2 and H3 follow analogously.)
Step 3: Closing the chapter neutrally – What are the key results (without interpretation)?
Summarising key findings
In summary, the analyses show that perceived usefulness is significantly associated with the intention to continue using the platform (H1), whilst for H2 and H3 [significant/non-significant] relationships in a [positive/negative] direction were identified.
Transitioning to the discussion
The interpretation of these results within the theoretical and empirical research context is provided in the discussion chapter (Chapter X).
Concrete Example of a Results Chapter (Qualitative Research)
Step 1: Engaging readers – What data and structure should be expected?
Naming the object of analysis
The study examined how students perceive the use of a university online learning platform and which aspects they describe as conducive or obstructive to continued use.
Explaining the chapter structure
The results are structured according to the study's research questions (RQ1–RQ3). First, an overview of the analysed data material is provided, followed by a presentation of the key themes and categories for each research question.
Presenting the descriptive baseline
The analysis is based on 12 semi-structured interviews with students from different degree programmes and semesters. The data material comprises a total of [X] minutes of audio material, which was fully transcribed and included in the analysis. Table 1 provides an overview of the interview sample.
Step 2: Presenting main results – What was found for each research question?
Establishing the results structure
The main results are reported below research question by research question (RQ1–RQ3). Each subsection begins with a clear statement of the key result and is supplemented by the associated empirical findings.
Formulating the main result (research question RQ1)
To answer RQ1, we examined how students perceive the use of the online learning platform in their daily studies.
Specifying empirical findings
The qualitative content analysis showed that the perceived usefulness of the platform was discussed primarily in connection with temporal flexibility, location-independent access, and individual learning organisation. These aspects were mentioned in most interviews; differences emerged in the intensity and manifestation of the experiences described.
Structuring and illustrating results
Table 2 summarises the key categories and subcategories for RQ1. Additionally, selected brief interview statements are cited in the text to illustrate typical manifestations of the identified themes.
(Further subsections on RQ2 and RQ3 follow analogously.)
Step 3: Closing the chapter neutrally – What are the key results (without interpretation)?
Summarising key findings
In summary, the results show that students describe the online learning platform positively, particularly about its perceived usefulness and flexibility, whilst limitations were mainly discussed in relation to [aspect] and [aspect].
Transitioning to the discussion
The interpretation of these findings and their significance for existing research and practice are presented in the discussion chapter (Chapter X).
After you have written your results, ask yourself: Can a reader understand, based solely on this chapter, what was found, specifically in relation to the tested hypotheses or the investigated research questions?
Using AI to Accelerate Your Results Writing
We could not leave AI out of this conversation, knowing that AI tools can help you refine thoughts and remember key elements you need for your research work. Here’s a ready-to-use AI mega prompt*:
Mega Prompt for a Clear Results Chapter
I am writing the results chapter of my [master’s thesis / dissertation] in the field of [discipline]. Based on the information below, create a clearly structured, academically appropriate results chapter that reports findings neutrally (no interpretation, implications, or recommendations). Use headings and a formal academic style. Do not invent any numbers or results; use placeholders such as [ ] if information is missing.
Included sections:
- Engage the reader (Step 1): briefly name the subject of analysis, provide a concise explanation of the chapter structure, and present key descriptive information about the data (qualitative: type and scope of the material; quantitative: sample size and key characteristics).
- Report main results (Step 2): present results in the order of the research question(s) or hypothesis(es) (one subsection per research question/hypothesis), each with a clear statement of the main finding and precise empirical details (quantitative: key statistics; qualitative: main themes/categories); refer to tables and figures as appropriate to support the text.
- Conclude neutrally (Step 3): provide a brief, factual summary of the main findings (short paragraph with 3–5 bullet points) without interpretation, and explicitly refer to the discussion chapter, where significance and contextualization are addressed.
Reporting rules:
- Only report results derived from your chosen method and present them consistently (quantitative: e.g., means, effect sizes, confidence intervals; qualitative: e.g., categories, themes, or typical manifestations).
- Use descriptive, non-interpretative phrasing (e.g., “was associated with,” “was higher/lower,” “themes were identified”) and avoid causal statements unless these are explicitly supported by the study design.
- Use consistent terminology throughout, aligned with the variables, categories, scales, research questions, and hypotheses used.
Details about my study:
[Insert here research questions/hypotheses, analysis plan, key results (numbers/themes), and tables/figures.]
__________
*Delta Lektorat privacy note: When using AI tools, do not paste any identifying or confidential information from your research (e.g., participant names, organisations, exact locations, or raw transcripts). Use placeholders instead, such as [University], [Company], [City], [Participant Group].
Several AI tools can support you on this journey. Claude, ChatGPT, Gemini, and Perplexity all handle this prompt effectively, each with different strengths. Remember that the prompt gives you a starting point and some ideas to build on, but AI drafts are just drafts. Your task is to properly write and revise the content and make sure it truly reflects your analysis.
When using AI in academic writing, make sure your practice aligns with the ethical expectations of your field. Start by checking your institution’s or publisher’s policy, as rules on AI use and disclosure vary widely. If disclosure is required, clearly state that AI assisted in drafting and explain its role. Maintain full intellectual ownership by ensuring that all analysis, interpretation, and final claims reflect your own critical judgment rather than unverified AI output. The key point is this: the reported findings must be based on your own analysis and verified outputs (e.g., software results, tables, coding summaries). AI can help you present results clearly, but it must not generate, alter, or “fill in” results, and it must not replace your responsibility to check accuracy, consistency, and appropriate reporting standards.
Conclusion
The results chapter is where your analysis becomes evidence. By reporting your findings clearly, consistently, and in the same order as your research question(s) or hypotheses, you make it easy for readers to understand what you found, and you set yourself up for a stronger discussion chapter.
If you want your thesis to read as coherent and academically rigorous, keep your results chapter neutral and let the discussion do the interpreting. Our guides on writing a strong introduction, a coherent methodology section, a discussion chapter, and an effective conclusion provide practical, step-by-step instructions to help you bring your research together coherently.
If you want tailored feedback on your results chapter or entire thesis, book a coaching call or submit your thesis to receive professional editing and proofreading.
Frequently Asked Questions (FAQs)
There is no fixed length for a results chapter; the guidelines of your institution are always decisive. Based on experience, however, the results chapter typically comprises about 20–25% of the total work. Prioritise clarity over length: use tables and figures to present complex results efficiently, and, if necessary, move detailed statistical outputs or extensive qualitative material to the appendix.
In most disciplines, no. The results chapter is reserved for reporting findings only. Interpretation, explanation, and implications belong in the discussion chapter. If your discipline permits limited interpretation, keep it brief, clearly signposted, and strictly grounded in the analysis.
Summarise the key finding in one clear sentence and refer the reader to the relevant table or figure (e.g., “see Table 2”). Do not reproduce all numerical values or themes in paragraph form, as this reduces readability and adds redundancy.
Yes. Quotes should be concise, anonymized, and clearly assigned to a reported topic or category. They serve to support key findings, not for interpretation or contextualization.
Yes. Reporting only statistically significant or “positive” findings can be misleading. A transparent results chapter includes all analyses specified in the methodology, regardless of outcome, allowing the discussion chapter to address their relevance.
Editors primarily assess clarity, structure, and consistency. Our experts look for results that are logically organised around research questions or hypotheses, reported neutrally, and aligned with the methodology. Delta Lektorat supports authors by refining structure, improving academic language, ensuring consistency across text, tables, and figures, and helping results meet academic standards without drifting into interpretation.
Disclosure: This article was prepared by human contributors. Generative AI tools were used to support brainstorming, language refinement, and structural editing. All final decisions regarding content, recommendations, and academic insights reflect human judgment and expertise.
References
Dietrichs, I. (2018). Academic writing in a Swiss university context. Pressbooks Hochschule Luzern. https://ebooks.hslu.ch/academicwriting/chapter/conclusion-recommendations/
Ni, N. (2024, May 27). How to write a good results section. Science & Society – SciComm. https://www.the-scientist.com/how-to-write-a-good-results-section-71858
Swales, J. M., & Feak, C. B. (2012). Academic writing for graduate students: Essential tasks and skills (3rd ed.). University of Michigan Press.
Dea Elmasllari, MSc.
Dea is a senior researcher passionate about helping students navigate the world of academia. She explores the intersection of Artificial Intelligence (AI) and scholarly work, offering insights on how technology can enhance writing, research, and learning. As the Head of Partnerships at Delta Lektorat, Dea leads collaborations with universities and student associations to promote excellence in academic writing and innovative approaches to thesis support. Her work focuses on bridging traditional academic rigour with emerging digital tools that empower students and scholars alike.
Dr. Meriton Ceka
Meriton is a scholarly author, lecturer, and researcher with many years of experience supporting academic writing. He assists students and researchers in producing structured, clear, and persuasive work. As the founder of Delta Lektorat, he collaborates closely with universities to promote academic excellence through professional feedback and methodological clarity.
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