You have collected your data, cleaned it and run your analyses. Now you face a folder full of output files and a blank document titled “Chapter 4: Data Analysis and Results”. Many students find this chapter surprisingly hard to write. There is so much output, and it is not obvious what to include, in what order, and how much to explain.
A strong data analysis chapter presents your results clearly, logically and honestly, in a way that directly answers your research questions or tests your hypotheses. This post explains the purpose of the chapter, a recommended structure for quantitative and qualitative studies, what to include in each section, and common mistakes to avoid.
What is the purpose of the data analysis chapter?
The data analysis (or results, or findings) chapter reports what you found. Its main purposes are to:
- Show how the data were prepared and analysed.
- Present results that answer each research question or test each hypothesis.
- Provide enough detail for readers to evaluate the evidence.
- Do this objectively, before interpretation in the discussion chapter.
In many thesis formats, the results chapter reports findings with limited interpretation, while the discussion chapter explains what they mean in relation to theory and literature. Some disciplines and qualitative traditions combine results and discussion. Check your university's guidelines and your supervisor's expectations.
Before you start writing
1. Return to your research questions and hypotheses
List them clearly. Every major section of the chapter should connect to at least one of them. Output that does not relate to a research question probably does not belong in the main text.
2. Organise your output
Save and label your output files, syntax or scripts. Create a simple map linking each analysis to the research question it answers.
3. Decide on your structure
A logical structure makes the chapter easy to follow. Common options include organising by research question, by hypothesis, or by analysis stage.
Suggested structure for a quantitative data analysis chapter
1. Introduction
Briefly restate the purpose of the chapter and outline its structure. Remind readers of the research questions or hypotheses.
2. Data preparation and screening
Explain how you prepared the data:
- Number of responses received and number retained after screening.
- Criteria for removing cases, such as incomplete responses, failed attention checks or duplicates.
- Handling of missing data, including the amount and the method used.
- Treatment of outliers.
- Reverse coding and computation of scale scores.
Some of this may already appear in the methodology chapter. Avoid repeating it at length; refer back where appropriate.
3. Sample characteristics
Present a table of participant demographics and briefly describe the sample. If possible, comment on how it compares with the target population.
4. Measurement quality
Report reliability (such as Cronbach's alpha or composite reliability) and validity evidence (such as EFA or CFA results) for your scales.
5. Descriptive statistics and correlations
Present means, standard deviations and correlations among the main variables. Highlight key patterns, but save detailed interpretation for later.
6. Assumption testing
Report checks of assumptions for your main analyses, such as normality, linearity, homoscedasticity and multicollinearity. Keep this concise; detailed diagnostics can go into an appendix.
7. Hypothesis testing or research question analysis
This is the core of the chapter. For each hypothesis or research question:
- Restate it briefly.
- Name the analysis used.
- Present the key statistics in text and tables or figures: test statistic, degrees of freedom, p-value, effect size and confidence interval.
- State clearly whether the hypothesis was supported, partially supported or not supported.
8. Additional or exploratory analyses
If you ran analyses that were not planned, report them separately and label them as exploratory.
9. Summary
End with a brief summary of the main findings, often including a table listing each hypothesis and whether it was supported. This helps readers and leads into the discussion chapter.
Suggested structure for a qualitative findings chapter
Qualitative findings chapters are organised differently:
- Introduction: restate the research questions and describe how the chapter is organised.
- Participant overview: a table of participant characteristics using pseudonyms or codes.
- Overview of themes: a table or thematic map showing themes and sub-themes.
- Theme-by-theme presentation: for each theme, explain its meaning, describe variations across participants, and support it with well-chosen quotes. Link each theme back to the research questions.
- Summary: summarise how the themes address the research questions.
In qualitative writing, analysis and illustration are woven together. Quotes should support your analytic claims, not replace them. Avoid long strings of quotes with little commentary.
Writing tips for presenting results
Lead with the answer
Start each section with the key finding in plain language, then provide the supporting statistics. For example: “Supervisor support was a significant predictor of job satisfaction, even after controlling for age and tenure (β = .38, p < .001).”
Use tables and figures strategically
Use tables for detailed numbers and figures for patterns. In the text, refer to each table or figure and highlight what matters, rather than repeating every value.
Be consistent
Use the same terminology, variable names, decimal places and formatting throughout. If a variable is called “work engagement” in your hypotheses, do not call it “employee involvement” in the results.
Report all planned analyses
Include non-significant results as well as significant ones. Selective reporting misrepresents the evidence and can be viewed as a research integrity issue.
Report effect sizes and confidence intervals
Do not rely on p-values alone. Effect sizes show the magnitude of findings.
Avoid over-interpretation
In the results chapter, describe what you found; save the “why” and the “so what” for the discussion. Avoid causal language unless your design supports it.
Use past tense
Results are usually reported in the past tense: “Participants reported...”, “The analysis showed...”.
Example: writing up one hypothesis
Here is an illustrative example of how a single hypothesis might be presented in a quantitative results chapter.
4.6.2 Hypothesis 2: Supervisor support and job satisfaction
Hypothesis 2 predicted that supervisor support would be positively associated with job satisfaction after controlling for age, gender and tenure. A hierarchical multiple regression was conducted, with control variables entered in Step 1 and supervisor support in Step 2 (Table 4.7).
The control variables explained 4% of the variance in job satisfaction, R² = .04, F(3, 246) = 3.42, p = .018. Adding supervisor support in Step 2 explained an additional 17% of the variance, ΔR² = .17, F(1, 245) = 52.7, p < .001. Supervisor support was a significant positive predictor of job satisfaction, β = .42, 95% CI [.31, .53], p < .001. Hypothesis 2 was therefore supported. (All values are illustrative.)
Notice the structure: restate the hypothesis, name the analysis, refer to the table, present the key statistics in a logical order, and finish with a clear statement about whether the hypothesis was supported. Repeating this structure for each hypothesis makes the chapter predictable in a good way; examiners know exactly where to look for each answer.
A hypothesis summary table
At the end of a quantitative results chapter, a simple summary table is very helpful. It might have three columns:
- Hypothesis: a short version, such as “H2: Supervisor support → job satisfaction (+)”.
- Analysis: such as “Hierarchical regression”.
- Result: “Supported”, “Partially supported” or “Not supported”, perhaps with the key statistic.
This table gives readers an overview at a glance and provides a natural bridge into your discussion chapter.
What about mixed methods studies?
In mixed methods research, you have several options: present quantitative and qualitative results in separate chapters, present them in separate sections of one chapter, or organise by research question and integrate both strands within each section. Whichever you choose, make the integration explicit. For example, show where qualitative findings help explain a surprising quantitative result, or where the two strands agree or disagree. A joint display table, placing quantitative results and related qualitative themes side by side, is a useful tool for this.
How much detail should I include?
Include enough detail for a knowledgeable reader to understand and evaluate your analyses, but not so much that key findings get lost. A useful approach:
- Main text: key statistics, summary tables and figures that answer research questions.
- Appendices: full output tables, detailed assumption checks, additional analyses, full item-level statistics and coding frameworks.
Common mistakes to avoid
- Pasting raw software output into the chapter.
- Organising the chapter by software procedure rather than by research question.
- Reporting results without linking them to hypotheses.
- Omitting non-significant findings.
- Repeating every number in both tables and text.
- Interpreting and discussing literature extensively in the results chapter (unless your format combines them).
- Inconsistent variable names and formatting.
- Presenting qualitative themes as lists of quotes without analysis.
A quick self-check before submission
- Does every section link clearly to a research question or hypothesis?
- Is the order logical, moving from data preparation to descriptive results to main analyses?
- Have I reported all planned analyses?
- Are tables and figures properly formatted, numbered and referred to?
- Are effect sizes and confidence intervals included?
- Is there a clear summary at the end?
- Could someone understand my main findings by reading only the first sentence of each section?
Final thoughts
The data analysis chapter is where your research questions meet your evidence. Organise it around your research questions, present results clearly with well-designed tables and figures, report effect sizes and non-significant findings honestly, and keep interpretation for the discussion. A well-structured results chapter makes your thesis easier to examine and sets up a strong discussion of what your findings mean.
Are you writing your results chapter now? Share your biggest challenge in the comments below.