Introduction: Why the Data Analysis Section Is the Heart of Your Thesis

You’ve spent months—maybe even years—reading, planning, collecting, and cleaning your data. Finally, you’ve arrived at the stage every graduate student both dreams of and dreads: How to Write the Data Analysis Section.

This chapter is where your research comes to life. It’s the moment your work stops being theoretical and starts becoming evidence-based. But writing the data analysis section can be intimidating, especially if you’re asking questions like:

• How do I start writing my results?

• What counts as “analysis” versus “interpretation”?

• How do I make sure my tables, charts, and text look professional?

If you’ve found yourself Googling how to write the data analysis section in a thesis or how to write the data analysis chapter in a dissertation, you’re not alone. It’s one of the most searched topics among graduate students because it’s one of the most important—and most misunderstood—chapters in academic writing.

In this guide, I’ll walk you step-by-step through:

• Structuring your results chapter logically.

• Writing quantitative results in APA style with correct formatting.

• Writing qualitative results with clear themes and participant quotes.

• Handling mixed methods results without creating confusion.

• Avoiding common mistakes students make.

• Using templates and tools to save time.

By the end, you’ll have a clear, ready-to-use formula for writing your data analysis section—whether you’re reporting a regression model, summarizing interview themes, or both.

1. Understanding the Purpose of the Data Analysis Section

Before you start writing, you need to understand what this section actually does.

The data analysis section is about presenting your findings, not interpreting them. Think of it as a courtroom witness: its job is to state the facts clearly and accurately. The “lawyer” (your discussion chapter) will later argue what those facts mean.

Your goals in this chapter are to:

1. Answer your research questions or hypotheses directly.

2. Present results in a clear, logical, and consistent way.

3. Follow academic formatting rules (APA, MLA, Chicago, etc.).

4. Maintain objectivity—no personal opinions or “I think” statements.

2. What to Include and What to Avoid

Include:

• A brief chapter introduction.

• A restatement of your research questions/hypotheses.

• A short recap of analysis methods (optional if covered in methodology).

• Results (quantitative, qualitative, or both).

• Tables and figures (formatted correctly).

• A short chapter summary or transition to discussion.

Avoid:

• Lengthy literature review quotes.

• Personal opinions.

• Over-explaining what results mean (save for discussion).

• Data unrelated to your research questions.

3. Structuring the Data Analysis Chapter

A clean structure helps your reader follow your work—and it also keeps you from getting lost as you write.

Suggested Structure:

1. Introduction (short, sets up the chapter)

2. Restate Research Questions (optional, but useful for clarity)

3. Overview of Analysis Methods (brief recap)

4. Results Section(s):

   • Quantitative results first (APA format).

   • Qualitative results after.

   • If mixed methods, keep them in clearly separated subsections.

5. Tables and Figures (embedded where relevant).

6. Summary Paragraph (recaps main results in 3–4 sentences).

Pro Tip:
If your thesis has multiple research questions, use subheadings for each question and put the relevant results under them. This not only keeps things organized but also makes it easy for your examiner to see you’ve addressed everything.

4. Writing Quantitative Data Analysis

Quantitative data reporting can feel rigid, but that’s because it follows specific rules—especially if you’re using APA style. Students often search how to report statistical results in a thesis (APA), so let’s break it down step-by-step.

4.1 Step One: Start with Descriptive Statistics

Before diving into inferential tests (like t-tests or ANOVA), start with descriptive statistics that summarize your data. This gives your reader a picture of your sample.

Include:

• Mean (M)

• Standard deviation (SD) or Variance (Check the difference between variance and Standard Deviation)

• Median (Mdn) if useful

• Minimum and maximum values

• Sample size (N)

Example:

The sample (N = 120) had a mean age of 22.4 years (SD = 2.6). The average GPA was 3.45 (SD = 0.41), with scores ranging from 2.80 to 4.00.

APA Table Example:

Note: In APA, the table number and title go above the table, and notes (if any) go below.

4.2 Step Two: Report Inferential Statistics

Once you’ve described your sample, move on to the tests you used to answer your research questions. For each, report:

• The name of the test.

• The test statistic (italicized).

• Degrees of freedom (in parentheses).

• The value of the statistic.

• The p-value.

• Effect size (Cohen’s d, η², etc.).

Independent Samples t-test Example:

An independent-samples t-test showed a statistically significant difference in exam scores between Group A (M = 85.2, SD = 4.8) and Group B (M = 79.6, SD = 5.2), t(58) = 4.13, p < .001, Cohen’s d = 1.07.

One-Way ANOVA Example:

A one-way ANOVA revealed a significant effect of study method on exam performance, F(2, 57) = 5.21, p = .008, η² = .16. Post-hoc Tukey tests indicated that Group A performed significantly better than Group C (p = .006).

Regression Example:

A simple linear regression found that study hours significantly predicted GPA, b = 0.04, t(118) = 3.92, p < .001, R² = .115.

Chi-Square Example:

The relationship between gender and participation in extracurricular activities was statistically significant, χ²(1, N = 120) = 4.22, p = .040, φ = 0.19.

4.3 Step Three: Present Tables and Figures in APA Format

If you’ve been wondering how to present tables and figures APA (thesis), here are the essentials:

• The table number and title go above the table.

• The figure number and caption go below the figure.

• Refer to every table and figure in your text.

Example reference in text:

As shown in Table 2, the experimental group’s mean score was higher than that of the control group.

Checklist for Quantitative Reporting:
✅ Every result has df, p-value, and effect size.
✅ Tables/figures follow APA rules.
✅ Only relevant results are included.
✅ No interpretation is mixed in.

That’s Part 1 (about 1,450 words).

If you’re ready, I’ll continue with Part 2, which will cover:

• The rest of the quantitative reporting (multiple test examples).

• Full qualitative reporting with multiple frameworks.

• Mixed methods section.

4.4 Expanding Quantitative Reporting – Test-by-Test Walkthrough

Now that you know the APA basics, let’s go deeper with specific test examples students frequently use in theses. The goal is to give you a ready-to-adapt formula for each type of analysis so you can plug in your own numbers without worrying about style.

Paired Samples t-test

Used when you have two measurements from the same group (pre-test and post-test).

Example:

A paired-samples t-test revealed a statistically significant increase in scores from pre-test (M = 75.4, SD = 6.2) to post-test (M = 82.1, SD = 5.9), t(29) = 6.48, p < .001, Cohen’s d = 1.18.

Two-Way ANOVA

Used to examine the effect of two independent variables on one dependent variable, plus interaction effects.

Example:

A two-way ANOVA showed a significant main effect of teaching method on exam scores, F(1, 56) = 12.33, p = .001, η² = .18, and a significant main effect of gender, F(1, 56) = 4.45, p = .039, η² = .07. The interaction effect between teaching method and gender was not significant, F(1, 56) = 2.01, p = .162.

Multiple Regression

Used when predicting a dependent variable from two or more independent variables.

Example:

A multiple regression was conducted to predict GPA from study hours and attendance rate. The overall model was significant, F(2, 117) = 14.82, p < .001, R² = .202. Study hours contributed significantly to the prediction (β = .32, p < .001), as did attendance rate (β = .28, p = .002).

Logistic Regression

Used when predicting a binary outcome.

Example:

A binary logistic regression showed that study hours significantly predicted the likelihood of passing the course (OR = 1.45, 95% CI [1.18, 1.77], p < .001). Attendance rate was not a significant predictor (p = .084).

Correlation

Used to measure the strength and direction of association between two variables.

Example:

Pearson’s correlation showed a strong positive relationship between study hours and GPA, r(118) = .65, p < .001.

Effect Sizes and Confidence Intervals

Effect sizes (Cohen’s d, η², r²) tell you how big the difference or relationship is — not just whether it’s statistically significant.

Confidence intervals (CIs) give a range within which the true effect likely falls.

Example:

The difference in means had a large effect size, Cohen’s d = 0.80, 95% CI [0.42, 1.18].

Mini-Checklist – Quantitative Section:

• Start with descriptive statistics.

• Move to inferential stats, grouped by research question.

• Include test name, df, statistic, p, effect size, CI.

• Tables/figures referenced properly.

5. Writing Qualitative Data Analysis

If your study involves interviews, focus groups, or open-ended responses, your results section will present themes, codes, and supporting quotes rather than statistical tests.

Students often search how to write qualitative results chapter (thematic analysis), and the key is to keep it organized and concise while letting participant voices shine.

5.1 Thematic Analysis Structure

A common approach is thematic analysis, where you group data into patterns or themes.

For each theme:

• Provide a theme heading (short, descriptive).

• Briefly explain the theme.

• Provide 2–3 representative quotes.

• Note participant IDs (e.g., P3, P14).

Example:

Theme 1: Feeling Under-Prepared
Participants described entering the data analysis phase with uncertainty and lack of training.

“I had no clue how to choose a statistical test — it was terrifying.” (P3)
“All I knew was how to enter data in Excel, not how to analyze it.” (P8)

Theme 2: Data Cleaning as a Turning Point
For many, cleaning the dataset increased understanding.

“Removing outliers helped me actually understand the shape of my data.” (P7)

5.2 Grounded Theory Example

If you used grounded theory, your results section might present categories and subcategories that emerged from your coding process.

Example:

Core Category: Navigating Academic Uncertainty

• Subcategory: Seeking Informal Support

  “I turned to YouTube tutorials because I couldn’t get help from my supervisor.” (P12)

• Subcategory: Learning Through Trial and Error

  “I ran my test five times before I understood the syntax.” (P5)

5.3 Content Analysis Example

Content analysis often includes frequency counts of codes/themes alongside qualitative interpretation.

Example:

5.4 Presenting Quotes

Best Practices:

• Keep quotes short (1–3 sentences).

• Use ellipses (…) for omitted text.

• Avoid overly long monologues.

• Always anonymize names or sensitive details.

5.5 Avoiding Common Qualitative Mistakes

• Too many quotes without explanation.

• No clear connection between theme and research question.

• Over-interpreting in the results section.

Mini-Checklist – Qualitative Section:

• Each theme supported by quotes.

• Participant IDs anonymized.

• Logical theme order.

• Minimal interpretation here (save for discussion).

6. Writing Mixed Methods Data Analysis

If your research uses both quantitative and qualitative data, the main challenge is keeping them distinct while showing how they answer your research questions.

6.1 Structuring Mixed Methods Results

Two common approaches:

Sequential:

• Present quantitative results first.

• Present qualitative results second.

Concurrent:

• For each research question, present both quantitative and qualitative results together in subsections.

Example – Sequential:

Quantitative Results:

Exam scores increased by 12% after the intervention (p = .003).

Qualitative Results:

Students reported higher confidence: “I felt like I could finally understand the material.” (P6)

Example – Concurrent:

RQ1: How did the intervention affect student performance and perceptions?

• Quantitative: Test scores improved significantly (p < .01).

• Qualitative: Themes included improved confidence and greater engagement.

6.2 Integration Tips

• Keep methods separate in the results chapter.

• Save combined interpretation for the discussion chapter.

• Use parallel subheadings so readers can match findings across methods.

7. Templates and Tools for Data Analysis Writing

7.1 Quantitative Templates

APA Table Shell:

Statistical Result Sentence Formula:

A [test type] showed [direction/size] [outcome] between [groups/variables], statistic(df) = value, p = value, effect size = value, 95% CI [lower, upper].

7.2 Qualitative Templates

Theme Template:

Theme X: [Title]
Short description of the theme.

“Quote 1” (Participant ID)
“Quote 2” (Participant ID)

7.3 Recommended Tools

• SPSS – Popular for statistical testing.

• JASP – Free, open-source alternative with APA-ready output.

• NVivo – For coding and analyzing qualitative data.

• Atlas.ti – Another qualitative data analysis tool.

That’s Part 2.

Part 3 will cover:

• Common mistakes & how to avoid them.

• Extended FAQs (to catch SEO long-tail searches).

• Full chapter summary example.

• Ready-to-use student checklists.

• Closing section with call-to-action for readers.

8. Common Mistakes in the Data Analysis Chapter (and How to Avoid Them)

Even strong researchers can lose marks in the data analysis chapter because of preventable mistakes. Let’s break down the most frequent ones so you can avoid them.

8.1 Mixing Results with Interpretation

• The problem: You slip into explaining what the results mean instead of just stating them.

• Why it’s bad: It confuses readers and blurs the line between results and discussion chapters.

• Fix: Keep this chapter purely factual. Save phrases like “this suggests” or “this could mean” for the discussion.

8.2 Overloading with Tables and Figures

• The problem: Including every chart or table you made during analysis.

• Why it’s bad: Clutters the chapter, distracts from key points.

• Fix: Only include visuals that directly address your research questions. Put extras in appendices.

8.3 Omitting Effect Sizes and Confidence Intervals

• The problem: Reporting statistical significance without context.

• Why it’s bad: P-values alone don’t tell the size or importance of an effect.

• Fix: Always add effect sizes (Cohen’s d, η², r²) and CIs.

8.4 No Connection Between Themes and Research Questions (Qualitative)

• The problem: Themes feel random or disconnected from your study’s purpose.

• Why it’s bad: Makes your analysis look unfocused.

• Fix: Organize qualitative themes around research questions or objectives.

8.5 Formatting Errors (Especially APA)

• The problem: Incorrect placement of titles, captions, or statistical notation.

• Why it’s bad: Looks unprofessional and can cause point deductions.

• Fix: Use the official APA manual or free APA templates from university writing centers.

Pro Tip: Before submission, do a self-audit with this mini-checklist:

• Every statistic has test name, df, value, p, effect size.

• All tables/figures referenced in text.

• No interpretation mixed in.

• Formatting matches style guide.

9. Extended FAQs – Student Search Favorites

Adding an FAQ section isn’t just good for your readers—it’s excellent for SEO. These questions come directly from real student search queries.

Q1: What exactly goes in the data analysis section of a thesis?

Only your results: statistical outputs (for quantitative) or themes/quotes (for qualitative). Exclude interpretations, discussions, or literature comparisons.

Q2: How long should the data analysis chapter be?

It depends on your field and research type. For most master’s theses, 15–25% of total word count is normal. A 20,000-word thesis might have a 3,000–5,000-word results chapter.

Q3: How to report p-values in APA style?

Format: t(df) = value, p = value, effect size = value. For example: t(38) = 2.45, p = .019, Cohen’s d = 0.65. Report p-values to three decimal places, unless p < .001.

Q4: How do I present interview quotes in a dissertation?

Use short, relevant quotes under each theme, followed by participant ID. Example:

“I felt overwhelmed by the number of statistical tests to choose from.” (P5)

Q5: How is the Results chapter different from the Discussion chapter?

Results = “what” you found.
Discussion = “so what” it means.

Q6: Can I combine quantitative and qualitative results?

Yes, but keep them clearly separated or presented per research question. Integration happens in the discussion chapter.

Q7: Should I include non-significant results?

Yes—especially if they answer your research questions. Omitting them can be seen as selective reporting.

10. Example Chapter Summary

A strong summary wraps up your analysis chapter and prepares the reader for the discussion.

Example:

This chapter presented the results of both quantitative and qualitative analyses. Quantitative findings indicated a significant improvement in exam scores following the intervention, with large effect sizes. Regression analysis further revealed that study hours and attendance rate predicted 20% of the variance in GPA. Qualitative themes reinforced these findings, highlighting increased student confidence and the role of peer study groups. The next chapter will interpret these results in the context of existing literature and theoretical frameworks.

11. Student-Friendly Checklists

Adding checklists to your post makes it more actionable and shareable.

Quantitative Results Checklist

• Descriptive statistics reported (M, SD, N).

• Inferential stats for each research question.

• df, test value, p, effect size, CI for each test.

• APA-formatted tables and figures.

• No unnecessary results.

Qualitative Results Checklist

• Each theme clearly named.

• Short explanation of each theme.

• 2–3 quotes per theme, anonymized.

• Logical flow of themes.

• No deep interpretation here.

Mixed Methods Checklist

• Quantitative and qualitative results separated.

• Parallel subheadings used where possible.

• All findings tied to research questions.

12. Ready-to-Use Templates

Giving your readers templates adds huge value—and keeps them coming back for more.

Template 1 – Quantitative Result Sentence

A [test name] showed that [brief result], statistic(df) = value, p = value, effect size = value, 95% CI [lower, upper].

Template 2 – Qualitative Theme Presentation

Theme X: Title
Short description of the theme.

“Quote 1” (Participant ID)
“Quote 2” (Participant ID)

Template 3 – APA Table Shell

13. Wrapping It All Up – Your Data Analysis Game Plan

The data analysis section is more than just a requirement—it’s the foundation for your discussion chapter, conclusions, and recommendations.

Here’s your quick game plan:

1. Know your audience (academic, but also human).

2. Follow a logical order: intro → descriptives → inferential → visuals → summary.

3. Keep results and interpretation separate.

4. Format everything (especially stats) correctly.

5. Use templates to save time and ensure consistency.

Now you know How to Write the Data Analysis Section perfectly