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Contents
- Glossary of Key Terms
- Key Takeaways
- What Is the Results Chapter, and Why Does It Matter?
- What Should You Include in the Results Chapter?
- How Should You Structure the Results Chapter?
- Step-by-Step: How Do You Write the Results Chapter?
- How Do You Write Qualitative Results?
- What Are the Rules for Using Tables and Figures?
- Annotated Discipline-Specific Examples
- Should You Report Unexpected or Negative Findings?
- What Language and Tense Conventions Apply to the Results Chapter?
- How Do You Transition from Results to Discussion?
- What Are the Most Common Mistakes in the Results Chapter?
- Pre-Submission Checklist for the Results Chapter
- Frequently Asked Questions
Glossary of Key Terms
The following terms appear throughout this guide. Familiarity with these concepts will help you navigate the guidance and apply it confidently to your own dissertation.
| Term | Definition |
| Cronbach’s Alpha | A reliability coefficient (0 to 1) measuring the internal consistency of a composite scale; values above 0.7 are generally acceptable. |
| Descriptive Statistics | Summary measures (mean, median, standard deviation, frequency) that describe the basic features of a dataset without making inferences beyond the data. |
| Effect Size | A quantitative measure indicating the practical magnitude of a difference or relationship, independent of sample size (e.g., Cohen’s d, Pearson’s r). |
| Hypothesis | A formal, testable prediction about the relationship between two or more variables, stated before data collection. |
| Inferential Statistics | Statistical methods (t-tests, ANOVA, regression) used to draw conclusions about a population based on a sample. |
| Null Hypothesis (H0) | The default assumption that there is no significant relationship or difference between variables under investigation. |
| Outlier | An observation that lies an abnormal distance from other values in the dataset; may indicate data entry errors or genuine anomalies. |
| p-value | The probability of obtaining a result at least as extreme as the one observed, assuming the null hypothesis is true; commonly compared against a 0.05 significance threshold. |
| Qualitative Data | Non-numerical data such as interview transcripts, observational notes, or written responses, typically analyzed for themes, patterns, or meanings. |
| Quantitative Data | Numerical data collected through surveys, experiments, or secondary sources, analyzed using statistical methods. |
| Reliability | The degree to which a measurement instrument produces consistent, repeatable results under the same conditions. |
| Research Question | A focused, clear question that guides the study and determines which data to collect and how to analyze it. |
| Statistical Significance | An indication that an observed result is unlikely to have occurred by chance alone, typically denoted by p < 0.05. |
| Thematic Analysis | A qualitative method for identifying, analyzing, and reporting patterns (themes) within qualitative data. |
| Validity | The extent to which a measure accurately captures the concept it claims to measure. |
| Variable | Any attribute, characteristic, or factor that can take different values across observations (independent, dependent, or control). |
Key Takeaways
- The results chapter presents your findings objectively and factually, without interpretation or discussion.
- Align every finding you report directly with a stated research question or hypothesis to maintain a clear golden thread.
- Use tables, figures, and charts to supplement written text, not to replace it.
- Quantitative results should include demographic data, reliability tests, descriptive statistics, and inferential statistics in that order.
- Qualitative results should be organized by theme or research question, supported by representative participant quotations.
- Report unusual findings, outliers, or null results transparently rather than omitting them.
- Maintain tense consistency: use past tense to report what you found and present tense only for established facts.
- Keep interpretation strictly within the discussion chapter unless your institution combines results and discussion.
- Label and caption all figures and tables sequentially, and always cross-reference them in the running text.
- Proof your results chapter meticulously: statistical errors in this section undermine the entire dissertation.
What Is the Results Chapter, and Why Does It Matter?
The results chapter is the factual core of your dissertation. It presents the data you collected and analyzed, without interpretation, argument, or commentary. Every claim in this chapter should be traceable directly to your data.
The chapter matters because it provides the empirical foundation for your discussion. Without a clearly written results section, your subsequent interpretation lacks credibility. Examiners look to this chapter to verify that your methodology produced usable data and that you can represent those data accurately and objectively.
How Does the Results Chapter Differ from the Discussion Chapter?
The distinction between results and discussion is fundamental. Results describe what you found; discussion explains what those findings mean. The table below illustrates the key differences:
| Feature | Results Chapter | Discussion Chapter |
| Core function | Present data objectively | Interpret and contextualize data |
| Use of literature | Not included | Integrated to compare with findings |
| Subjectivity | None | Analytical and evaluative |
| Language | Dry, factual, past tense | Evaluative, critical, analytical |
Think of your results as the portion of an iceberg visible above water and your discussion as the larger portion submerged below: one is immediately observable; the other requires deeper investigation to understand.
Can Results and Discussion Be Combined?
In some disciplines and institutions, a single combined results and discussion chapter is acceptable or even preferred. This is more common in qualitative research and in fields such as education, social sciences, and the humanities. Even within a combined chapter, however, it is good practice to present a subset of results before discussing them, then move to the next subset, rather than merging description and interpretation sentence by sentence.
Always check your institution’s specific guidelines and consult your supervisor before deciding on the format. Different departments can have very different expectations, and no single structure fits all disciplines.
What Should You Include in the Results Chapter?
After completing your data analysis, you will likely have far more material than belongs in the results chapter. Your task is to select findings that directly address your research aims, objectives, and research questions. Everything else may inform your understanding but does not need to appear in the chapter.
Quantitative Results: Core Components
For quantitative dissertations, the results chapter typically includes five main components, presented in the order shown below:
| Component | Purpose | Example Content |
| 1. Sample demographics | Assess representativeness of the sample | Age range, gender distribution, geographic region, education level |
| 2. Reliability tests | Validate composite measurement scales | Cronbach’s Alpha values for Likert-scale constructs |
| 3. Descriptive statistics | Summarize the shape of the data | Means, standard deviations, frequencies, and histograms |
| 4. Inferential statistics | Test relationships between variables | Correlation, regression, t-test, ANOVA, chi-square results |
| 5. Hypothesis test outcomes | Report acceptance or rejection of H0 | p-values, confidence intervals, effect sizes |
Not every study requires all five components. For example, if your research is purely descriptive, you may not need inferential statistics. Use your research questions as the litmus test for relevance.
Qualitative Results: Core Components
For qualitative dissertations, the results chapter presents themes, patterns, or categories derived from your data. The exact structure depends on your analytical approach (thematic analysis, grounded theory, discourse analysis, etc.).
- A brief restatement of the data sources and analytical approach used.
- A clear description of each major theme or category identified.
- Representative quotations or excerpts from participants or documents to illustrate each theme.
- Sub-themes or patterns nested within major themes, where applicable.
- Frequency or prevalence information (e.g., the proportion of participants who expressed a particular view), where relevant.
- Disconfirming cases or negative evidence that complicates straightforward thematic conclusions.
Mixed-Methods Results
Mixed-methods dissertations present both quantitative and qualitative findings, typically in separate subsections. You may present the quantitative strand first and then the qualitative strand, or vice versa, depending on which strand is primary. Always indicate explicitly how the two sets of findings relate to or illuminate each other, though the detailed integration usually belongs in the discussion chapter.
How Should You Structure the Results Chapter?
The optimal structure depends on the nature of your study. Four organizational frameworks are commonly used, and in practice, many dissertations combine more than one.
| Framework | Best Suited For | How It Works |
| By research question or hypothesis | Studies with multiple discrete research questions | Devote a subsection to each question; present only the data relevant to that question. |
| By variable | Experimental or quasi-experimental designs | Isolate each independent or dependent variable; report how results changed across levels. |
| Chronological or procedural | Longitudinal studies; pre-test/post-test designs | Report data in the order they were collected; highlights change over time. |
| By research method | Mixed-methods studies | Separate quantitative and qualitative strands; report each in its own subsection. |
A Recommended Internal Structure
Regardless of the organizational framework chosen, most results chapters follow this internal sequence:
- Step 1: Opening paragraph: briefly restate the research aims and explain how the chapter is organized.
- Step 2: Data preparation and quality checks: describe reliability tests, data cleaning steps, and any exclusions.
- Step 3: Sample or participant overview: present demographics and assess representativeness.
- Step 4: Main findings: present the substantive results organized by your chosen framework.
- Step 5: Secondary or ancillary findings: report additional results that may be relevant to the discussion.
- Step 6: Closing summary: briefly summarize the key patterns emerging from the data without interpreting them.
Step-by-Step: How Do You Write the Results Chapter?
Follow the steps below in sequence. Each step builds on the previous one and ensures that your chapter is coherent, aligned with your research questions, and examiner-ready.
Step 1: Revisit Your Research Questions and Objectives
Before writing a single sentence of results, return to your research aims, objectives, and research questions. These are the governing framework for your entire chapter. For each research question, list the specific data, tests, or analyses that address it. Then, for each piece of analysis, decide in which order it should appear in the chapter. This process creates an outline that reflects the logic of your study rather than the chronological order in which you ran your analyses.
Step 2: Write a Brief Opening Introduction
Begin the chapter with a short introduction of no more than two paragraphs. Explain what the chapter covers, how it is organized, and how it connects to the research questions. Weave the research questions explicitly into this section so that examiners immediately see the connection between your method and your findings. This creates the golden thread that should run throughout the entire dissertation.
Step 3: Present Sample Demographics
Present the characteristics of your study sample as the first substantive finding. This provides essential context for interpreting all subsequent results. Typical demographic variables include age, gender, ethnicity, education level, and geographic location. Compare your sample to the broader population of interest to assess representativeness.
Annotated Example (Education):
The study recruited 214 secondary school teachers across four state schools in the Midlands region of England. Table 1 summarizes the sample demographics. Female teachers comprised 62.1% of the sample (n = 133), consistent with the national teacher workforce demographic reported by the Department for Education (2022), in which women account for 62% of all secondary school teachers in England. The majority of respondents (74.3%) held a postgraduate teaching qualification. This close alignment between the sample and national benchmarks supports the potential generalizability of the findings within the English secondary school context.
Step 4: Report Reliability and Data Quality Checks
Before presenting substantive findings, demonstrate that your data are fit for the analytical methods you used. For quantitative data, this typically involves reporting Cronbach’s Alpha for composite scales and checking distributional assumptions (e.g., normality, homoscedasticity). For qualitative data, describe how you established trustworthiness through methods such as member checking, reflexivity, or intercoder reliability.
Annotated Example (Business):
Reliability analysis was conducted on the four-item Employee Engagement Scale adapted from Schaufeli and Bakker (2003). Cronbach’s Alpha was 0.87, exceeding the commonly accepted threshold of 0.70 (Nunnally, 1978), indicating strong internal consistency. Normality of the engagement scores was assessed using the Shapiro-Wilk test (W = 0.97, p = 0.23), and the null hypothesis of normality was not rejected, supporting the use of parametric inferential tests in subsequent analyses.
Step 5: Present Descriptive Statistics
Report the central tendency, spread, and distribution of all key variables before moving to inferential testing. Descriptive statistics allow the reader to understand the raw landscape of the data. Use tables for efficiency when reporting multiple variables, and accompany each table with a brief narrative that highlights the most notable features.
Annotated Example (Biology):
Table 3 presents the descriptive statistics for cell proliferation rates (cells/mm²/hour) across the three treatment groups: control, low-dose (5 μM), and high-dose (20 μM). The mean proliferation rate was highest in the control group (M = 48.7, SD = 3.2), followed by the low-dose group (M = 41.3, SD = 4.1) and the high-dose group (M = 29.6, SD = 5.7). The increasing standard deviation across treatment groups suggests that higher compound concentrations may introduce greater variability in cellular response.
Step 6: Report Inferential Statistics and Hypothesis Test Outcomes
Inferential statistics form the analytical heart of most quantitative dissertations. For each inferential test, report the test statistic, degrees of freedom, p-value, and effect size. Always contextualize the result by linking it explicitly to the relevant hypothesis or research question. Do not interpret the meaning here; that belongs in the discussion chapter.
| Statistical Test | When to Use | Key Values to Report |
| Independent samples t-test | Comparing means of two independent groups | t-value, df, p-value, Cohen’s d |
| One-way ANOVA | Comparing means of three or more groups | F-ratio, df (between/within), p-value, eta-squared |
| Pearson’s correlation | Assessing linear relationship between two continuous variables | r, p-value, sample size |
| Multiple linear regression | Predicting one continuous outcome from multiple predictors | R², adjusted R², F-ratio, beta coefficients, p-values |
| Chi-square test | Assessing association between two categorical variables | χ², df, p-value, Cramer’s V |
Annotated Example (Chemistry):
A one-way ANOVA was conducted to determine whether mean reaction yield (%) differed significantly across the five catalyst concentrations tested (0%, 0.5%, 1.0%, 2.0%, and 5.0% w/v). The ANOVA was statistically significant, F(4, 45) = 18.74, p < 0.001, η² = 0.63, indicating a large effect of catalyst concentration on yield. Post-hoc Tukey HSD comparisons revealed that the 2.0% concentration produced significantly higher yields than all other concentrations (all p < 0.01). The 5.0% concentration did not differ significantly from the 2.0% concentration (p = 0.43), suggesting a plateau effect at higher concentrations.
Statistical precision is critical in this section. Even minor inconsistencies in reported values can undermine your examiner’s confidence in your methodology. If you want expert eyes on your results chapter before submission, consider the Editage Dissertation Editing and Proofreading Service, which specializes in checking statistical reporting, language, and formatting for academic dissertations.
How Do You Write Qualitative Results?
Qualitative results chapters present themes, categories, or patterns derived from your data. The primary challenge is presenting findings that are rich and grounded in evidence without sliding into interpretation.
Organizing Qualitative Findings by Theme
Thematic analysis is the most widely used approach in qualitative dissertations. After coding your data, group codes into broader themes and sub-themes. Each theme becomes a subsection of your results chapter, introduced with a brief descriptive label and supported by quotations.
- Introduce the theme with a clear, descriptive heading.
- Summarize the theme in two to three sentences before presenting supporting evidence.
- Select the most representative or illuminating quotation to illustrate the theme; do not include every quotation you collected.
- Identify the source of each quotation using a participant code (e.g., P07, T03) rather than personal names to preserve anonymity.
- Briefly note the frequency or prevalence of the theme (e.g., expressed by 15 of 20 participants) to give the reader a sense of how widespread the pattern is.
- Describe any variation or contradictions within the theme rather than presenting it as monolithic.
Annotated Example (Education):
Theme 2: Perceived Lack of Institutional Support. Fourteen of the eighteen participants expressed the view that their schools provided insufficient resources for implementing inclusive teaching practices. This theme was expressed most vividly by a Year 9 science teacher with 11 years of experience: “We are told to differentiate for every learner, but we are given one teaching assistant for thirty-two students, six of whom have Education, Health and Care Plans. There is a fundamental mismatch between the rhetoric and the reality” (P09). This sentiment was echoed across both urban and rural school contexts, suggesting that resource limitations are not geographically confined.
Using Participant Quotations Effectively
Quotations are the evidence base of qualitative results. However, poor use of quotations is a common weakness. The table below contrasts effective and ineffective quotation practices:
| Practice | Effective | Ineffective |
| Length | Concise excerpts (2 to 5 lines) that capture the essence of the theme | Long, unedited blocks of transcript that overwhelm the analysis |
| Attribution | Anonymous participant codes (P01, T04) or pseudonyms | Real names, or no attribution at all |
| Introduction | Contextualized with a sentence explaining what to look for | Dropped in without framing or commentary |
| Frequency | One or two exemplary quotations per theme or sub-theme | Every quotation collected, regardless of redundancy |
What Are the Rules for Using Tables and Figures?
Tables and figures are essential tools for presenting data clearly and efficiently. The rule is straightforward: use them to supplement your written narrative, never to replace it. Every table and every figure must be introduced and cross-referenced in the running text.
Numbering, Captioning, and Referencing
- Number tables and figures sequentially in the order they appear: Table 1, Table 2, Figure 1, Figure 2.
- Place a descriptive caption above each table and below each figure.
- Keep captions concise but informative enough for the reader to understand the table or figure without reading the surrounding text.
- Always introduce a table or figure before it appears: “Table 4 presents…” or “As shown in Figure 2…”.
- If you reproduce a table or figure from another source, include a full source citation in the caption.
Choosing Between Tables and Figures
| Use a Table When… | Use a Figure When… |
| You are reporting precise numerical values that the reader may need to look up | You want to show trends, distributions, or relationships visually |
| You are comparing multiple variables across multiple groups | You are reporting proportions or percentages (bar or pie charts) |
| Your data have a structured, categorical organization | You want to illustrate change over time (line graphs) |
| Your narrative references specific numbers frequently | The pattern in the data is more important than the precise values |
Formatting tables and figures to meet APA, Harvard, or your institution’s style guide can be surprisingly time-consuming. The Editage Dissertation Editing and Proofreading Service includes a detailed check of figure and table formatting to ensure everything is consistent, correctly captioned, and style-compliant before submission.
Annotated Discipline-Specific Examples
The results chapter looks different across disciplines. The annotated examples below illustrate how field-specific conventions shape structure, language, and content.
Education: Survey-Based Quantitative Study
Study context: A dissertation examining the relationship between formative assessment frequency and student academic self-efficacy across 214 secondary school teachers.
Excerpt: A Pearson correlation analysis was conducted to examine the association between the frequency of formative assessment (measured in assessments per week) and students’ reported academic self-efficacy scores (out of 100). The analysis revealed a statistically significant, moderate positive correlation, r(212) = 0.48, p < 0.001. This result indicates that classes in which formative assessment was administered more frequently tended to be associated with higher student self-efficacy scores, addressing Research Question 2. The strength of the association (r = 0.48) is consistent with Cohen’s (1988) designation of a medium effect.
Annotation: Note that the author reports the test statistic, degrees of freedom, p-value, and effect size in one sentence. The research question is referenced explicitly. No interpretation of why the correlation exists is offered; that belongs in the discussion.
Chemistry: Experimental Laboratory Study
Study context: A dissertation investigating the effect of varying palladium catalyst concentrations on the Suzuki-Miyaura cross-coupling reaction yield.
Excerpt: Table 5 presents the mean percentage yield (± standard error) for each of the five palladium catalyst concentrations tested across three independent replicates. Yield increased progressively from 0% palladium (M = 12.4%, SE = 1.1%) to 2.0% palladium (M = 87.3%, SE = 1.8%), at which point the increase appeared to plateau. The 5.0% concentration produced a mean yield of 88.1% (SE = 2.0%), representing a non-significant increase relative to the 2.0% concentration (p = 0.43). Two outlying yield values at the 5.0% concentration (54.2% and 59.7%) were investigated and found to be attributable to documented equipment calibration anomalies on the measurement dates in question; these values were retained in the analysis but flagged for discussion.
Annotation: The author identifies outliers transparently and explains their origin, rather than simply deleting them. Reporting error statistics (SE) alongside means is standard in experimental chemistry. The plateau effect is described factually, leaving causal explanation to the discussion.
Business: Mixed-Methods Study
Study context: A dissertation examining the factors influencing employee turnover intention in the retail banking sector, using a sequential explanatory mixed-methods design.
Quantitative strand excerpt: Multiple linear regression was used to examine predictors of turnover intention (n = 187). The overall regression model was statistically significant, F(4, 182) = 22.37, p < 0.001, R² = 0.33, indicating that the four predictors collectively explained 33% of the variance in turnover intention. Job satisfaction (β = -0.41, p < 0.001) and perceived organizational support (β = -0.29, p = 0.003) were the strongest negative predictors.
Qualitative strand excerpt: Theme 3: Limited Career Progression Opportunities. Eleven of fifteen interview participants cited the absence of a transparent promotion pathway as a primary driver of their intention to leave. As one branch manager stated: “I have been in this grade for six years. I was told twice that a promotion was coming. It never did. At some point you accept that you are invisible” (P11). This theme appeared most pronounced among employees aged 30 to 40, suggesting an alignment with mid-career career development concerns.
Annotation: Mixed-methods results are presented in clearly labeled strands. The quantitative strand uses precise statistical notation. The qualitative strand uses a participant code and contextualizes the quotation. Neither strand interprets why the findings occurred.
Biology: Experimental Study with Visual Data
Study context: A dissertation investigating the inhibitory effect of a novel compound (Compound X) on the proliferation of HeLa cervical cancer cells at varying concentrations.
Excerpt: Figure 2 displays the dose-response curve for Compound X on HeLa cell proliferation over a 72-hour incubation period. Cell viability declined in a concentration-dependent manner across all doses tested (0.1 μM to 100 μM). The half-maximal inhibitory concentration (IC₅₀) was calculated as 8.4 μM (95% CI: 7.1 to 9.9 μM) using nonlinear regression. At the highest concentration tested (100 μM), cell viability was reduced to 4.3% (± 0.8%) of the untreated control, consistent with near-complete inhibition of proliferation. These results address Hypothesis 1, which predicted a significant dose-dependent reduction in HeLa cell viability.
Annotation: The figure is cross-referenced before it appears. IC₅₀ values include confidence intervals, which is standard reporting in pharmacological biology. The link to the hypothesis is stated explicitly, maintaining the golden thread.
Laboratory and experimental results require meticulous attention to scientific notation, unit consistency, and statistical reporting conventions. Before you submit, the Editage Dissertation Editing and Proofreading Service can check your manuscript for scientific language accuracy and help ensure that your results chapter meets the standards of your discipline.
Should You Report Unexpected or Negative Findings?
Yes, always. Unexpected findings, null results, and outliers are part of the scientific and scholarly record. Omitting them is a form of reporting bias that undermines the integrity of your dissertation and misrepresents the evidence. Report all relevant findings, including those that do not support your hypotheses or that contradict your literature review.
How to Handle Null and Unexpected Results
- State the result clearly and factually: ‘The analysis did not reveal a statistically significant association between X and Y (r = 0.08, p = 0.43).’
- Do not qualify the result with apologies or hedges in the results chapter; save your explanation for the discussion.
- Report outliers: describe them, state whether they were retained or removed, and explain the basis for that decision.
- If a data collection instrument underperformed (e.g., Cronbach’s Alpha below 0.70), report this transparently and note the implications for reliability.
- For qualitative research, report disconfirming cases: data points that do not fit the dominant themes add nuance and credibility.
What Language and Tense Conventions Apply to the Results Chapter?
The language of the results chapter is dry, direct, and precise by design. It should read like a factual account, not an argument or a narrative. The following conventions apply:
| Convention | Guidance | Example |
| Tense | Past tense for describing what you found; present tense for established facts | “The analysis revealed…” vs. “The p-value threshold is 0.05…” |
| Voice | Passive voice is acceptable and common in sciences; active voice is often preferred in social sciences and humanities | “Data were collected from…” (passive) vs. “We collected data from…” (active) |
| Objectivity | Avoid evaluative language; describe without judging | “Results differed significantly” not “Results were disappointingly different” |
| Precision | Use exact numerical values; avoid vague quantifiers | “68.3% of respondents” not “most respondents” |
Common Language Pitfalls to Avoid
- Saying ‘proves’ or ‘confirms’: results provide evidence for or against a hypothesis; they do not prove anything conclusively.
- Conflating statistical significance with practical importance: a result can be statistically significant but have a negligible effect size.
- Interpreting in the results chapter: phrases such as ‘this suggests that,’ ‘this implies,’ or ‘this demonstrates’ signal interpretation and belong in the discussion.
- Using vague terms: replace ‘a lot,’ ‘some,’ and ‘many’ with precise counts or percentages.
- Inconsistent notation: use one notation system throughout (e.g., APA for psychology, Vancouver for medicine) and apply it uniformly.
How Do You Transition from Results to Discussion?
The transition between the results and discussion chapters is one of the most important structural moments in your dissertation. Readers need a clear signal that the mode of writing is shifting from descriptive to interpretive.
Closing the Results Chapter
- End the results chapter with a brief summary paragraph that recaps the major patterns and findings without interpreting them.
- Refer back to the research questions to show that all of them have been addressed by the data presented.
- Avoid introducing new data or new concepts in the closing paragraph.
- Use a brief transitional sentence that signals the shift: ‘The following chapter interprets these findings in relation to the theoretical framework and existing literature.’
The Boundary Between Results and Discussion
The iceberg metaphor is helpful here: the results chapter shows the visible portion of your findings, the facts you can state with certainty. The discussion chapter dives beneath the surface to explore what those facts mean, why they occurred, how they compare to existing research, and what implications they carry. Maintain this boundary strictly throughout both chapters.
The boundary between results and discussion is one of the most frequently flagged issues by dissertation examiners and peer reviewers. Professional editing can help you identify and resolve these boundary violations before submission. Explore the Editage Dissertation Editing and Proofreading Service to see how expert editors support PhD and master’s candidates through every chapter of their dissertations.
What Are the Most Common Mistakes in the Results Chapter?
Awareness of common errors can help you avoid them during drafting and revision. The mistakes below appear frequently in dissertations across all disciplines.
| Mistake | Why It Is a Problem | How to Fix It |
| Including interpretation in the results | Blurs the boundary between description and analysis; confuses the reader about where interpretation begins | Move all explanatory or evaluative language to the discussion chapter |
| Presenting every piece of analysis conducted | Creates an unfocused, overly long chapter; buries the key findings | Select only findings that directly address the research questions |
| Substituting tables for written narrative | Tables do not speak for themselves; readers need guidance on what to notice | Always describe the most important features of each table in the text |
| Omitting effect sizes from inferential tests | p-values alone do not communicate the practical significance of findings | Report Cohen’s d, eta-squared, r, or equivalent alongside p-values |
| Inconsistent table and figure numbering | Creates confusion and signals careless proofreading | Number all visuals sequentially in order of appearance |
| Not addressing all research questions | Leaves the dissertation incomplete; examiners will specifically check for this | Use the research questions as a checklist before finalizing the chapter |
| Using first person inconsistently | Inconsistency is jarring; follows no recognized convention | Decide on one voice (first or third person) and apply it throughout |
| Reporting incomplete statistical output | Omitting degrees of freedom, test statistics, or p-values is a methodological red flag | Use a statistical reporting checklist aligned with your field |
Pre-Submission Checklist for the Results Chapter
Use the checklist below before submitting your dissertation. Each item addresses a specific examiner concern.
- Every finding reported addresses at least one research question or hypothesis.
- Sample demographics are presented clearly with appropriate comparisons to the target population.
- Reliability or trustworthiness checks are reported and meet acceptable thresholds.
- Descriptive statistics are presented before inferential tests.
- All inferential tests include the test statistic, degrees of freedom, p-value, and effect size.
- Null and unexpected results are reported transparently.
- All tables and figures are numbered sequentially and captioned descriptively.
- Every table and figure is cross-referenced in the running text before it appears.
- No interpretation, argumentation, or reference to literature appears in this chapter.
- Past tense is used consistently throughout when describing findings.
- The chapter ends with a brief, non-interpretive summary of the major patterns.
- A transitional sentence links the results chapter to the discussion chapter.
- All statistical notation is consistent with the required citation style (APA, Harvard, Vancouver, etc.).
- Outliers and anomalies are reported, their handling is explained, and the justification is clear.
Once you have worked through this checklist, a professional editor can catch issues you may have overlooked after weeks of close reading. The Editage Dissertation Editing and Proofreading Service offers specialized dissertation editing that covers structure, language, statistical reporting, referencing, and formatting, giving you confidence that your results chapter is exam-ready.
Frequently Asked Questions
How long should the results chapter be?
Length varies by discipline and study design, but the results chapter is typically the shortest of the core chapters. A quantitative master’s dissertation might have a results chapter of 2,500 to 4,000 words; a PhD dissertation might run to 5,000 to 8,000 words. Qualitative results chapters are often longer due to the inclusion of extended quotations. Always prioritize quality and relevance over word count.
Should I use first person or third person in the results chapter?
This depends on disciplinary convention and institutional preference. Sciences traditionally prefer third person and passive voice (e.g., ‘Data were analyzed using…’). Social sciences and education increasingly accept first person (e.g., ‘I analyzed…’). Mixed practice within a single chapter is generally discouraged. Follow your institution’s guidelines, and if in doubt, ask your supervisor.
Do I need to include all the statistical output from SPSS or R?
No. Full statistical output (SPSS tables, R console output) belongs in an appendix, if anywhere. In the results chapter, report only the specific values required to address your research questions: test statistics, degrees of freedom, p-values, and effect sizes. If an examiner wants to see the full output, they will look in the appendices.
Can I use subheadings in the results chapter?
Yes, and in most cases, you should. Subheadings organized by research question, hypothesis, theme, or variable make a long results chapter far more navigable. They also signal to examiners that the chapter is structured and purposeful rather than a stream of data.
What is the difference between the results and findings chapters?
The terms are used interchangeably in most contexts. Some qualitative traditions prefer ‘findings’ because ‘results’ implies a quantitative focus, but in practice, most institutions accept either term. The key is consistency: use one term throughout your dissertation. Check your institution’s guidelines or dissertation handbook for the preferred terminology.
How should I handle data that do not support my hypotheses?
Report them honestly and without apology. A null result is a legitimate scientific finding. State the outcome factually (e.g., ‘No statistically significant relationship was found between X and Y, r = 0.06, p = 0.51’). You can acknowledge the unexpected nature of the result and note that it will be discussed in the following chapter, but do not attempt to explain it away within the results section.
Is it acceptable to include a figure that I created in Excel or SPSS?
Yes, provided the figure is clearly labeled, captioned, and formatted consistently with the rest of the document. Many examiners accept figures generated by standard statistical or graphing software. However, ensure the figure is of sufficient resolution (300 dpi minimum for print), is legible at normal reading size, and is sourced correctly if it uses data from a published dataset.
Can I refer to the literature in the results chapter?
Generally, no. The results chapter should focus exclusively on your own data. Reference to prior literature belongs in the discussion chapter, where you compare your findings to existing research. A very limited exception applies in some disciplines when a published reference is needed to justify a specific analytical decision (e.g., citing a paper that establishes the threshold used to classify a result), but this should be rare and brief.
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