What Dissertation Results Writing Is—and Why It Matters

Dissertation results writing is the focused, objective presentation of findings. It answers “What does the evidence show?” and defers “What does it mean?” to the discussion.

Keeping this boundary sharp makes your chapter readable and examiner-friendly. In practice, dissertation results writing should map one-to-one to your research questions (RQs) or hypotheses (Hs) and show only the evidence required to convince a critical reader.

What belongs in dissertation results writing

• Direct answers to each RQ/H, introduced in the same wording used earlier.
• Evidence: tables, figures, and concise statistics or coded themes.
• Micro-summaries that close subsections without drifting into discussion.
• Signposts to robustness checks and supplementary materials.

What doesn’t belong in dissertation results writing is extended interpretation, speculation about causes, or policy recommendations—save those for the next chapter. The cleaner your separation, the higher your credibility.

A Clear Structure for Dissertation Results Writing

Clarity starts with structure. Organise dissertation results writing around your RQs/Hs. Each subsection should answer one RQ/H with the minimum evidence required.

Suggested outline for dissertation results writing

1. Orientation: one paragraph reminding readers of the RQs/Hs and dataset/sample.
2. RQ/H-by-section: present the primary analysis first, then secondary analyses.
3. Robustness checks: assumptions, sensitivity tests, rival specifications.
4. Micro-summary: a one-line wrap for each subsection.

This approach makes dissertation results writing predictable: examiners can find answers without hunting, and you avoid repetition.

Aligning Results to Research Questions (Templates)

Good dissertation results writing mirrors the exact wording of your RQs/Hs. Here are simple, reusable templates that keep alignment tight.

Template A: RQ-First (Quantitative)

RQ1: Does X predict Y after adjusting for Z?
Answer: Yes—X is positively associated with Y (β=0.43, 95% CI 0.21–0.65, p=.0003; Table 2). The association remains under alternative codings of Z (Appendix D).

Template B: RQ-First (Qualitative)

RQ2: How do participants experience A after intervention B?
Answer: Three themes explain experiences of A following B: “Resetting routines”, “Negotiating trade-offs”, and “Reclaiming agency” (Figure 3). Each theme is evidenced by varied extracts and deviant cases.

Template C: Hypothesis-First

H3: The intervention reduces readmissions within 30 days.
Result: Readmissions are lower in the intervention group (OR=0.71, 95% CI 0.58–0.88, p=.001; Figure 4), robust to alternative specifications (Appendix F).

Use these micro-patterns across your dissertation results writing to keep prose tight and evidence-led.

Dissertation Results Writing for Quantitative Studies

Quantitative dissertation results writing aims for economy: enough statistics to convince, not so many that the signal disappears. Think “title = takeaway; table = detail.”

Descriptive statistics that matter

• Define the analytic sample; state exclusions and missingness.
• Report n, mean (SD) or median (IQR) depending on distribution.
• If groups are compared, show baseline comparability succinctly.

Model reporting that examiners trust

• State the model (e.g., OLS, logistic, Cox) and outcome(s).
• Report coefficients/OR/HR with 95% CIs and exact p values.
• Include effect sizes and a compact model fit indicator (R², AIC/BIC).
• Summarise assumption checks; park full diagnostics in an appendix.

Examples of concise quantitative prose

• Group difference: “Intervention improved adherence by 18% (95% CI 10%–26%, p<.001; Table 3).”
• Regression: “After adjusting for age and baseline severity, X remained positively associated with Y (β=0.43, 95% CI 0.21–0.65, p=.0003; Figure 2).”
• Survival: “Median time-to-event increased by 4.1 months (95% CI 2.2–6.0; HR=0.72, 95% CI 0.60–0.86; Figure 5).”

Special cases

• ANOVA/ANCOVA: Report F, df, p, partial η², and planned contrasts with adjusted CIs.
• Multilevel models: Identify levels, ICC, random effects, and variance explained.
• Prediction models: Report calibration, discrimination (AUC), and internal validation.

Dissertation Results Writing for Qualitative Studies

Qualitative dissertation results writing presents themes credibly and economically. Your reader needs to see how claims are grounded in data without being buried under long quotations.

Theme presentation that earns trust

• Open each theme with a precise, claim-style heading.
• Provide a concise analytic memo (2–4 sentences) explaining the pattern.
• Use 1–3 short, varied quotations as evidence; label with participant type/ID.
• Include deviant cases to sharpen interpretation later.

Worked example (extract)

Theme 2: Negotiating trade-offs becomes easier with peer scaffolding — Participants described early reluctance to prioritise study over work obligations easing once peers modelled boundary setting. Two respondents framed this as “permission” to protect coursework time; a third described “copying their scripts” for difficult conversations (P12, P19, P27).

Audit trails and coding credibility

Mention coding approach (e.g., reflexive thematic analysis, IPA), double-coding or peer debrief processes, and a succinct code tree (as a figure). In dissertation results writing, link claims to extracts; reserve methodology reflections for earlier chapters and extended reflexivity for the discussion.

Dissertation Results Writing for Mixed Methods

Mixed-methods dissertation results writing must show how strands inform each other. Joint displays make integration visible at a glance.

Joint display blueprint

• Design: State whether convergent, explanatory, or exploratory, and note priority.
• Alignment: Place numeric patterns adjacent to quotations or themes.
• Integration labels: Flag convergence, complementarity, or divergence clearly.

End each mixed-methods subsection with a one-line integration statement (e.g., “Survey gains in autonomy are echoed by narratives of ‘permission to prioritise’.”). This is still dissertation results writing—save causal rationales for discussion.

Tables, Figures, and Visuals that Examiners Trust

Visuals are the engine room of dissertation results writing. They speed comprehension and reduce repetition. Follow these rules and your figures will work anywhere.

Five rules for tables

• One idea per table; use a claim-style title (“Intervention reduced readmissions”).
• Order columns and rows logically (size, chronology, or conceptual group).
• Round sensibly; align decimals and include units.
• Note sample size and missingness; footnote tests or adjustments.
• Keep borders minimal; emphasise rows/columns that carry decisions.

Five rules for figures

• Choose the simplest chart that answers the question.
• Label axes directly; avoid cryptic legends and 3D effects.
• Use one highlight colour for the key pattern; keep gridlines light.
• Caption with the takeaway; cite the data source inline.
• Test for readability in grayscale and on small screens.

For accessible presentation styles and chart clarity that adapt well to slides and pages, review the UK Office for National Statistics guidance on charts.

Statistical Significance, Effect Sizes, and Practical Meaning

Examiners look past stars. In dissertation results writing, report what changed and by how much, not just whether p<.05. Pair p values with effect sizes and confidence intervals, and say what the magnitude means.

Reporting set that travels across disciplines

• Exact p (e.g., p=.037), not thresholds.
• Effect sizes (Cohen’s d, r or β_std, OR/HR, partial η², ω²).
• 95% CIs for key estimates and predicted margins where informative.
• Plain-English magnitude (“small but policy-relevant” or “clinically meaningful”).

For succinct guidance adaptable to UK theses, see APA JARS: Statistics. Use judgement—your programme’s preferred style (APA/Harvard/Vancouver) governs exact formatting, but the logic of transparent reporting is universal.

Assumptions, Diagnostics, and Robustness Checks

Transparent dissertation results writing shows that findings do not hinge on fragile assumptions. Summarise diagnostics succinctly and park detail in an appendix.

Typical checks to include

• Linearity & homoscedasticity (plots/tests); note any remedies.
• Normality of residuals (QQ plots) when relevant to inference.
• Influential points (Cook’s distance) and multicollinearity (VIF).
• Sensitivity analyses: rival specifications, sub-samples, alternative codings.
• Robust/clustered SEs or non-parametric alternatives if assumptions fail.

Write one sentence per check and reference a figure/table in the appendix: that’s enough in the main dissertation results writing chapter.

Missing Data, Multiple Testing, and Bias

Nothing erodes trust faster than hidden decisions. In dissertation results writing, state clearly how you handled missingness, controlled Type I error, and mitigated bias.

Missing data

• Report extent and pattern; if tested, state MCAR/MAR/MNAR assumptions.
• Justify method (complete case, MI with m=, chained equations, etc.).
• Provide a sensitivity check comparing main vs. alternative approach.

Multiple testing

• Limit primary tests; pre-label exploratory analyses.
• Consider Holm–Bonferroni or FDR; discuss trade-offs.

Bias

• Selection: recruitment and attrition patterns addressed.
• Measurement: instrument validity/reliability noted.
• Confounding: explicit adjustment sets and rationale.
• Qualitative reflexivity: position and influence statements.

Reporting Standards, Reproducibility, and Transparency

UK examiners increasingly expect transparent dissertation results writing. Borrow best practice from research reporting guidelines—even when not mandated.

Standards you can adapt

• EQUATOR Network (CONSORT, STROBE, PRISMA, SRQR, COREQ): adopt relevant checklist items.
• UK Data Service: data documentation, anonymisation, and sharing guidance.
• Office for National Statistics (ONS) – Charts: clear, accessible visual standards.

Consider an appendix listing the items you followed. This signals mature dissertation results writing and reduces examiner queries.

Discipline Variations in Dissertation Results Writing

While principles are stable, conventions differ. Tailor dissertation results writing to your field’s norms.

STEM

• Concise stats, uncertainty (SE/CI), and instrument details where needed.
• Figures with units, readable error bars, and calibration notes.

Business and management

• Link results to frameworks; foreground effect sizes and managerial significance.
• Use compact model families tables; highlight out-of-sample performance for predictions.

Health and nursing

• Adhere to clinical reporting norms; show outcomes with CIs and number-needed-to-treat where relevant.
• Label subgroup/sensitivity analyses clearly as exploratory.

Social sciences

• Balance quant/qual evidence; foreground validity and reflexivity.
• Report saturation and deviant cases; use joint displays for integration.

Humanities

• Curate excerpts that reveal patterns; use micro-summaries to connect extracts to claims.
• Keep interpretive moves short here; save hermeneutic argument for discussion.

Language, Tone, and Reader-Focused Style

Reader-centred dissertation results writing makes the examiner feel guided, not overwhelmed. Use short sentences, precise verbs, and consistent terminology. Keep interpretations light and local (“suggests”, not “proves”).

Micro-patterns that lift clarity

• Title every table/figure with the takeaway, not just a label.
• Open each subsection with a one-line aim (“This subsection tests H2…”).
• Close each subsection with a one-line micro-summary.
• Prefer decimals to fractions; avoid excessive significant figures.

Transition Sentences and Micro-Summaries

Good transitions stitch dissertation results writing together so readers never feel lost. Copy these structures.

Openers that orient

• “This subsection addresses RQ1 by comparing …”
• “To test H2, we estimate … and report …”

Closers that contain interpretation

• “Micro-summary: Adherence increased by 18% (95% CI 10%–26%); effects persisted in sensitivity tests.”
• “Micro-summary: Participants described three shifts that together explain …; we explore meaning in the discussion.”

Common Mistakes in Dissertation Results Writing—and Fixes

Repeating table cells in prose

Fix: state the key pattern once; let the table carry detail.

Hiding assumptions and exclusions

Fix: add a two-line “Sample and assumptions” note per subsection.

Overloading with secondary analyses

Fix: label exploratory results and move extras to an appendix.

Long quotations without synthesis

Fix: pair 1–3 short, varied extracts with a compact analytic memo.

Unclear figure titles

Fix: convert labels into claim-style sentences that can be skimmed.

Ambiguous RQ/H mapping

Fix: mirror exact RQ/H wording in subheadings and first sentences.

Fast, Practical Workflows (48-Hour and 7-Day Plans)

Even under pressure, you can produce clear dissertation results writing. Use these sprint plans.

48-Hour plan

• Day 1 AM: List RQs/Hs; pair each with its primary evidence; sketch tables/figures.
• Day 1 PM: Draft orientation + first pass subsections; build clean visuals with captions.
• Day 2 AM: Add diagnostics/robustness summaries; cut repetition; refine titles to takeaways.
• Day 2 PM: Run checklist; final formatting; export a results-only PDF for your supervisor.

7-Day plan

• D1: Outline and evidence audit; identify gaps.
• D2: Draft quantitative subsections; tables first, prose second.
• D3: Draft qualitative themes; code tree figure.
• D4: Mixed-methods integration; joint display.
• D5: Diagnostics, sensitivity, missingness, multiple testing notes.
• D6: Language tightening; micro-summaries; figure/table polish.
• D7: Supervisor mini-review and change log; final checks.

Handling Supervisor Feedback with a Change Log

Professional dissertation results writing includes a short change log that turns diffuse comments into decisions.

Change log template

• Theme (e.g., “Assumptions”).
• Feedback (“Check heteroscedasticity”).
• Action (“Breusch–Pagan; robust SE added”).
• Location (“Table 3 note; Appendix B”).
• Status (“Done 20 Sept”).

Attach the log to your email when submitting revised dissertation results writing—it accelerates approvals.

Pre-Submission Checklist for Dissertation Results Writing

• Every RQ/H has a subsection with primary evidence.
• Tables/figures use claim-style titles and sensible rounding with units.
• Key estimates include effect sizes and 95% CIs; model fit noted.
• Assumptions/diagnostics summarised; details signposted to appendix.
• Missing data handling stated; multiple testing addressed.
• Qualitative themes include analytic memos and varied extracts.
• All visuals have sources; abbreviations expanded on first use.
• Language is concise; interpretation reserved for discussion.
• Mixed-methods integration stated explicitly (convergence/compl./divergence).
• Change log prepared and cross-linked to document sections.

What to Move to Appendices (and How to Signpost)

Lean dissertation results writing offloads detail to appendices while keeping the main chapter readable.

Good candidates for appendices

• Full diagnostic plots; alternative model specifications; code snippets.
• Extended tables (variable definitions, correlation matrices, item lists).
• Qualitative codebooks, code trees, and additional extracts.
• Mixed-methods integration details that exceed a single joint display.

Signposting formula

“Diagnostics support model assumptions (Appendix B: Figures B1–B4). Alternative codings yield similar effects (Appendix C: Table C2).” Repeat sparingly but consistently across dissertation results writing.

Mini Exemplars You Can Model (Quant, Qual, Mixed)

Sometimes the quickest way to strengthen dissertation results writing is to mirror a proven pattern. These three compressed exemplars show how to combine claim-style headings, evidence that can be skimmed, and micro-summaries that contain interpretation.

Quantitative exemplar: Claim–Evidence–Assurance

Claim: “Peer-mentoring increased weekly study time by ~1.9 hours.” Evidence: “Difference-in-differences estimate 1.93 hours (95% CI 1.12–2.74, p<.001; Table 2), adjusted for baseline workload and term effects (R²=0.38).” Assurance: “Parallel trends hold in the pre-period; results robust to alternative bandwidths (Appendix D).”

• Use a short table with only necessary covariates; put controls in a footnote.
• Round to two decimals unless precision is decision-critical.
• Include n for each group and note any attrition.

Qualitative exemplar: Theme–Memo–Extracts

Theme: “Accountability nudges reframe procrastination as a social contract.” Memo: “Participants describe deadlines ‘owned by the group’ as easier to honour than private calendars; avoidance declines when check-ins are predictable.” Extracts: “‘I didn’t want to be the one who slipped’ (P07). ‘We borrowed each other’s momentum’ (P15).”

• Label extracts with role/ID only; keep timestamps to appendices if needed.
• Note a deviant case where it clarifies boundary conditions.
• Cross-reference the code tree figure once; don’t reproduce it per theme.

Mixed-methods exemplar: Joint Display–Integration Line

Display: Columns = survey outcomes (autonomy, persistence, help-seeking); Rows = corresponding narrative themes. Populate each cell with the quantitative delta and a one-sentence qualitative gloss. Integration line: “Increases in autonomy (β_std=.29) coincide with narratives of ‘permission to prioritise’, while help-seeking rises modestly and is framed as ‘asking smarter’ rather than asking more.”

Mapping to Marking Rubrics and Viva Advantage

High-scoring dissertation results writing makes a marker’s job easy by matching rubric language. Most UK rubrics reward clarity of presentation, appropriateness of analysis, critical judgement, and professional standards. You can encode these into your chapter so each criterion is visibly met.

Rubric-to-results mapping

• Clarity of presentation: RQ/H-matched subheadings; claim-style figure/table titles; micro-summaries per subsection.
• Appropriateness of analysis: Model choice stated; effect sizes and CIs reported; assumptions and diagnostics summarised.
• Critical judgement: Explicit labels for exploratory analyses; deviant cases in qualitative themes; sensitivity tests signposted.
• Professional standards: Consistent rounding/units; accessible figures; sources and abbreviations defined.

Viva advantage baked into dissertation results writing

Write with your viva in mind. Use anchor phrases you can recall under pressure (“primary estimate… uncertainty… robustness”). When challenged, jump directly to the table or figure whose title already states the answer. This design turns dissertation results writing into a retrieval aid during questioning.

Micro-checklist card for the viva

• Estimate: “The primary effect is …”
• Uncertainty: “95% CI is …; power/sensitivity …”
• Assumptions: “We checked …; departures handled by …”
• Limits: “Scope limits include …; generalisability …”
• Integration (if mixed): “Quant pattern aligns with theme …”

By aligning dissertation results writing to rubrics and viva tactics, you reduce cognitive load for markers and for yourself—often the difference between a good mark and a distinction.

How to Get Expert, Ethical Support (Internal Links)

If you want structured help with dissertation results writing—from building clean tables/figures to effect-size reporting and qualitative theming—work with specialists while keeping authorship and integrity clear.

• See the simple process on How It Works.
• Place a secure request via the Order Form.
• For polishing only, explore Proofreading and Editing.
• Quick answers on timelines, confidentiality, and revisions: FAQs.

Ethics and Integrity Note

We support students by improving clarity, structure, and presentation in dissertation results writing. You remain the researcher and author. We never fabricate data, alter findings, or misrepresent analysis.

Support aligns with UK university policies and research integrity principles (see COPE). Where sensitive data are involved, we follow recognised practices for anonymisation and secure handling (see the UK Data Service), and we encourage adoption of relevant reporting standards (e.g., EQUATOR Network checklists).

FAQs

How long should dissertation results writing be?

Long enough to answer each RQ/H clearly without duplicating tables in prose. Many UK theses allocate 15–25% of the main word count to results—follow your programme’s rubric.

Should I interpret results in the results chapter?

Keep interpretation brief and local (e.g., a one-line takeaway). Reserve extended interpretation, theory links, and implications for the discussion chapter.

What statistics must I include?

Report exact p values, appropriate effect sizes, and 95% confidence intervals. Add model fit and essential diagnostics. The APA JARS pages are a helpful reference for transparent reporting.

How many quotations are enough in qualitative results?

Use the minimum that convincingly evidences each theme—typically 1–3 short, varied extracts per theme—paired with a concise analytic memo and a note on deviant cases.

Do I need a reporting checklist?

Not always required, but adapting relevant EQUATOR Network items (e.g., STROBE/COREQ/SRQR/PRISMA) signals rigour and makes examiners’ jobs easier.

How should I handle missing data?

State extent and pattern; justify your approach (e.g., multiple imputation vs. complete case) and provide a sensitivity analysis. Document decisions in an appendix and summarise briefly in the main dissertation results writing chapter.

Summary

Dissertation results writing earns high marks when it delivers fast, credible answers to your research questions with minimal friction for the reader. The core move is simple: mirror the exact wording of your RQs/Hs in subheadings, present only the evidence needed to convince a critical examiner, and title every table and figure with the main takeaway.

Quantitative results should pair exact p values with effect sizes and 95% confidence intervals, state model fit succinctly, and summarise assumptions and robustness checks without drowning the reader in diagnostics. Qualitative results should open each theme with a precise claim, support it with a tight analytic memo and a handful of short, varied extracts, and acknowledge deviant cases that sharpen the analysis.

In mixed methods, joint displays that align numeric patterns with quotations allow examiners to see convergence, complementarity, or divergence at a glance. Across disciplines, conventions differ—STEM stresses uncertainty and instrument detail; social sciences balance validity, reflexivity, and clear visuals; humanities curate excerpts that illuminate patterns without premature theorising; health fields foreground outcomes, confidence intervals, and sensitivity analyses.

Whatever the field, the same reader-centred habits apply: one idea per table/figure, sensible rounding and units, high-contrast visuals with direct labels, short sentences, and claim-style headings. Address missing data explicitly, mark secondary analyses as exploratory, and manage multiple testing thoughtfully. Keep interpretation light in the results chapter; save extended meaning-making for the discussion.

Reproducibility strengthens trust. Adapting elements of recognised reporting standards from the EQUATOR Network, drawing on APA statistics guidance, and following UK Data Service practices for data documentation and anonymisation will reduce examiner queries and smooth your viva.

A short change log translates supervisor feedback into concrete actions and keeps momentum high. Before submission, run a strict checklist: RQ-aligned sections; essential effect sizes and CIs; assumptions and sensitivity summaries; clear qualitative themes; and clean, well-sourced visuals that stand alone.

If you want structured help without compromising integrity, ethical support focuses on clarity, formatting, and presentation—not fabricating or altering data. For step-by-step assistance with dissertation results writing, review the process on How It Works, submit your brief via the secure Order Form, and consider final polish through Proofreading and Editing.

With an RQ-first structure, evidence-led visuals, and transparent reporting, you will deliver results that examiners can verify quickly and respect immediately—and you set up a stronger, more persuasive discussion chapter.