The analysis is done. There is a folder of output, a set of tables you cannot bring yourself to look at, and a chapter heading with nothing underneath it. Two weeks ago the problem was getting the data; now the problem is that nobody ever taught you how to turn output into prose — and this is the chapter that, with the discussion, carries roughly 40% of your word count and most of the analytical credit. You can start building the chapter in Tesify tonight, free; first, here is why it will not start.
The reason it will not start
You are trying to write the chapter from your output. A results chapter is written from your research questions.
Statistical software and coding software both produce material in the order the software works, which has nothing to do with the order your argument needs. Open the output folder and you are looking at a list of procedures. Open your methods chapter’s list of research questions and you are looking at a chapter outline — because your results chapter has exactly one job: to answer, in order, each question you said you would answer, with the evidence that answers it.
That single reframe removes most of the paralysis, because it converts an unbounded writing task into a fixed number of small ones.
The cost of getting this chapter wrong
Two failure modes, and both are expensive.
The narrated-output chapter. Every table reproduced, every number restated in a sentence, no signal about which finding matters. It is long, it feels thorough, and it reads to a marker as an inability to distinguish the important from the incidental. It also consumes the words the discussion needed.
The chapter that leaks its discussion. Findings interpreted as they appear, so that by the time you reach the discussion there is nothing left to say and you restate. This is the more common of the two, and the harder to repair late, because unpicking interpretation from reporting means rewriting both chapters.
The proportion matters as well. Results and discussion take roughly 20% each of a typical empirical dissertation, as our breakdown of undergraduate dissertation word counts sets out. A results chapter that has swollen to a third of the dissertation has taken those words from the chapter that earns more of them.
Step 1: Sort the output into three piles
Print or list every piece of output you have and put each item into one of three piles.
- Answers a research question. This is your chapter, and it is usually smaller than you fear — three questions might need five tables.
- Describes the sample. Response rates, demographics, missing data, reliability figures. This is the short opening section, not the body.
- Everything else. Exploratory runs, checks you did and abandoned, alternative specifications. This goes in an appendix, or nowhere.
The third pile is where most over-length results chapters come from, and cutting it is not hiding anything: an analysis you ran, did not preregister and do not report as answering a question belongs in an appendix with a line in the text saying it is there.
Expected output: a numbered list of sections, one per research question, with the specific tables or themes that belong in each.
Step 2: Write the sample section first, because you already know it
It is the easiest thing in the chapter and it breaks the blank page. How many people you approached, how many responded, who they were, what was missing, and any reliability or assumption checks. Facts you already have, in the past tense.
If your achieved sample fell short of your target, this is where you say so and where you carry the consequence forward — the honest construction, including the sensitivity analysis that replaces a post-hoc power calculation, is set out in our guide to sample size for an undergraduate dissertation. Stating the shortfall in the results chapter and revisiting it in the discussion is a mark of competence, not a confession.
Expected output: 300–500 words on the desk, before you have interpreted anything.
Step 3: One sentence per table, then the table
This is the rule that turns output into a chapter. Each table or figure gets one sentence of prose before it that says what it shows, and one sentence after it, at most, that points at the specific value the reader should notice. Nothing else.
The University of Manchester’s Academic Phrasebank describes exactly this convention for quantitative work: results are presented with “tables and figures, and writers comment on the significant data shown in these”, and “more elaborate commentary on the results is normally restricted to the Discussion section”. Its worked phrasings are the ones to imitate — “Table 1 shows an overview of…”, “As can be seen from the table (above), the X group reported significantly more Y than…”, and the summarising move “The most striking result to emerge from the data is that…”.
Two things this rule prevents. It stops you retyping the table into prose, which is the single commonest padding in an undergraduate dissertation. And it stops the table from arriving unannounced, which is what makes a results chapter feel like a report dump.
Expected output: a chapter whose prose can be read on its own and still makes sense, with every table introduced.
Step 4: Report the numbers in the conventional form
Reporting conventions are not decoration; they are how a marker checks your analysis quickly. The Phrasebank’s own example gives the shape: “There was a significant difference in X, t(11) = 2.906, p<0.01” — test statistic, degrees of freedom, value, then significance, in that order and in one bracketed string.
Three habits raise the mark on this alone. Give an effect size alongside every significance test, because a p value tells a reader whether an effect is detectable and not whether it is large. Report exact p values where your handbook allows it rather than only thresholds. And report a non-significant result in exactly the same form as a significant one — “No significant differences were found between…” is a finding, written as a finding. The mechanics for individual tests, including how the output maps to the reported string, are worked through in our guide to running and reporting an independent-samples t-test in SPSS, and what to do when a test’s assumptions are not met is in our guide to failed statistical assumptions.
If your work is qualitative, the equivalent conventions are structural. The Phrasebank describes qualitative results as highlighting themes with supporting excerpts, which in practice means: name the theme, state how it presents across the dataset, then give one or two illustrative extracts with a participant identifier. The full sequence, and where analysis stops being coding, is in our guide to doing a thematic analysis. The one rule that transfers from the quantitative side without modification: an extract is evidence for a claim you have already made in your own words, never a substitute for making it.
Step 5: Hold the line between results and discussion
The test is simple and you can apply it to any sentence you have written. If the sentence could be checked against your data, it belongs in results. If it requires the literature, an explanation, or a judgement about importance, it belongs in the discussion.
“Attendance was lower among students travelling more than 45 minutes” is a result. “This suggests that practical constraints rather than motivation explain disengagement” is a discussion. The second sentence is more interesting, which is exactly why it migrates into the results chapter if you let it.
Some departments explicitly combine the two into a single “findings and discussion” chapter, particularly in qualitative and case-study work. That is a legitimate structure and it is a decision your handbook makes, not you. If it does combine them, the discipline still applies inside each section: report first, interpret second, visibly.
Step 6: Read the chapter backwards against your questions
Take your list of research questions and, for each one, find the sentence in the results chapter that answers it. If you cannot find one, the chapter has reported around the question without landing on it — the commonest reason a marker writes “descriptive” in the margin. Write the missing sentence.
Then check the reverse: every section in the chapter should map to a question. A section that maps to nothing is pile three, and it belongs in the appendix.
Expected output: a one-to-one mapping between questions and answers, which is also the skeleton of your discussion and your conclusion.
Writing it with Tesify, concretely
The mechanical part of this chapter is real and it is where the week goes: holding a fixed structure while you fill it, keeping table numbering and cross-references consistent, and making sure the claims in the chapter still match the abstract and the conclusion when you have finished moving things around.
That is the division of labour Tesify is built for. You give it your research questions and your own findings; it holds the chapter structure, keeps the bibliography built from what your text actually cites, and keeps the document consistent as it grows, so the chapter you finish on Thursday still agrees with the introduction you wrote in March. Every word is written by you — the analysis, the interpretation and the judgement about what matters are the assessed parts and they stay yours.
Open your results chapter in Tesify now. It is free to start, and the first thing it will ask you for is the list you built in Step 1.
Frequently asked questions
How long should a dissertation results chapter be?
About 20% of the total on a conventional empirical dissertation, with the discussion taking a similar share. On a 10,000-word limit that is roughly 2,000 words. Your handbook’s allocation, where it gives one, overrides the convention.
What is the difference between results and discussion?
A results statement can be checked against your data; a discussion statement requires the literature, an explanation or a judgement about importance. The Academic Phrasebank puts the same line as “more elaborate commentary on the results is normally restricted to the Discussion section”.
Should I include every table my software produced?
No. Include the output that answers a research question or describes the sample; put the rest in an appendix with a sentence in the text saying it is there. A results chapter is a selection, and making the selection is part of what is being assessed.
How do I report a non-significant result?
In exactly the same form as a significant one, without apology. “No significant differences were found between…” is a finding. What you must not do is run further tests until something reaches significance, which is an integrity question rather than a writing one.
Do I interpret findings in the results chapter?
Only to the extent of pointing at what the reader should notice. Save explanation and comparison with the literature for the discussion — unless your department requires a combined findings-and-discussion chapter, in which case still report before you interpret within each section.
How do I write up qualitative findings?
Name the theme, state how it presents across your dataset in your own words, then give one or two illustrative extracts with participant identifiers. Extracts support a claim you have made; they do not make it for you.
Is it cheating to use an AI tool to write my results chapter?
It depends entirely on what the tool does and what your department permits. A tool that holds structure, formats citations and checks consistency while you write the words is a different thing from one that generates findings, and only the second misrepresents authorship. Read your assessment brief’s AI statement, disclose what it asks you to disclose, and keep the interpretation yours.
How much does Tesify cost?
It is free to start, which is the tier most students finish a dissertation on. Paid options exist for longer projects; you do not need one to build a results chapter this week.
Is my unpublished data safe in a writing tool?
Your work stays yours, it is not published or shared into any public database, and you can export it. Separately, and regardless of tool: report anonymised or pseudonymised data in your chapter, keep identifiable material out of any document you sync, and follow the conditions of your own ethics approval.
My results are not what I hypothesised. Is that a problem?
No. A well-designed study reporting an unexpected or null result is a pass; the marks are in the design, the analysis and the honesty of the reporting. What costs marks is a discussion that pretends the prediction was confirmed.
Where do tables and figures actually go?
Inline, immediately after the sentence that introduces them, unless your handbook says otherwise. Numbered sequentially, with a caption above tables and below figures at most institutions, and listed on the list of tables or figures in your preliminary pages.