How to Fix a Rejected Dissertation Results Chapter: A Dissertation Results Chapter SPSS Guide (Chapter 4)
A rejected dissertation results chapter spss draft is not the end of your degree. In twelve years of doing this, I have seen the same five mistakes repeat themselves over and over. I am not going to give you the textbook version of “how to write a results chapter” that every university library page already gives you. I am going to tell you what your committee is actually scanning for when they read Chapter 4, because that part nobody writes about honestly.
Quick summary, if you only read four lines:
- A dissertation results chapter spss rejection almost always comes down to five fixable mistakes, not a flawed study
- The single most common cause is a missing effect size, not a wrong statistic
- Learning how to write an SPSS results chapter the way committees expect takes less time than most students fear
- This guide gives you the exact spss results chapter structure committees are trained to look for, plus a real rejection-to-acceptance rewrite
What a Rejected Chapter 4 Actually Means
A dissertation results chapter, commonly called Chapter 4, is the section where you report what your statistical analysis found, without interpreting it. It presents descriptive statistics, states whether each hypothesis was supported, and gives the test statistic, degrees of freedom, exact p-value and effect size for every analysis. The discussion chapter, Chapter 5, is where you explain what those findings mean, connect them to your literature review, and argue their significance. Mixing the two is one of the quiet reasons committees push a chapter back, even when every number in it is correct.
Why Chapter 4 Gets Sent Back More Than Any Other Chapter
Chapter 3 is largely a template. You describe what you did. Chapter 4 is where your committee finally checks whether you understood what you did. A study tracking rejected doctorates across three decades found that results and analysis was one of just three areas that consistently sank otherwise sound dissertations, alongside argument precision and methodological transparency.
In my experience, committees read Chapter 4 looking for one thing first: do the numbers match the claims you made in Chapter 3? If they do not, everything after gets questioned.
You Don’t Have to Wait for a Rejection to Get This Checked
Most students only think about getting Chapter 4 reviewed after it comes back with comments. That is the expensive way to do it. A pre-submission check usually takes a fraction of the time a post-rejection fix does, because nothing needs to be unlearned first, just tightened. If your chapter is approved on the first pass, the natural next step is linking your findings cleanly into Chapter 5 and preparing for your defence, which is a different kind of writing problem entirely.
The 5 Reasons Committees Reject a Results Chapter, and the Fix for Each
Most generic chapter 4 results help articles stop at telling you to “format your tables properly” and move on. I disagree with that as a complete fix. A beautifully formatted table built on an untested assumption is still invalid, it just looks more convincing while being wrong. Formatting matters, but only after the statistics underneath it can actually be defended. Here is the mapping I use with every client.
| Rejection Reason | The Fix |
|---|---|
| Missing effect sizes | Add the correct effect size, and a confidence interval where relevant, for every test you report |
| Raw pasted SPSS tables | Convert output into an APA formatted table |
| No assumption statement | State and report the assumption tests you ran |
| Weak narrative | Connect every number back to the research question |
| Wrong test reported | Match the test to your actual data and hypothesis |
Reason 1: Missing Effect Sizes
A p-value only tells your committee whether an effect exists. It says nothing about whether the effect matters, and it says nothing about the range the true effect likely falls in. This is the single most common line I see in rejection feedback: “no effect size reported.”
A t-test needs Cohen’s d. An ANOVA needs partial eta squared. A chi-square needs Cramér’s V. Where your test allows it, add a confidence interval alongside the effect size, since a point estimate on its own invites a committee to ask how precise that estimate really is.
Reason 2: Raw Pasted SPSS Tables Instead of APA Tables
I understand the temptation. You ran the analysis and SPSS gave you a clean looking table, so why retype it? A pasted SPSS output box carries SPSS branding, unnecessary decimal places, and columns your committee does not need.
An APA table has only the statistic, df, p-value and effect size your reader actually uses. This single swap fixes a surprising number of rejections on its own.
Reason 3: No Assumption Statement
Parametric tests like t-tests, ANOVA and regression are only valid when your data meets specific conditions, with normality and homogeneity of variance being the two most checked. For normality, that usually means a Shapiro-Wilk test stated plainly, pass or fail, before you touch your main analysis. For homogeneity of variance across groups, Levene’s test does the same job, and both need to appear in your chapter even when they pass quietly without issue. Skip this and your entire analysis is open to challenge, no matter how clean your results look afterward.
Reason 4: Weak or Missing Narrative
A results chapter that is only tables and numbers with no connecting sentences reads like a data dump, not research. Every statistic needs one sentence tying it back to the specific hypothesis it tests. I tell my students to write it like they are explaining the finding to their advisor out loud, then trim the spoken tone and keep the clarity.
Reason 5: Wrong Statistical Test Reported for the Research Question
This is the one that genuinely worries me when I see it, because it usually means the whole analysis needs to be rerun, not just rewritten. Using an independent t-test on paired pre-post data is a classic example, and the difference between a paired t-test and a repeated measures ANOVA changes which test actually fits your design. The same confusion shows up constantly between Pearson and Spearman correlation. Deciding between Pearson and Spearman correlation depends on whether your data is normally distributed, and that is worth settling before you run anything, not after.
Dissertation Results Chapter SPSS: How to Structure It So Committees Accept First Time
Here is the spss results chapter structure I use with every client, in the order committees expect to read it.
- Brief introduction restating your research questions or hypotheses
- Descriptive statistics for your sample and key variables
- Assumption testing results, stated plainly, pass or fail
- Inferential statistics for each hypothesis, test by test
- A short summary tying results back to each hypothesis
US vs UK Committee Expectations, What Actually Differs
US committees, especially in the social sciences, lean heavily on strict APA 7 formatting and expect effect sizes as a default, not an extra. UK examiners care just as much about the numbers but are often stricter about the narrative thread between results and your literature review. They want to see the connection, not just the table. Know which audience you are writing for before deciding how much prose versus how many tables to include.
APA 7 Formatting Rules for Tables and Statistics
The APA Publication Manual sets a few non-negotiable formatting rules for statistics. Test statistics are italicised, and p-values drop the leading zero, so you write .03 rather than 0.03. SPSS’s p = .000 is never correct. It should always read p < .001, since SPSS only rounds to three decimals.
If your regression analysis involves multiple predictors, check your VIF values for multicollinearity before you finalise any table. A high VIF quietly invalidates a clean looking regression output without ever showing up in the p-value.
Real Case: A Rejected Chapter 4, Before and After
The example below is a composite built from a pattern I see constantly, details changed to protect confidentiality. A UK based MBA candidate came to me after her committee rejected her Chapter 4 with one line of feedback, “insufficient statistical rigour.”
The Original Paragraph
“The ANOVA was run and showed a significant result (p = .000). This means there is a difference between the three groups.”
The Rewrite That Got Accepted
“A one way ANOVA was conducted to compare employee satisfaction across three departments. There was a statistically significant difference between groups, F(2, 87) = 6.42, p < .001, η² = .13, indicating a medium sized effect. Post hoc comparisons using Tukey HSD showed that the Operations group reported significantly lower satisfaction than Marketing (p = .002).”
What Changed and Why It Worked
Three things changed: the degrees of freedom and exact statistic appeared, the effect size was added, and the vague “p = .000” was corrected to proper APA format. Her design had equal variances across groups, which is why Tukey HSD was the right post hoc choice here. If variances are unequal, Games-Howell is the more defensible option, and using Tukey anyway becomes its own quiet rejection risk. Nothing about her actual data changed, only how it was reported, and that is often the entire gap between rejection and acceptance.
Is It Okay to Get Help Fixing Your Results Chapter?
This is the question I get asked in nearly every first consultation, and I respect that it gets asked. It usually comes with two unspoken worries: whether this is a legitimate academic service or a paper mill in disguise, and whether someone working from India can really understand what a US or UK committee expects. Both are fair questions, so I will answer them directly before going further.
A legitimate statistics consultant works only with your existing data and your existing research design. The role is to explain and correct, not to invent a study on your behalf, and that distinction is what separates consulting from a paper mill. As for the country question, APA 7 and UK institutional rubrics are published, documented standards, not local secrets. Reading them correctly has everything to do with having seen hundreds of rejected chapters and nothing to do with which country the person explaining them lives in.
Where the Ethical Line Actually Sits
Getting your results explained, your reporting corrected, and your assumption tests reviewed is not misconduct. It is the same support a writing centre or a statistics consultant at your own university would give. The line is crossed only when someone else runs your actual research design or fabricates findings for you. If you are unsure whether your situation needs outside input at all, this breakdown of when a dissertation expert is actually worth it is a fair place to check before you spend anything.
What a Stats Consultant Should and Shouldn’t Touch
A consultant should check your test selection, your assumption testing, and your APA reporting. A consultant should not be choosing your research questions or writing your discussion chapter’s arguments for you. You remain the author of your dissertation. The help lies in getting the numbers reported the way your committee expects.
Chapter 4 Rejection Proof Checklist
Before you resubmit, run through this list.
- Every test has an effect size attached, with a confidence interval where relevant
- Every table is APA formatted, not a pasted SPSS screenshot
- Assumption testing is stated explicitly, with pass or fail noted for each one
- Every result has one connecting sentence back to its hypothesis
- Every test matches your actual data design and research question
- No p = .000 anywhere, it should read p < .001
FAQ
What’s the difference between the results chapter and the discussion chapter?
The results chapter reports findings only, facts and numbers with no interpretation. The discussion chapter explains what those findings mean and why they matter, and belongs in Chapter 5, not here.
What effect size should I report for my statistical test?
A t-test needs Cohen’s d, an ANOVA needs partial eta squared, a chi-square needs Cramér’s V, and a correlation reports r itself as its own effect size.
What happens if my results don’t support my hypothesis?
A non significant or unexpected result is still a valid finding. Report it honestly in Chapter 4, the explanation for why belongs in your discussion chapter, not here.
How long does it take to fix a rejected Chapter 4, and what does it usually cost?
A reporting and formatting fix, when your actual analysis was correct, usually takes a few days and is priced far lower than a full rewrite. A wrong test error that needs a rerun takes longer and costs more, since it genuinely means redoing the analysis, not just the sentences around it.
Is it against academic integrity to get help with my results chapter?
No, getting your reporting reviewed and your test selection checked is standard academic support. It becomes a problem only if someone else designs your study or invents your findings.
Can results and discussion be combined into one chapter?
Some disciplines and institutions allow a combined results and discussion chapter. Check your own department’s guidelines before deciding, this varies by field.
Why does SPSS show p = .000, and how do I write that in APA?
SPSS rounds p-values to three decimals and cannot show a true zero. Report this as p < .001, never as p = .000.
If you want chapter 4 results help before you resubmit, or you simply want a second pair of eyes before you ever hit the point of rejection, here is how to find the right kind of dissertation mentor for that stage of your work.
Written by Siddharth, a dissertation statistics consultant with 12 years of research guidance experience. Connect on LinkedIn.