The data-analysis chapter is where dissertations are won or lost. It carries a large share of the marks because it shows whether you can turn raw information into meaningful, defensible conclusions. This step-by-step guide walks you through analysing data for a UK dissertation.
Step 1: Revisit Your Research Questions
Before touching your data, write your research questions at the top of the page. Every analysis you run must serve one of them.
Step 2: Clean and Prepare Your Data
Quantitative data needs checking for missing values, outliers and coding errors. Qualitative data needs accurate transcription. Document every cleaning decision for a transparent, reproducible process.
Step 3: Choose the Right Method
For quantitative work, match the test to your data: descriptive statistics, t-tests, ANOVA, correlation and regression, using SPSS, R, Stata or EViews. For qualitative work, thematic, content or framework analysis are common.
Step 4: Run, Interpret and Report
Interpret what the numbers or themes mean in plain English. Report statistics correctly and present findings with clear tables and figures.
Step 5: Link Findings Back to the Literature
Connect your results to the studies in your literature review. Do your findings confirm, contradict or extend previous research?
Frequently Asked Questions
Which software should I use for dissertation data analysis?
SPSS and R for quantitative work; NVivo and Atlas.ti for qualitative coding. Choose what your department supports.
What if my results are not what I expected?
Unexpected results are still valid results. Report them honestly and discuss why they might have occurred.
How do I choose between quantitative and qualitative analysis?
Let your research questions decide. If you’re testing relationships or measuring scale across a large sample, go quantitative. If you’re exploring meaning, experience or context in depth with a smaller sample, qualitative analysis fits better. Many dissertations combine both.
Do I need to report a p-value for every test?
Report the p-value alongside effect size and confidence intervals wherever relevant, not in isolation. Examiners increasingly expect effect size because it shows practical significance, not just statistical significance.
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