Analysing your data correctly can make or break your dissertation findings chapter. This step-by-step guide walks through the process from raw data to a clear, defensible set of results.
Step 1: Clean and Organise Your Raw Data
Remove duplicate entries, check for missing values, and standardise formatting before running any analysis. A clean dataset prevents errors from appearing later in your results.
Step 2: Choose the Right Analysis Method
Quantitative data usually calls for statistical tests such as regression or ANOVA, while qualitative data often uses thematic or content analysis. Your method should match your research question, not the other way around.
Step 3: Run the Analysis and Record Everything
Keep a clear record of every test, software version, and setting you use. This makes your methodology reproducible and helps you answer supervisor questions about your process with confidence.
Step 4: Present Findings Clearly
Use tables and charts only where they add clarity, and always explain what each result means in plain language before linking it back to your research question.
Frequently Asked Questions
What software is best for dissertation data analysis?
SPSS and R are common for quantitative statistics, while NVivo is widely used for coding qualitative interview or survey data.
How much data analysis detail should go in the main chapter?
Keep the main chapter focused on key results and their meaning, and move detailed calculations, full tables, or raw outputs to an appendix.
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