How to Analyse Data for Your Dissertation: A Complete UK Guide

Learning how to analyse data for your dissertation is an essential skill for UK university students. Data analysis turns the information you collected into findings that answer your research questions. Whether your data is numerical or textual, a clear, appropriate analysis is what gives your dissertation credibility. This complete UK guide explains how to analyse quantitative and qualitative data, common tools like SPSS and Excel, and how to present your analysis.

How to analyse data for your dissertation: Step-by-Step Guide

Match Analysis to Your Data

The first rule: your analysis method must fit your data and questions. Quantitative (numerical) data calls for statistical analysis; qualitative (textual) data calls for methods like thematic analysis. Using the wrong method undermines your findings.

For further guidance on how to analyse data for your dissertation, visit the Prospects UK dissertation guide — a trusted resource for UK students and graduates.

Analysing Quantitative Data

Quantitative analysis ranges from descriptive statistics (means, frequencies, percentages) to inferential statistics (tests like t-tests, correlation, regression) that test relationships and significance. Tools such as SPSS, Excel or R handle the calculations.

Analysing Qualitative Data

Qualitative analysis identifies patterns and meanings — commonly through thematic analysis, but also content or framework analysis. The aim is to organise rich data into clear, evidenced themes. See our thematic analysis guide.

Common Tools

✓  SPSS — widely used for statistics.
✓  Excel — good for descriptive stats and charts.
✓  R — powerful statistical computing.
✓  NVivo — for organising qualitative data.

Presenting Your Analysis

Report findings clearly with tables, charts and (for qualitative) representative quotes, and keep results separate from interpretation. The analysis belongs in your results chapter; what it means belongs in the discussion. See our results chapter guide.

Common Mistakes and Tips

✓  Using the wrong test for the data.
✓  Mixing results with interpretation.
✓  Poorly labelled tables.
✓  Over-claiming from limited data. Tip: match the method to your data, present clearly, and interpret separately.

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Quantitative Analysis: Choosing the Right Test

One of the most common challenges in quantitative dissertation analysis is selecting the appropriate statistical test. The decision depends on: your research question (comparing groups vs. measuring relationships vs. predicting outcomes); the level of measurement of your variables (nominal, ordinal, interval, or ratio); whether your data meets the assumptions of parametric tests (normality, homogeneity of variance); and the number of groups or variables involved.

Comparing two groups: Independent samples t-test (parametric) or Mann-Whitney U (non-parametric). Paired samples t-test (same group, two time points) or Wilcoxon signed-rank test.

Comparing three or more groups: One-way ANOVA (parametric) or Kruskal-Wallis H (non-parametric). Post-hoc tests (Tukey, Bonferroni) identify which specific groups differ.

Measuring relationships: Pearson’s r (two continuous variables, parametric) or Spearman’s rho (ordinal or non-parametric). Chi-square test of association (categorical variables).

Predicting outcomes: Simple or multiple linear regression (continuous outcome), logistic regression (binary outcome), or hierarchical regression (controlling for covariates).

Reporting Statistical Results in UK Dissertations

Results must be reported in the format specified by your required style. For APA/psychology: t(df) = value, p = .xxx, d = effect size. For example: t(198) = 3.45, p = .001, d = 0.49. Always report: the test statistic, degrees of freedom (where applicable), p-value, and effect size measure (Cohen’s d, eta-squared, r). Do not just report whether p < .05 — report the exact p-value and the effect size, which tells you the practical as well as statistical significance.

Frequently Asked Questions

How do I analyse data for a dissertation?
Match the method to your data — statistical analysis for numerical data, thematic analysis for textual data.

What is descriptive statistics?
Summary measures such as means, frequencies and percentages.

What is inferential statistics?
Tests such as t-tests, correlation and regression that test relationships and significance.

What tools analyse quantitative data?
SPSS, Excel and R are commonly used.

How do I analyse qualitative data?
Through methods like thematic, content or framework analysis.

What tool helps with qualitative data?
NVivo is widely used to organise qualitative data.

Where does analysis go in a dissertation?
In the results chapter, with interpretation kept for the discussion.

What is a common analysis mistake?
Using the wrong statistical test or over-claiming from limited data.

Related Study Guides

How to Do a Thematic Analysis  •  How to Write a Results Chapter  •  How to Write a Methodology  •  Qualitative vs Quantitative Research

UK students who master how to analyse data for your dissertation gain a significant advantage in their academic career. Whether you are in your first year or final year, understanding how to analyse data for your dissertation thoroughly will improve your overall academic performance and help you achieve better grades.

Quantitative Data Analysis Methods for UK Dissertations

Quantitative data analysis involves applying statistical methods to numerical data to identify patterns, relationships, and differences. The choice of statistical method depends on the type of data you have collected, the scale of measurement (nominal, ordinal, interval, or ratio), the distribution of the data, and the specific research question you are addressing.

Descriptive statistics summarise the characteristics of your dataset. Measures of central tendency (mean, median, mode) describe the typical value; measures of dispersion (standard deviation, range, interquartile range) describe the spread. Frequencies and percentages are used for categorical variables. Descriptive statistics are reported before inferential analyses and provide the reader with an overview of the data before detailed testing begins.

Inferential statistics allow you to draw conclusions from your sample that generalise to the broader population. Common tests include: t-tests (comparing means between two groups), ANOVA (comparing means across three or more groups), chi-square tests (testing associations between categorical variables), Pearson or Spearman correlation (measuring the strength and direction of relationships between variables), and regression analysis (predicting outcomes from one or more predictor variables). The choice between parametric and non-parametric tests depends on whether your data meets the assumptions of normal distribution and equal variance.

SPSS (Statistical Package for the Social Sciences) is the most widely used statistical software in UK social science and health research dissertations. R and Stata are more commonly used in economics, epidemiology, and advanced quantitative research. Many UK universities provide student licences for SPSS through their IT services, and the university library typically offers training workshops and resources for getting started.

Qualitative Data Analysis Methods for UK Dissertations

Qualitative data analysis involves systematically examining non-numerical data—interview transcripts, focus group recordings, observational field notes, documents, or visual materials—to identify patterns, themes, and meanings. Unlike quantitative analysis, qualitative analysis does not follow a single standardised procedure; the choice of analytical approach must be justified in relation to your research question and epistemological position.

Thematic analysis is the most widely used qualitative analytical approach in UK dissertation research. The process involves familiarising yourself with the data, generating initial codes, developing themes from codes, reviewing and refining themes, defining and naming them clearly, and producing a written analysis. Braun and Clarke’s (2006) framework is the most frequently cited approach to thematic analysis in UK social science and health research.

Framework analysis, developed by the National Centre for Social Research, is particularly used in applied policy and health research. It uses a matrix-based approach to systematically organise and interrogate qualitative data against predefined categories derived from the research questions or a theoretical framework.

Grounded theory is a more intensive approach that develops theory inductively from the data through iterative coding and constant comparison. It is less commonly used in taught Master’s dissertations due to its time-intensive nature, but is appropriate for doctoral research seeking to develop original theoretical contributions from qualitative data.

NVivo is the most widely used qualitative data analysis software in UK academic research, allowing researchers to code, organise, and query large volumes of qualitative data systematically. Your university library may offer NVivo training and student licences. If you need expert guidance on your dissertation data analysis, professional research support from qualified methodologists can help you select and apply the most appropriate analytical approach for your data and research question.