Dissertation Data Analysis: A Practical Guide for UK Students
Written By
Andrew Prescott
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Contents
Data analysis is where raw information becomes findings. It's the bridge between data collection and conclusions. If you collect wonderful data but analyse it poorly, your dissertation suffers.
This section covers both quantitative and qualitative data analysis, helping you understand which approaches suit different research questions and how to execute them well.
Quantitative Data Analysis
Quantitative analysis means converting numerical data into findings. If you've given questionnaires to three hundred participants and entered their responses into a spreadsheet, quantitative analysis is what you do next.
Descriptive statistics. Start here. Describe your data. What's the average? The range? The standard deviation? For social media use, you might report: "Participants reported an average of 3.2 hours daily social media use (SD = 1.8, range = 0.5 to 8.5 hours)." These summary statistics help readers understand your sample.
Inferential statistics. This's where you examine relationships or differences. Correlation examines whether two variables are related (do people who use social media more report higher anxiety?). Regression examines whether one variable predicts another (does social media use predict anxiety after controlling for other factors?). Comparison tests (t-tests, ANOVA) examine whether groups differ (do males and females differ in anxiety levels?).
Which test to use? That depends on your research question and data characteristics. You don't need to memorise statistics. You need to understand your data and know which questions to ask. Consult with your supervisor. Look at similar research to see what tests they used. Read the methods section of relevant papers.
Common mistakes. Using tests inappropriate to your data. Ignoring assumptions (tests work when certain conditions are met). Reporting too many tests without correction (the more tests you run, the more likely you're to find false positives). Being vague about what you found ("We found a relationship" without reporting the actual statistic).
Qualitative Data Analysis
Qualitative analysis means finding meaning in text or interview data. If you've conducted interviews or analysed documents, qualitative analysis is finding themes, patterns, and meaning.
Thematic analysis. This's the most common approach. You read your data (interview transcripts, written responses) and identify themes: patterns, concepts, and meanings that emerge. You code the data (label sections with theme names), then organise codes into broader themes.
Process: Read your data thoroughly. Code segments of text. Group codes into preliminary themes. Refine themes. Check that your themes actually reflect your data.
Content analysis. You count how frequently specific concepts appear. If you're analysing newspaper articles about social media, you might count how many times words like "anxiety," "addiction," or "danger" appear. This quantifies qualitative data.
Interpretative phenomenological analysis (IPA). You're interested in how people experience and make sense of phenomena. You examine how participants talk about social media, what it means to them. This's deeply interpretative.
Grounded theory. You're developing a theory from your data. Rather than testing an existing theory, you're generating one from what you observe.
Common mistakes. Claiming objectivity you don't have (all qualitative analysis involves interpretation). Underreporting the analytical process (examiners want to know how you actually did the analysis). Finding only what you expected to find (be open to surprising findings). Failing to check your codes against the data (does your code actually describe what that participant said?).
Software Tools
Quantitative: SPSS, R, Stata, Python. Most UK universities provide SPSS access. Learn it. It's not complicated and it makes analysis easier.
Qualitative: NVivo, ATLAS.ti, Dedoose. Or spreadsheets. Many researchers do qualitative analysis in Word or Excel. Software helps with organisation but isn't key.
Presenting Your Analysis
For quantitative: Report actual statistics. "We found a considerable positive correlation (r = .38, p < .001)" not just "We found a relationship." Include enough information that readers could find these results in a table if they wanted.
For qualitative: Provide quotes. Show evidence for your themes. If you're claiming a theme exists, demonstrate it with participant quotes.
Common Analysis Mistakes
Fishing for findings. Running many statistical tests until you find something considerable. This inflates false positives.
Inadequate detail. Readers should understand your analytical process. How did you code? How did you decide what constituted a theme?
Mismatching analysis to questions. If your research question asks "what's this like?" qualitative analysis. If it asks "how much?" quantitative analysis.
Ignoring contradictions. If some participants disagreed with your theme, acknowledge it. Qualitative data rarely shows unanimous agreement.
Over-interpreting. Stick to what your data shows. Don't speculate beyond your evidence.
Your introduction plays a important part in setting up the rest of your dissertation, since it is here that you establish the context for your research, explain its significance, and outline the structure of what follows. A common mistake that students make in dissertation introductions is spending too long on background information at the expense of articulating a clear and focused research question that motivates the rest of the study. The introduction should demonstrate that you understand the broader academic and professional context in which your research sits, without becoming so general that it loses sight of the specific contribution your dissertation aims to make. By the end of your introduction, your reader should have a clear sense of what you are investigating, why it matters, how you intend to approach the investigation, and what they can expect to find in each subsequent chapter.
Getting Help
Talk to your supervisor about analysis. Look at similar dissertations to see how they analysed data. Your university likely offers statistical consulting or qualitative research support.
Data analysis is the centrepiece of your dissertation. Get it right and everything else becomes clearer.
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