Dissertation Data Analysis: SPSS, NVivo & Excel

Dissertation Data Analysis: SPSS, NVivo & Excel Guide (2026)

Dissertation data analysis UK students undertake is often the most technically demanding phase of their university research. Whether you are analysing quantitative survey data, qualitative interview transcripts, or mixed-methods datasets, choosing the right software tools and applying them correctly is essential to the validity and credibility of your findings. This guide explains how to use SPSS, NVivo, and Excel for dissertation data analysis and how to present your results effectively.

Quantitative Analysis: SPSS

SPSS (Statistical Package for the Social Sciences, now IBM SPSS Statistics) is the most widely used statistical software in UK social science, health, and business dissertation research. It is available free or at reduced cost to students at most UK universities through the campus software licence. SPSS allows you to conduct descriptive statistics, inferential tests, and multivariate analyses without needing to write code.

Commonly Used SPSS Tests

• Descriptive statistics: Frequencies, means, standard deviations — use to summarise your sample and variables before running inferential tests.
• Independent samples t-test: Tests whether the means of two independent groups differ significantly on a continuous variable (e.g., comparing stress scores between male and female students).
• One-way ANOVA: Tests differences in means across three or more groups (e.g., comparing anxiety scores across three different teaching methods).
• Pearson’s r correlation: Measures the strength and direction of the relationship between two continuous variables.
• Multiple linear regression: Examines whether one or more predictor variables explain variance in a continuous outcome variable.
• Chi-square test: Tests the association between two categorical variables.

Before running any inferential test, check that your data meets the test’s assumptions: normality (Shapiro-Wilk or Kolmogorov-Smirnov), homogeneity of variances (Levene’s test), and absence of significant outliers. Report assumption checking in your methodology chapter.

Qualitative Analysis: NVivo

NVivo is the most widely used qualitative data analysis software in UK academic research. It allows you to import, organise, code, and query qualitative data — including interview transcripts, focus group recordings, documents, social media data, and survey free-text responses. NVivo does not do the analysis for you; it provides a structured environment in which to apply your analytical approach (typically thematic analysis, grounded theory, framework analysis, or IPA).

Using NVivo for Thematic Analysis

The most commonly used qualitative analysis approach in UK dissertations is thematic analysis (Braun and Clarke, 2006). In NVivo, the process typically follows these steps: (1) import transcripts and read each one in full; (2) create initial codes (NVivo “nodes”) — brief labels for segments of data that capture something analytically interesting; (3) organise codes into potential themes using NVivo’s node structure; (4) review and refine themes — checking that each theme is coherent and supported by sufficient data; (5) define and name themes; (6) produce extracts from the data (direct quotes) to illustrate each theme in your findings chapter.

NVivo also allows you to run queries — such as word frequency queries, matrix coding queries (to explore relationships between codes), and compound queries — that can support your analytical process. However, these tools supplement, not replace, the interpretive analytical work that must be done by the researcher.

Quantitative Analysis: Excel

Microsoft Excel is widely available and can perform basic to intermediate statistical analysis suitable for many undergraduate and some postgraduate dissertations. Excel is particularly useful for: descriptive statistics (mean, median, mode, standard deviation, range); frequency distributions and histograms; scatter plots and trend line analysis; Pearson’s correlation (using the CORREL function); and basic t-tests and F-tests (using the Analysis ToolPak add-in).

Excel is not suitable for advanced inferential statistics (ANOVA, regression with multiple predictors, factor analysis, structural equation modelling) — use SPSS, R, or Stata for these. However, for straightforward quantitative analyses in small-sample undergraduate dissertations, Excel is a perfectly acceptable tool, provided you report your analysis process transparently in the methodology chapter.

Presenting Data Analysis Results in Your Dissertation

Quantitative results are typically presented in the Findings chapter using tables and figures. Key rules: every table and figure must be numbered, titled, and captioned; all abbreviations and units must be defined; statistical values must include appropriate indicators (mean, SD, t-value, p-value, effect size such as Cohen’s d or eta-squared); and every table and figure must be referred to and discussed in the main text. Do not present raw SPSS output — reformat tables to professional standards before including them in your dissertation.

Qualitative findings are presented using direct quotations from participants, identified only by a code (e.g., Participant 3, or P3) to preserve anonymity, alongside your analytical commentary interpreting what each quotation illustrates. Balance breadth (showing the range of themes) with depth (demonstrating that each theme is well-supported by multiple data points across your dataset).

Frequently Asked Questions

Which software should I use for my dissertation data analysis?

The choice depends on your research design and data type. For quantitative data with inferential statistics, use SPSS (or R if you are comfortable with programming). For qualitative data from interviews or focus groups, use NVivo (or Atlas.ti as an alternative). For basic descriptive statistics and charts, Excel is often sufficient at undergraduate level. If your university provides free access to SPSS and NVivo (most UK universities do), learn to use them — they are industry-standard tools that will also be useful in professional contexts after graduation.

Do I need to show my SPSS or NVivo output in my dissertation?

Raw software output (SPSS output tables, NVivo node structures, screenshots) is typically included in the appendices, not the main body of the dissertation. The main body should present clean, professionally formatted tables and figures with your interpretive commentary. In the methodology chapter, state which software you used and how — for example, “Thematic analysis was conducted using NVivo 14 (QSR International)” or “Statistical analyses were performed using IBM SPSS Statistics version 29.”

Related Study Guides

• Dissertation data analysis: SPSS, NVivo & Excel (full guide)
• Dissertation methodology guide
• Primary vs secondary research
• Five major parts of a dissertation methodology

Interpreting and Presenting Your Analysis Results

Collecting and analysing data is only part of the challenge in dissertation research; presenting your analysis clearly and interpreting it accurately are equally important and are often where students struggle most. For quantitative data analysed in SPSS or Excel, the presentation of results typically involves tables and figures that summarise key statistical outputs — means, standard deviations, regression coefficients, confidence intervals — alongside narrative interpretation that explains what these numbers mean in relation to your research question. A common error is presenting tables without sufficient narrative commentary: examiners expect you to tell them what the data shows, not simply display it.

For qualitative data analysed in NVivo or through manual thematic analysis, the presentation of findings typically involves selecting representative quotations or excerpts that illustrate each identified theme, accompanied by analytical commentary that explains how the evidence supports your interpretation. The selection of quotations requires care: choose passages that are vivid, specific, and directly relevant to the theme rather than generic statements that could apply to almost any participant. A well-selected quotation does much of the analytical work for you by making the evidence visible and compelling.

In both quantitative and qualitative dissertations, it is important to distinguish clearly between reporting results and discussing their implications. The results chapter presents what you found; the discussion chapter interprets what it means, how it relates to the existing literature, and what theoretical or practical implications it carries. Many students conflate these two functions, mixing interpretation into their results chapter and then finding they have little left to say in the discussion. Maintaining this distinction will produce a more coherent and professionally structured dissertation.

Getting Expert Help with Dissertation Data Analysis

Data analysis is one of the most technically demanding aspects of dissertation writing, and it is also an area where many students genuinely benefit from expert consultation. Whether you are struggling with statistical analysis in SPSS, unsure how to approach qualitative coding in NVivo, or finding it difficult to interpret your results in relation to your research questions, professional support is available and widely used by UK students at undergraduate, master’s, and doctoral level.

Reputable UK academic support services employ qualified researchers with specific expertise in data analysis methods across a range of disciplines. A statistical consultant can review your analysis design, check your calculations for errors, advise on the appropriate tests for your data, and help you interpret outputs accurately. A qualitative research specialist can review your coding framework, advise on analytical approaches appropriate to your methodology, and provide feedback on whether your thematic categories adequately capture the complexity of your data.

When seeking data analysis support, look for providers who ask detailed questions about your research design, data collection methods, and analytical goals rather than offering generic advice. The quality of methodological consultation depends heavily on the consultant’s familiarity with your specific research context. Providers who have genuine subject expertise in your field — rather than generic statistical knowledge — will provide more relevant and actionable guidance that directly improves your dissertation’s analytical rigour.

Choosing the Right Software for Your Dissertation Data Analysis

One of the first decisions dissertation students face once their data is collected is which software to use for analysis. This decision should be driven by the nature of your data and the type of analysis you need to perform, not by familiarity or convenience alone.

SPSS (Statistical Package for the Social Sciences) remains the dominant quantitative analysis tool in UK social science, education, health, and business research at the dissertation level. Its graphical interface makes it more accessible than command-line tools such as R or Stata, while its analytical capabilities are comprehensive: it handles descriptive statistics, cross-tabulations, t-tests, ANOVA, regression analysis, factor analysis, and many more procedures. Most UK universities provide SPSS licences to students, either through on-campus computer labs or remote access, and your library or methods support team can advise on how to access it.

NVivo is the leading qualitative data analysis software used in UK universities for managing and analysing interview transcripts, focus group data, documents, and other textual or multimedia sources. It allows you to organise your data into projects, create a coding framework by applying codes to segments of text or other content, visualise relationships between codes, and run queries across your dataset. While NVivo significantly improves the transparency and organisation of qualitative analysis, it is important to remember that the analytical thinking — the judgement about what each code means and how themes relate to each other — remains the researcher’s intellectual contribution. NVivo manages data; it does not produce analysis.

Microsoft Excel is appropriate for basic quantitative analysis — calculating means, medians, standard deviations, and producing charts — and for data management tasks such as cleaning and organising survey data before it is imported into SPSS or R. Excel is not suitable for complex inferential statistics, as its statistical functions are limited and less reliable than dedicated statistical packages. However, for straightforward descriptive analysis and data visualisation, it is a practical and universally available tool that most students already know how to use.

Whichever software you use, document your analytical process carefully. Keep a record of all analyses you run, including those that produce non-significant or unexpected results. Methodological transparency — including negative or null findings — is a sign of academic rigour, not failure, and your methodology chapter should include a clear description of the specific analyses you conducted and the procedures you followed, so that your work could in principle be replicated by another researcher.