Dissertation Data Analysis
The point of paying for analysis is not to avoid learning statistics. It is to avoid the two expensive failures: running the wrong test, which invalidates a results chapter, and being unable to explain the output in a viva. Everything we return is annotated for that reason. Output you cannot defend is worse than no output.
| Excel | £99 |
|---|---|
| SPSS | £149 |
| R and Stata | £169 each |
| Python | £189 |
| Interpretation only | £99 |
| Turnaround | Three to seven working days |
Which software, and what each covers
Use whatever your department teaches unless you have a reason not to. Examiners are more comfortable with output they recognise, and your supervisor can help you more with software they use.
Excel, £99
Adequate for descriptive statistics, correlation, t-tests, chi-square and simple regression via the Analysis ToolPak. Fine for small undergraduate projects. Its limits appear quickly: no reliable non-parametric suite, awkward ANOVA, and no factor analysis. If your design needs any of those, start elsewhere rather than forcing it.
SPSS, £149
The UK default in psychology, health, education and much of business. Covers t-tests, ANOVA and ANCOVA, chi-square, correlation, multiple and logistic regression, factor analysis, reliability testing with Cronbach’s alpha, and the full non-parametric range. Output is verbose, which is a real advantage in a viva because every assumption test is visible.
R, £169
Free, script-based and reproducible, which increasingly matters for doctoral work. Strongest for mixed effects models, survival analysis, structural equation modelling via lavaan, and publication-quality visualisation with ggplot2. The learning curve is real, so we return commented scripts you can re-run rather than just results.
Stata, £169
Dominant in economics, epidemiology and quantitative social policy. Best-in-class for panel data, instrumental variables, difference-in-differences and survey-weighted analysis. Do-files make the whole analysis auditable, which examiners in these fields expect.
Python, £189
Appropriate when your project involves data at a scale or in a format that statistical packages handle badly: web-scraped data, text corpora, large administrative datasets. pandas and statsmodels for inference, scikit-learn for predictive modelling, delivered as an annotated notebook.
| Service | Price |
|---|---|
| Excel Analysis Descriptives, pivot analysis, correlation and regression via Analysis ToolPak. | £99 |
| SPSS t-tests, ANOVA, chi-square, regression, factor analysis and reliability testing. | £149 |
| R Programming Reproducible scripts, ggplot2 visualisation, mixed models and survival analysis. | £169 |
| Stata Panel data, instrumental variables, difference-in-differences and do-files. | £169 |
| Python pandas and statsmodels pipelines, scikit-learn models, and notebook write-ups. | £189 |
Prices cover cleaning, test selection, execution, assumption testing and annotated output for a standard dissertation dataset. Unusually large or messy datasets are quoted individually rather than surcharged silently.
Choosing the right test
Test selection follows from three things: what you are asking, what type your variables are, and whether your groups are independent or related. It is not a matter of preference, and it is the decision most worth getting right before you collect anything.
| Your question | Test |
|---|---|
| Do two independent groups differ on a continuous measure? | Independent samples t-test, or Mann-Whitney U if non-normal |
| Did the same people change between two time points? | Paired t-test, or Wilcoxon signed-rank |
| Do three or more groups differ? | One-way ANOVA, or Kruskal-Wallis |
| Do two categorical variables relate? | Chi-square test of independence |
| Are two continuous variables associated? | Pearson correlation, or Spearman if non-normal |
| Does a set of predictors explain a continuous outcome? | Multiple linear regression |
| Does a set of predictors explain a yes or no outcome? | Binary logistic regression |
| Do my scale items measure one construct? | Factor analysis plus Cronbach’s alpha |
Assumptions, and what to do when they fail
Every parametric test assumes things about your data, and dissertation datasets frequently violate them. This is normal. What separates a strong results chapter from a weak one is not whether assumptions held, but whether you tested them, reported honestly, and justified your response.
- Normality. Test with Shapiro-Wilk and inspect Q-Q plots. If violated, use the non-parametric equivalent, transform the variable, or bootstrap.
- Homogeneity of variance. Levene’s test. If violated in a t-test, report Welch’s correction, which most software produces automatically.
- Independence of observations. A design issue, not fixable after the fact. Clustered data needs a multilevel model.
- Multicollinearity in regression. Check VIF. Above 10 is a problem; above 5 is worth discussing.
- Linearity and homoscedasticity. Inspect residual plots. Non-linearity may need a transformed or polynomial term.
What you receive
- 1The raw output file from the software used.
- 2A plain-English annotation of every table, explaining what it shows and what it does not.
- 3APA-formatted results tables, ready to paste into your results chapter.
- 4An assumptions note stating what was tested, what held, and what was done about anything that did not.
- 5The syntax, do-file, script or notebook, so the analysis is reproducible and you can re-run it if your data changes.
- 6A short list of the questions an examiner is most likely to ask about this analysis.
Reporting results properly
Most marks lost in a results chapter are lost to reporting convention rather than to the analysis itself. Significance alone is not a finding.
Before:The t-test was significant (p < 0.05), showing that remote workers were more satisfied.
After:Remote workers reported significantly higher job satisfaction (M = 4.21, SD = 0.68) than office-based workers (M = 3.74, SD = 0.71), t(148) = 4.12, p < .001, d = 0.68, 95% CI [0.24, 0.70]. The effect size is moderate, indicating a difference that is practically as well as statistically meaningful.
Report the test statistic, degrees of freedom, exact p-value to three decimals, an effect size, and a confidence interval. Effect sizes in particular are expected at master’s level and above, and their absence is frequently commented on.
Everything we support, with prices
Research Proposal
Aims, questions and a methodology your supervisor will approve.
Literature Review
Thematic synthesis that builds to a defensible gap.
Undergraduate Dissertation
Final-year projects, 8,000 to 15,000 words.
Master's Dissertation
MSc, MA and MBA, 12,000 to 18,000 words.
PhD Research Support
Doctoral chapter review and defence preparation.
Thesis Support
MPhil and PhD thesis structure and analysis.
Data Analysis
SPSS, R, Stata, Python and Excel with annotated output.
Assignment Support
Briefs, rubrics and draft feedback across eleven disciplines.
Proofreading and Editing
Language, register and consistency. Your argument stays yours.
Referencing
Full citation audit in APA, Harvard, MLA, Chicago or OSCOLA.
Turnitin Similarity Review
A similarity report and which matches need re-citing.
Viva Preparation
Mock viva, predicted questions and defence coaching.
Presentation Design
Designed slides built from your dissertation or report.
Frequently asked questions
How much does dissertation data analysis cost?
It depends on the software. Excel analysis is £99, SPSS £149, R and Stata £169 each, and Python £189. If you have already run the analysis and only need the results written up, statistical interpretation is £99, which is the right choice more often than students expect.
Is paying for statistical analysis allowed?
At most UK universities, yes. Many run statistics clinics that do the same work with longer queues, and doctoral schools routinely fund statistical consultancy. The condition is that you understand and can defend the analysis, which is why every piece of work we return is annotated. Check your handbook, and if your department requires you to declare it, declare it.
Which statistical test do I need?
It follows from your research question, your variable types and your design. Comparing two independent group means is a t-test; three or more is ANOVA; two categorical variables is chi-square; predicting a continuous outcome from several variables is multiple regression; a binary outcome is logistic regression. Test selection is part of the service, and getting it wrong is the most expensive error in a quantitative dissertation.
What do I actually receive?
The output file, a plain-English annotation of what each table shows, APA-formatted results tables ready to paste into your dissertation, a note on which assumptions were tested and whether they held, and the syntax or script so the analysis is reproducible and you can re-run it if your data changes.
My data violates the assumptions for the test I planned. What now?
Very common and usually solvable. Options include a non-parametric equivalent such as Mann-Whitney instead of a t-test, transforming the variable, using robust standard errors, or bootstrapping. What matters academically is that you report the violation, state what you did about it, and justify the choice. Concealing it is what damages a viva.
How long does analysis take?
Three to seven working days for most dissertation datasets, depending on how clean the data is and how many tests are involved. Data cleaning is frequently the longest part, particularly with survey exports carrying incomplete responses and inconsistent coding.
Can you help with qualitative analysis too?
Yes. Thematic analysis using Braun and Clarke, framework analysis, and NVivo coding support. Qualitative work is priced per project rather than per software because the volume of data varies so widely, so send us the transcripts and we will quote.
I have output but cannot interpret it. Do I need full analysis?
No, and this is worth reading twice. If the analysis is already run, statistical interpretation at £99 turns your output into defensible prose. Paying for a full re-analysis of work you already have is one of the most common overspends in this market.
Related services and guides
The methodology chapter in the order examiners expect: philosophy, approach, strategy, sampling, collection, a
MSc, MA and MBA dissertation support for 12,000 to 18,000 words, from £899.
Doctoral chapter review, analysis support and viva defence coaching, from £1,599.
Final-year project support for 8,000 to 15,000 words, from £499.
Thematic synthesis and a defensible gap statement, £149.
Mock viva, predicted question list and defence coaching, £149.
How we work, and what stays yours
WritingBays provides research support: structural feedback, editing and proofreading, data analysis and statistical interpretation, referencing correction, reference-only model exemplars, and viva preparation. We do not sell work for submission as your own. Your research question, your argument and your conclusions remain yours, which is what makes the support defensible in a viva and compliant with your institution’s academic conduct policy. If you are unsure what your university permits, check its academic integrity guidance before ordering and we will scope the work to fit.