SPSS & data analysis chapter
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Data and Statistical Analysis Help for UK Coursework
Running the test is the easy part. The marks are in choosing the right one, checking its assumptions, and explaining what the output means in words a marker will credit.
- SPSS, R, Stata, Excel, Python and NVivo
- Assumption testing done and reported, not skipped
- Output interpreted in plain English, APA-formatted
- Annotated syntax so you can rerun and defend it
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What this service covers
Descriptive statisticsT-tests & ANOVARegression modellingChi-square & non-parametricFactor analysisThematic analysisData cleaning
Choosing the right test
Choosing a test comes down to three things. How many groups or variables you have. What type of data they are. And whether your data meet the parametric assumptions. Students who pick a test because it came up in the lecture usually pick the wrong one, and clean output cannot rescue the wrong test.
| Your question | Parametric test | Non-parametric alternative |
|---|---|---|
| Two independent groups differ? | Independent-samples t-test | Mann-Whitney U |
| Same group, before and after? | Paired-samples t-test | Wilcoxon signed-rank |
| Three or more groups differ? | One-way ANOVA | Kruskal-Wallis H |
| Two categorical variables related? | — | Chi-square test of independence |
| Strength of a relationship? | Pearson correlation | Spearman's rho |
| Predicting a continuous outcome? | Multiple linear regression | Robust or quantile regression |
| Predicting a binary outcome? | Binary logistic regression | — |
| Reducing many items to factors? | EFA / PCA | — |
Assumptions — the section markers look for first
Reporting a t-test without evidence that you checked its assumptions is the fastest way to lose the analysis marks. We test them, report them properly, and if an assumption fails we say so and switch to the appropriate alternative rather than quietly proceeding.
- Normality — Shapiro-Wilk for smaller samples, plus Q-Q plots, skewness and kurtosis. On a large sample, a significant Shapiro-Wilk means very little on its own.
- Homogeneity of variance — Levene’s test, with the Welch correction applied where it is violated.
- Independence of observations — a design question, not a test, and one we will flag if your design breaches it.
- Linearity and homoscedasticity for regression — scatterplots and residual plots included in the appendix.
- Multicollinearity — VIF and tolerance reported for every predictor.
- Outliers — identified, examined and a stated decision made about retention or removal, with justification.
Qualitative analysis
Qualitative work has its own rigour, and “themes emerged from the data” is not evidence of it. We use Braun and Clarke’s six-phase thematic analysis by default. It is the most widely accepted framework in UK social science, health and education, and it leaves an auditable trail.
- Familiarisation with the transcripts, with notes recorded.
- Systematic initial coding across the whole dataset.
- Candidate themes constructed from the codes.
- Themes reviewed against coded extracts and the full dataset.
- Themes defined and named, each with a clear boundary.
- Write-up with illustrative extracts attributed to anonymised participants.
You get the NVivo project file or a coding framework in Excel, a codebook, the theme hierarchy, and a reflexivity statement. UK examiners increasingly expect that last one, and students routinely leave it out.
What you receive, and why the syntax matters
Every quantitative order comes with annotated syntax — an SPSS .sps file, an R script or a Stata do-file. This is not a nicety. It means you can rerun the whole analysis yourself. It means your supervisor can check it. And if a viva panel asks how you got a figure, you can show them.
Why students pick us for this.
SPSS, R, Stata, Excel, Python and NVivo.
You see the price before you commit. Every order comes with a free similarity report, and we rework it free as many times as you need.
What you receive
Every order comes back with all of this.
- Cleaned and coded dataset
- Annotated syntax file (SPSS, R or Stata)
- APA-formatted results tables
- Written interpretation of every output
- Assumption testing appendix with plots
Not sure this is the right service?
Send us your brief and we will tell you which service you need. Often it is a cheaper one than you expected — we will say so.
Ask a specialistData & statistical analysis — your questions
Which software do you use?
SPSS, R, Stata, Excel, Python and NVivo. Tell us what your department has licensed, since deliverables should be openable on your own machine.
I have not collected my data yet. Can you still help?
Yes, and this is the better time to talk to us. Sample size calculation, questionnaire design and scale selection before collection prevent the problems that cannot be fixed afterwards.
Will you explain the analysis so I can defend it?
That is the point of the annotated syntax and the written interpretation. Students facing a viva can also book a walkthrough call to rehearse the questions examiners actually ask.
Can you analyse data for a dissertation chapter?
Yes — our dissertation SPSS page covers the full findings-and-analysis chapter, which is a larger piece of work than a standalone coursework analysis.
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