SPSS, demystified.
A plain-language SPSS workflow for students: setting up variables, cleaning data, choosing the right test, and interpreting output you can defend.
SPSS frightens more students than statistics itself. The software is only a tool; the fear comes from not having a workflow. Here is the one we teach, from a messy spreadsheet to results you can defend in a viva.
01 Set up your variables properly
Before any analysis, define each variable’s type, measure (nominal, ordinal, scale) and value labels. Ninety percent of SPSS confusion comes from skipping this step.
02 Clean before you analyse
- Check for impossible or out-of-range values
- Decide, and document, how you handle missing data
- Recode and compute new variables you will need
- Screen for outliers that distort your tests
03 Match the test to the question
Comparing two group means points to a t-test; more than two, ANOVA; relationships between scale variables, correlation and regression; associations between categories, chi-square. Choosing the test is a research-design decision, not a menu click.
04 Interpret, do not just report
A p-value is not a conclusion. Report the effect size, explain what it means for your research question, and be ready to defend why you chose that test. That is what separates a pass from a distinction.
05 Check the assumptions first
Every test carries assumptions, and skipping them is where good data produces wrong conclusions. Before a t-test or ANOVA, check that your data is roughly normal and your groups comparable; before regression, check the relationships behave as the model expects. SPSS will happily run a test on data that violates its assumptions and hand you a confident, meaningless number. Checking first is the difference between a result and a mistake.
06 Report it the way examiners expect
A result is only useful if it is communicated in the accepted form. Report the test, the key statistic, the degrees of freedom, the p-value and the effect size, in the conventional order your field uses. Consistent, correctly formatted reporting signals that you know what the numbers mean, not just how to produce them — and it saves you from the questions a sloppy table invites.
- Treating a p-value as proof rather than evidence
- Running the wrong test because it was the one you knew
- Ignoring effect size, so a trivial finding looks important
- Forgetting to document cleaning and recoding decisions
None of these are about the software. They are about understanding, which is exactly what a viva is designed to test.
Independent sources
Every link was checked and live at the time of writing.
IBMIBM SPSS Statistics tutorialIBMSPSS Statistics Brief Guide (PDF)NIH · PubMed CentralSelecting an Appropriate Study DesignLinks open in a new tab. Manara is not affiliated with these publishers.
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