SPSS Analysis, explained clearly
SPSS is powerful but unforgiving. We help you set up your data correctly, run the right procedures, check the assumptions, and read the output so you can explain every table with confidence — in your thesis and in your viva.
SPSS remains the workhorse of medical and health-sciences research, and for good reason: it handles most study designs, produces familiar output, and is what most examiners expect to see. But a valid SPSS analysis needs far more than clicking through menus until a table appears. It needs correctly coded variables, the right procedure for your design, assumptions that have actually been tested, and a correct reading of an output window that is dense with numbers, many of which you do not need. We provide all four.
Just as importantly, we help you understand the workflow. Many clients do not simply want results handed back — they want to be able to reproduce and defend the analysis themselves. We can annotate every step so your SPSS work is transparent and repeatable, which matters both for your viva and for your next study.
What’s included
- Data structuring and coding — defining variable types, value labels, and measurement levels so SPSS treats your data correctly
- Data cleaning — identifying out-of-range values, inconsistent entries, and missing data, with a defensible handling strategy
- Assumption testing — normality (Shapiro–Wilk, Q–Q plots), homogeneity of variance (Levene’s test), and multicollinearity checks
- Descriptive and inferential procedures matched to your design, from cross-tabs to mixed models
- Regression — linear, binary and multinomial logistic, with diagnostics
- Reliability and scale analysis — Cronbach’s alpha, item-total statistics, and exploratory factor analysis
- ROC analysis for diagnostic and cut-off studies
- Publication-ready output — tables and charts exported and formatted to your journal or thesis style
Reading the output correctly
An SPSS output window can run to dozens of tables, and knowing which numbers matter is half the skill. We interpret the ones that count — the test statistic and its exact p-value, the effect size and its confidence interval, and the assumption checks that tell you whether the result can be trusted — and we ignore the noise. You receive a written explanation of each key table so that when your supervisor points at a figure and asks “what does this mean?”, you have a clear answer.
Learn as we go
If you want to build your own SPSS skills, we can work in a teaching mode: screenshots or notes of each step, an explanation of why a particular menu and option were chosen, and a short guide to reproducing the analysis. This is especially valuable for PhD scholars who will defend the analysis in a viva and may need to re-run it after examiner feedback.
Common SPSS pitfalls we prevent
- Coding a categorical variable as a number and analysing it as continuous
- Missing values silently dropping cases and shrinking the effective sample
- Choosing a parametric test when the normality assumption clearly fails
- Reading the wrong row of the output table (for example, ignoring Levene’s test when interpreting a t-test)
- Reporting only a p-value with no effect size, confidence interval, or sample size
Reporting to journal standard
We report results following CONSORT for trials, STROBE for observational studies, or your target journal’s guidelines, with exact statistics rather than vague summaries. Tables are formatted so they can be pasted straight into your manuscript, and figures are exported at the resolution journals require.
How to get started
Share your SPSS file (or your raw data in Excel) along with your research questions and any supervisor requirements. We confirm the design, check the coding and assumptions, run the analysis, and return interpreted, publication-ready output — usually with a short call to walk you through it.