You just finished running your statistical analysis in SPSS. Your output shows r = .42, p = .003. It’s statistically significant! A frequent reaction for students writing an SPSS dissertation is to instantly declare that their independent variable definitively causes changes in their dependent variable.
But as I frequently tell my academic consulting clients, navigating correlation vs causation is only the first hurdle in SPSS results interpretation. Here are three deeper errors students commonly make when reading their tables, and how to correct them.
Quick answer
A significant p-value does not prove causation or importance. When reading SPSS output, report the effect size, read Levene’s test correctly (p < .05 means unequal variances) and describe correlations as associations, not causes.
1. Confusing statistical significance with practical importance
The mistake: Seeing p = .001 and assuming you have found a massive, ground-breaking effect.
The reality: A p-value only evaluates the probability of your data assuming the null hypothesis is true; it does not measure the size of your finding.
The fix: Always report the effect size. A tiny p-value paired with a negligible effect magnitude means the association is statistically significant, but practically meaningless in the real world.
2. Misreading Levene’s test
The mistake: Students love small p-values. When running an independent-samples t-test, they spot a Levene’s test significance of .021 and celebrate.
The reality: For Levene’s test, statistical significance means you violated the assumption of equal variances.
The fix: If Levene’s p < .05, you must read the bottom row of your SPSS output (“Equal variances not assumed”) to report the correct adjusted t-statistic.
3. The causation illusion
The mistake: Treating a strong SPSS correlation as definitive proof of a mechanism.
The reality: Correlation simply measures shared variance. An unmeasured confounding variable could easily be driving both metrics.
The fix: State that your data “provides evidence for a positive association,” carefully noting that a significant correlation alone does not establish causality.
Quick reference
| Mistake | What it really means | What to do |
|---|---|---|
| p = .001, so the effect is huge | Only that the result is unlikely under the null hypothesis | Report and interpret the effect size |
| Levene’s p = .021, so it is “significant” | Equal variances assumption is violated | Use the “Equal variances not assumed” row |
| r = .42, so X causes Y | X and Y share variance; a confounder may drive both | Write “association”, not “cause” |
The takeaway
Learning how to interpret SPSS results accurately requires looking beyond the significance column. Proper SPSS output interpretation means evaluating effect sizes, verifying mathematical assumptions, and discussing your findings strictly within the limitations of the analysis. Next, read the 15 most common SPSS mistakes and make sure your dataset is ready with SPSS data cleaning before analysis.
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Get SPSS helpFrequently asked questions
Does a significant correlation prove causation?
No. A significant correlation shows that two variables move together, not that one causes the other. An unmeasured confounding variable could be driving both, so describe the result as evidence of an association.
What does a significant Levene’s test mean in SPSS?
It means the assumption of equal variances has been violated. When Levene’s p is below .05, read the “Equal variances not assumed” row of the t-test output.
Why should I report effect size as well as the p-value?
A p-value shows whether an effect is likely to be due to chance, not how large it is. Effect size tells your reader whether a statistically significant result is also practically meaningful.
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