What Is Repeated Measures ANOVA?
Repeated measures ANOVA analyzes data where the same individuals are measured at multiple time points or under multiple conditions. It is appropriate for pre-post-follow-up designs, crossover trials, and within-subjects experimental designs. Because the same participant appears at each time point, observations are correlated — requiring a different analytical approach than between-subjects ANOVA.
The Sphericity Assumption
Repeated measures ANOVA requires sphericity: the variances of the differences between all pairs of time points must be equal. This is tested using Mauchly's Test of Sphericity.
- Mauchly p>0.05 → Sphericity is assumed; report standard F results.
- Mauchly p<0.05 → Sphericity violated; apply Greenhouse-Geisser or Huynh-Feldt correction to adjust degrees of freedom.
Running Repeated Measures ANOVA in SPSS
Go to Analyze → General Linear Model → Repeated Measures.
- Enter the within-subject factor name (e.g., "Time") and specify the number of levels (measurement occasions).
- Map each measurement occasion to the corresponding variable in the Within-Subjects Variables list.
- Options: Descriptive statistics, Estimates of effect size, Observed power.
- Plots: Add the time factor to Horizontal Axis for a visual profile plot.
Interpreting the Output
Based on Mauchly's result, select the appropriate row in the Within-Subjects Effects table. Examine the F statistic and p-value for the time factor. p<0.05 → significant change over time. Follow up with Bonferroni-corrected pairwise comparisons to identify which time-point pairs differ.
APA Reporting Example
Mauchly's test indicated a violation of sphericity (W=0.72, p=.023); therefore, Greenhouse-Geisser corrected degrees of freedom are reported (ε=.81). A significant main effect of time was found, F(1.62, 80.9)=24.3, p<.001, η²p=.33, indicating a significant change in scores across measurement points.