Beyond ANOVA: Comparing Strategies for Analyzing (In)dependent-Sample Means in Small Samples

Developmental research often involves comparing cross-sectional or repeatedly measured means in small samples (e.g., N < 100), commonly using ANOVA. Generalized estimating equations (GEE), multilevel modeling (MLM), and structural equation modeling (SEM) are advantageous alternatives, which we didactically explain and demonstrate on N = 42 adolescent boys’ (14–16) alcohol-use data using SPSS and R. To compare their robustness in practice, mixed-design data are simulated under conditions commonly reported in the journal Child Development. Researchers using ANOVA should prefer heteroskedasticity-robust t statistics or CIs to test individual contrasts, whereas MLM and SEM can provide safer omnibus tests, assuming small-sample adjustments are applied to maintain nominal Type I error rates. GEE should be avoided with small samples.