Best Practices for Analyzing Interaction Effects in Stata: A Comparison of Statistical Approaches

When the effect of a treatment variable on an outcome variable differs between two groups, an interaction effect is present. Since this is one of the most common statistical analyses, Stata offers a wide variety of methods to investigate such effects. The present study outlines how these different analyses can be performed in Stata and provides a comprehensive simulation study to determine which method has the best statistical properties. To this end, both the nominal alpha error rate and statistical power are assessed for continuous and binary dependent variables. For a deeper analysis, not only are the sample sizes varied, but other potential challenges are also considered, such as heteroscedasticity for the continuous outcome and imbalanced binary outcome variables. The results indicate that some methods deviate significantly from the nominal alpha limit, leading to incorrect conclusions on average. For the continuous outcome variable, the OLS regression approach with robust (HC3) standard errors and an interaction term yields the best results. For the binary outcome, we recommend the logit model with robust standard errors or the linear probability model with robust (HC3) standard errors.