I was trying to understand the different use-cases for differences-in-differences models vs. ANCOVA (post-period = pre-period + experiment_group), for observational data. I came across the below article, which starts with:
"When comparing pretest to posttest changes in non-randomized groups, most researchers are correctly avoiding ANCOVA with posttest as the dependent variable and pretest as the covariate. "
Why should that approach be avoided? I have used that approach in the past to control for regression to the mean effect, in models where we are trying to understand which behaviors and covariates are associated with different changes (improvements/worsening) in clinical outcomes.
Also, if anyone has thoughts on the DiD vs. ANCOVA for large samples, I would love to hear!