Fixed effects models help reduce selection bias in studies by using only within-unit variation. However, their substantive importance is often overstated if researchers don't properly consider plausible counterfactuals for the independent variable being studied.
In this article, we replicate several recent fixed effects analyses to demonstrate improved interpretations that better capture real-world applicability and avoid overstatement.
Instead of focusing solely on statistical significance, researchers should:
- Characterize variation carefully
- Present realistic within-unit changes
- Use clear language when discussing findings
We provide a checklist for interpreting these models correctly.
