Why Fadeout is (Probably) Worse Than We Think: Adjusting for Correlated Sampling Error in Meta-Analyses of Behavioral Interventions

January 2026

The extent to which intervention effects persist or fade over time is an important question in the behavioral sciences. In meta-analysis, persistence is typically assessed by meta-regressing effect sizes at followup on effect sizes at endline. While common, the standard meta-regression does not adjust for the shared sampling error between effect sizes across time points. We show that in general, estimated slopes from the standard meta-regression are inflated under mild assumptions about correlations between outcomes across time. We show how to adjust for correlated sampling error using a sensitivity analysis approach with meta-analytic data from a series of social-emotional learning interventions. Our results suggest that effect fadeout is likely more severe than current estimates suggest.

Keywords

meta-analysis, meta-regression, persistence, fade out, sensitivity analysis

Education level

K-12 Education

Topics

Methods