Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Estimators With Variation in Treatment Timing

Peter Z. Schochet
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Peter Z. Schochet: Mathematica Policy Research

Journal of Educational and Behavioral Statistics, 2022, vol. 47, issue 4, 367-405

Abstract: This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate variation in treatment timing into the analysis. The power formulas also account for other key design features that arise in practice: autocorrelated errors, unequal measurement intervals, and clustering due to the unit of treatment assignment. We consider power formulas for both cross-sectional and longitudinal models and allow for covariates. An illustrative power analysis provides guidance on appropriate sample sizes. The key finding is that accounting for treatment timing increases required sample sizes. Further, DID estimators have considerably more power than standard CITS and ITS estimators. An available Shiny R dashboard performs the sample size calculations for the considered estimators.

Keywords: difference-in-differences designs; interrupted time series designs; statistical power; treatment effect estimation; variation in treatment timing (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:47:y:2022:i:4:p:367-405

DOI: 10.3102/10769986211070625

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