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Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods

Author

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  • Peter Z. Schochet

    (Mathematica Policy Research, Inc.)

Abstract

This article discusses estimation of average treatment effects for randomized controlled trials (RCTs) using grouped administrative data to help improve data access. The focus is on design-based estimators, derived using the building blocks of experiments, that are conducive to grouped data for a wide range of RCT designs, including clustered and blocked designs, and models with weights and covariates. Because of the linearity of the regression model underlying RCTs, the asymptotic properties of design-based estimators using group-level averages—formed randomly or by covariates for nonclustered designs and as cluster-level averages for clustered designs—match those using individual data. Furthermore, design effects from aggregation are tolerable with moderate numbers of groups and few covariates, suggesting little information is lost in these cases. Ecological inference methods for subgroup analyses, however, yield large design effects. Several empirical examples using real-world education RCT data demonstrate the theory.

Suggested Citation

  • Peter Z. Schochet, 2020. "Analyzing Grouped Administrative Data for RCTs Using Design-Based Methods," Journal of Educational and Behavioral Statistics, , vol. 45(1), pages 32-57, February.
    • Handle: RePEc:sae:jedbes:v:45:y:2020:i:1:p:32-57
    DOI: 10.3102/1076998619855350
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    References listed on IDEAS

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    Cited by:

    1. Fangzhou Su & Peng Ding, 2021. "Model‐assisted analyses of cluster‐randomized experiments," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 994-1015, November.
    2. Peter Z. Schochet, 2021. "Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Designs with Variation in Treatment Timing," Papers 2102.06770, arXiv.org, revised Oct 2021.

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