Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data

Guanglei Hong
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Guanglei Hong: University of ChicagoÂ

Journal of Educational and Behavioral Statistics, 2010, vol. 35, issue 5, 499-531

Abstract: Defining causal effects as comparisons between marginal population means, this article introduces marginal mean weighting through stratification (MMW-S) to adjust for selection bias in multilevel educational data. The article formally shows the inherent connections among the MMW-S method, propensity score stratification, and inverse-probability-of-treatment weighting (IPTW). Both MMW-S and IPTW are suitable for evaluating multiple concurrent treatments, and hence have broader applications than matching, stratification, or covariance adjustment for the propensity score. Furthermore, mathematical consideration and a series of simulations reveal that the MMW-S method has incorporated some important strengths of the propensity score stratification method, which generally enhance the robustness of MMW-S estimates in comparison with IPTW estimates. To illustrate, the author applies the MMW-S method to evaluations of within-class homogeneous grouping in early elementary reading instruction.

Keywords: concurrent treatments; inverse-probability-of-treatment weighting; multivalued treatments; potential outcomes; propensity score stratification (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:35:y:2010:i:5:p:499-531

DOI: 10.3102/1076998609359785

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