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

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  • Guanglei Hong

    (University of ChicagoÂ)

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.

Suggested Citation

  • Guanglei Hong, 2010. "Marginal Mean Weighting Through Stratification: Adjustment for Selection Bias in Multilevel Data," Journal of Educational and Behavioral Statistics, , vol. 35(5), pages 499-531, October.
    • Handle: RePEc:sae:jedbes:v:35:y:2010:i:5:p:499-531
    DOI: 10.3102/1076998609359785
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    Cited by:

    1. Rammer, Christian & Gottschalk, Sandra & Peneder, Michael & Wörter, Martin & Stucki, Tobias & Arvanitis, Spyros, 2017. "Does energy policy hurt international competitiveness of firms? A comparative study for Germany, Switzerland and Austria," Energy Policy, Elsevier, vol. 109(C), pages 154-180.
    2. Martorano, Bruno & Metzger, Laura & Sanfilippo, Marco, 2020. "Chinese development assistance and household welfare in Sub-Saharan Africa," World Development, Elsevier, vol. 129(C).
    3. Gerhard Krug, 2017. "Augmenting propensity score equations to avoid misspecification bias – Evidence from a Monte Carlo simulation [Erweiterung der Propensity Score Gleichung zur Vermeidung von Fehlspezifikationen? Ein," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 205-231, December.
    4. McNamara, Paul E. & Lee, Han Bum, 2017. "Strengthening Nutrition and Improving Livelihoods through Linking Women Farmers to Markets," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258091, Agricultural and Applied Economics Association.
    5. Tenglong Li & Kenneth A. Frank, 2019. "On the probability of a causal inference is robust for internal validity," Papers 1906.08726, arXiv.org.
    6. Spyros Arvanitis & Michael Peneder & Christian Rammer & Tobias Stucki & Martin Wörter, 2016. "How Different Policy Instruments Affect the Creation of Green Energy Innovation: A Differentiated Perspective," KOF Working papers 16-417, KOF Swiss Economic Institute, ETH Zurich.

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