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Generalizing from unrepresentative experiments: a stratified propensity score approach

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  • Colm O'Muircheartaigh
  • Larry V. Hedges

Abstract

type="main" xml:id="rssc12037-abs-0001"> The paper addresses means of generalizing from an experiment based on a non-probability sample to a population of interest and to subpopulations of interest, where information is available about relevant covariates in the whole population. Using stratification based on propensity score matching with an external populationwide data set, an estimator of the population average treatment effect is constructed. An example is presented in which the applicability of a major education intervention in a non-probability sample of schools in Texas, USA, is assessed for the state as a whole and for its constituent counties. The implications of the results are discussed for two important situations: how to use this methodology to establish where future experiments should be conducted to improve this generalization and how to construct a priori a strategy for experimentation which will maximize both the initial inferential power and the final inferential basis for a series of experiments.

Suggested Citation

  • Colm O'Muircheartaigh & Larry V. Hedges, 2014. "Generalizing from unrepresentative experiments: a stratified propensity score approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(2), pages 195-210, February.
  • Handle: RePEc:bla:jorssc:v:63:y:2014:i:2:p:195-210
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    File URL: http://hdl.handle.net/10.1111/rssc.2014.63.issue-2
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    Cited by:

    1. Michael Gechter & Keisuke Hirano & Jean Lee & Mahreen Mahmud & Orville Mondal & Jonathan Morduch & Saravana Ravindran & Abu S. Shonchoy, 2024. "Selecting Experimental Sites for External Validity," Papers 2405.13241, arXiv.org.
    2. Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl & Bedoya Arguelles,Guadalupe & Bittarello,Luca & Davis,Jonathan Martin Villars & Mittag,Nikolas Karl, 2017. "Distributional impact analysis: toolkit and illustrations of impacts beyond the average treatment effect," Policy Research Working Paper Series 8139, The World Bank.
    3. Elizabeth Tipton & Kelly Hallberg & Larry V. Hedges & Wendy Chan, 2017. "Implications of Small Samples for Generalization: Adjustments and Rules of Thumb," Evaluation Review, , vol. 41(5), pages 472-505, October.
    4. Elizabeth Tipton & Robert B. Olsen, "undated". "Enhancing the Generalizability of Impact Studies in Education," Mathematica Policy Research Reports 35d5625333dc480aba9765b3b, Mathematica Policy Research.
    5. Elizabeth A. Stuart & Anna Rhodes, 2017. "Generalizing Treatment Effect Estimates From Sample to Population: A Case Study in the Difficulties of Finding Sufficient Data," Evaluation Review, , vol. 41(4), pages 357-388, August.
    6. Elizabeth Tipton & Laura R. Peck, 2017. "A Design-Based Approach to Improve External Validity in Welfare Policy Evaluations," Evaluation Review, , vol. 41(4), pages 326-356, August.
    7. Wendy Chan, 2018. "Applications of Small Area Estimation to Generalization With Subclassification by Propensity Scores," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 182-224, April.
    8. Andrew P. Jaciw, 2016. "Assessing the Accuracy of Generalized Inferences From Comparison Group Studies Using a Within-Study Comparison Approach," Evaluation Review, , vol. 40(3), pages 199-240, June.

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