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How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations

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  • Elizabeth Tipton

Abstract

Although a large-scale experiment can provide an estimate of the average causal impact for a program, the sample of sites included in the experiment is often not drawn randomly from the inference population of interest. In this article, we provide a generalizability index that can be used to assess the degree of similarity between the sample of units in an experiment and one or more inference populations on a set of selected covariates. The index takes values between 0 and 1 and indicates both when a sample is like a miniature of the population and how well reweighting methods may perform when differences exist. Results of simulation studies are provided that develop rules of thumb for interpretation as well as an example.

Suggested Citation

  • Elizabeth Tipton, 2014. "How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations," Journal of Educational and Behavioral Statistics, , vol. 39(6), pages 478-501, December.
    • Handle: RePEc:sae:jedbes:v:39:y:2014:i:6:p:478-501
    DOI: 10.3102/1076998614558486
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    References listed on IDEAS

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    1. Robert B. Olsen & Larry L. Orr & Stephen H. Bell & Elizabeth A. Stuart, 2013. "External Validity in Policy Evaluations That Choose Sites Purposively," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 32(1), pages 107-121, January.
    2. Evelyn Kitagawa, 1964. "Standardized comparisons in population research," Demography, Springer;Population Association of America (PAA), vol. 1(1), pages 296-315, March.
    3. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    4. Elizabeth A. Stuart & Stephen R. Cole & Catherine P. Bradshaw & Philip J. Leaf, 2011. "The use of propensity scores to assess the generalizability of results from randomized trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 369-386, April.
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