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Using Cluster Analysis in Program Evaluation

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  • Laura R. Peck

    (Arizona State University)

Abstract

The conventional way to measure program impacts is to compute the average treatment effect; that is, the difference between a treatment group that received some intervention and a control group that did not. Recently, scholars have recognized that looking only at the average treatment effect may obscure impacts that accrue to subgroups. In an effort to inform subgroup analysis research, this article explains the challenge of treatment group heterogeneity. It then proposes using cluster analysis to identify otherwise difficult-to-identify subgroups within evaluation data. The approach maintains the integrity of the experimental evaluation design, thereby producing unbiased estimates of program impacts by subgroup. This method is applied to data from the evaluation of New York State’s Child Assistance Program, a reform that intended to increase work and earnings among welfare recipients. The article interprets the substantive findings and then addresses the advantages and disadvantages of the proposed method.

Suggested Citation

  • Laura R. Peck, 2005. "Using Cluster Analysis in Program Evaluation," Evaluation Review, , vol. 29(2), pages 178-196, April.
    • Handle: RePEc:sae:evarev:v:29:y:2005:i:2:p:178-196
    DOI: 10.1177/0193841X04266335
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    References listed on IDEAS

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    1. Heckman, James, 2001. "Accounting for Heterogeneity, Diversity and General Equilibrium in Evaluating Social Programmes," Economic Journal, Royal Economic Society, vol. 111(475), pages 654-699, November.
    2. James J. Heckman & Jeffrey Smith & Nancy Clements, 1997. "Making The Most Out Of Programme Evaluations and Social Experiments: Accounting For Heterogeneity in Programme Impacts," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 487-535.
    3. Jennifer Hill & Jane Waldfogel & Jeanne Brooks-Gunn, 2002. "Differential effects of high-quality child care," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 21(4), pages 601-627.
    4. James Heckman & Jeffrey Smith & Christopher Taber, 1998. "Accounting For Dropouts In Evaluations Of Social Programs," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 1-14, February.
    Full references (including those not matched with items on IDEAS)

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