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Modelling impact heterogeneity

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  • Ian Plewis

Abstract

Summary. The treatments embodied in social interventions are characterized by their heterogeneity, delivered as they often are by different individuals operating in different social and geographical contexts. One implication of this heterogeneity is that average treatment effects will often be less useful than estimates of differential impacts across contexts. The paper shows how multilevel models can be used to estimate variability of impact and to account for systematic effects. These models are specified for multisite interventions, for studies using cluster allocation and for designs that incorporate matching. The paper indicates how qualitative and quantitative approaches to evaluation could be linked.

Suggested Citation

  • Ian Plewis, 2002. "Modelling impact heterogeneity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 31-38, February.
    • Handle: RePEc:bla:jorssa:v:165:y:2002:i:1:p:31-38
    DOI: 10.1111/1467-985X.0asp1
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    Cited by:

    1. R. Bellio & E. Gori, 2003. "Impact evaluation of job training programmes: Selection bias in multilevel models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(8), pages 893-907.
    2. James A. Riccio & Howard S. Bloom, 2002. "Extending the reach of randomized social experiments: new directions in evaluations of American welfare‐to‐work and employment initiatives," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 13-30, February.

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