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On the Hedges Correction for a t-Test

Author

Listed:
  • Nathan M. VanHoudnos

    (Northwestern University)

  • Joel B. Greenhouse

    (Carnegie Mellon University)

Abstract

When cluster randomized experiments are analyzed as if units were independent, test statistics for treatment effects can be anticonservative. Hedges proposed a correction for such tests by scaling them to control their Type I error rate. This article generalizes the Hedges correction from a posttest-only experimental design to more common designs used in practice. We show that for many experimental designs, the generalized correction controls its Type I error while the Hedges correction does not. The generalized correction, however, necessarily has low power due to its control of the Type I error. Our results imply that using the Hedges correction as prescribed, for example, by the What Works Clearinghouse can lead to incorrect inferences and has important implications for evidence-based education.

Suggested Citation

  • Nathan M. VanHoudnos & Joel B. Greenhouse, 2016. "On the Hedges Correction for a t-Test," Journal of Educational and Behavioral Statistics, , vol. 41(4), pages 392-419, August.
    • Handle: RePEc:sae:jedbes:v:41:y:2016:i:4:p:392-419
    DOI: 10.3102/1076998616644990
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    References listed on IDEAS

    as
    1. E. C. Hedberg & Larry V. Hedges, 2014. "Reference Values of Within-District Intraclass Correlations of Academic Achievement by District Characteristics," Evaluation Review, , vol. 38(6), pages 546-582, December.
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