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Distribution Theory for Glass's Estimator of Effect size and Related Estimators

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  • Larry V. Hedges

Abstract

Glass's estimator of effect size, the sample mean difference divided by the sample standard deviation, is studied in the context of an explicit statistical model. The exact distribution of Glass's estimator is obtained and the estimator is shown to have a small sample bias. The minimum variance unbiased estimator is obtained and shown to have uniformly smaller variance than Glass's (biased) estimator. Measurement error is shown to attenuate estimates of effect size and a correction is given. The effects of measurement invalidity are discussed. Expressions for weights that yield the most precise weighted estimate of effect size are also derived.

Suggested Citation

  • Larry V. Hedges, 1981. "Distribution Theory for Glass's Estimator of Effect size and Related Estimators," Journal of Educational and Behavioral Statistics, , vol. 6(2), pages 107-128, June.
    • Handle: RePEc:sae:jedbes:v:6:y:1981:i:2:p:107-128
    DOI: 10.3102/10769986006002107
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    Cited by:

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    4. Kluve, Jochen & Puerto, Olga Susana & Robalino, David A. & Romero, Jose M. & Rother, Friederike & Stöterau, Jonathan & Weidenkaff, Felix & Witte, Marc J, 2016. "Do Youth Employment Programs Improve Labor Market Outcomes? A Systematic Review," IZA Discussion Papers 10263, Institute of Labor Economics (IZA).
    5. Martin Bøg & Jens Dietrichson & Anna A. Isaksson, 2021. "A multi-sensory tutoring program for students at risk of reading difficulties: Evidence from a randomized field experiment," The Journal of Educational Research, Taylor & Francis Journals, vol. 114(3), pages 233-251, April.
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    10. Marko Hofmann & Silja Meyer-Nieberg, 2018. "Time to dispense with the p-value in OR?," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 193-214, March.
    11. Wildgaard, Lorna, 2016. "A critical cluster analysis of 44 indicators of author-level performance," Journal of Informetrics, Elsevier, vol. 10(4), pages 1055-1078.
    12. Maurizio Canavari & Andreas C. Drichoutis & Jayson L. Lusk & Rodolfo M. Nayga, Jr., 2018. "How to run an experimental auction: A review of recent advances," Working Papers 2018-5, Agricultural University of Athens, Department Of Agricultural Economics.
    13. Josef Frysak & Edward W. N. Bernroider & Konradin Maier, 2017. "An Effort Feedback Perspective on Persuasive Decision Aids for Multi-Attribute Decision-Making," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 161-181, January.
    14. Kluve, Jochen & Puerto, Susana & Robalino, David & Romero, Jose M. & Rother, Friederike & Stöterau, Jonathan & Weidenkaff, Felix & Witte, Marc, 2019. "Do youth employment programs improve labor market outcomes? A quantitative review," World Development, Elsevier, vol. 114(C), pages 237-253.
    15. Daniel Stockemer & Rodrigo Praino, 2015. "Blinded by Beauty? Physical Attractiveness and Candidate Selection in the U.S. House of Representatives," Social Science Quarterly, Southwestern Social Science Association, vol. 96(2), pages 430-443, June.
    16. Brian Goesling & Joanne Lee & Robert G. Wood & Susan Zief, "undated". "Adapting an Evidence-Based Curriculum in a Rural Setting: The Longer-Term Impacts of Reducing the Risk in Kentucky," Mathematica Policy Research Reports 9c41ee6178db492d9c2f3c344, Mathematica Policy Research.
    17. Doll, Monika, 2017. "Relative efficiency of confidence interval methods around effect sizes," FAU Discussion Papers in Economics 22/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.

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