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The standardized mean difference (SMD) is perhaps the most important meta-analytic effect size. It is typically used to represent the difference between treatment and control population means in treatment efficacy research. It is also used to represent differences between populations with different characteristics, such as persons who are depressed and those who are not. Measurement error in the independent variable (IV) attenuates SMDs. In this article, we derive a formula for the SMD that explicitly represents accuracy of classification of persons into populations on the basis of scores on an IV. We suggest an alternate version of the SMD less vulnerable to measurement error in the IV. We derive a novel approach to correcting the SMD for measurement error in the IV and show how this method can also be used to reliability correct the unstandardized mean difference. We compare this reliability correction approach with one suggested by Hunter and Schmidt in a series of Monte Carlo simulations. Finally, we consider how the proposed reliability correction method can be used in meta-analysis and suggest future directions for both research and further theoretical development of the proposed reliability correction method.

Development and Monte Carlo Study of a Procedure for Correcting the Standardized Mean Difference for Measurement Error in the Independent Variable