Nugent William Robert, Moore Matthew, Story Erin
The University of Tennessee, Knoxville, TN, USA.
Educ Psychol Meas. 2015 Apr;75(2):284-310. doi: 10.1177/0013164414534067. Epub 2014 May 22.
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.
标准化均数差值（SMD）可能是最重要的荟萃分析效应量。它通常用于表示治疗效果研究中治疗组与对照组总体均值之间的差异。它还用于表示具有不同特征的人群之间的差异，例如抑郁者与非抑郁者。自变量（IV）中的测量误差会使SMD减小。在本文中，我们推导了一个SMD公式，该公式明确表示了根据IV得分将个体分类到不同总体中的准确性。我们提出了一个不易受IV测量误差影响的SMD替代版本。我们推导了一种针对IV测量误差校正SMD的新方法，并展示了该方法如何也可用于对未标准化均数差值进行信度校正。我们在一系列蒙特卡洛模拟中将这种信度校正方法与亨特和施密特提出的方法进行了比较。最后，我们考虑了所提出的信度校正方法如何用于荟萃分析，并为该信度校正方法的研究和进一步理论发展提出了未来方向。
