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首页> 外文期刊>Educational and Psychological Measurement >Development and Monte Carlo Study of a Procedure for Correcting the Standardized Mean Difference for Measurement Error in the Independent Variable
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Development and Monte Carlo Study of a Procedure for Correcting the Standardized Mean Difference for Measurement Error in the Independent Variable

机译：校正独立变量中测量误差的标准均值差的程序的开发和蒙特卡洛研究

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摘要

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.
机译：标准化均值差（SMD）可能是最重要的荟萃分析效应量。它通常用于表示治疗功效研究中治疗与对照组人群均值之间的差异。它也用于表示具有不同特征的人群之间的差异，例如抑郁者和非抑郁者。自变量（IV）中的测量误差会使SMD衰减。在本文中，我们导出了SMD的公式，该公式明确表示根据IV得分将人群分类为人群的准确性。我们建议使用SMD的替代版本，该版本不易受IV中测量误差的影响。我们推导了一种新颖的方法来校正IV中测量误差的SMD，并说明如何将该方法用于可靠性校正非标准化均值。我们将这种可靠性校正方法与Hunter和Schmidt在一系列蒙特卡洛模拟中提出的方法进行了比较。最后，我们考虑如何将所提出的可靠性校正方法用于荟萃分析，并为所提出的可靠性校正方法的研究和进一步的理论发展提供未来的方向。

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