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Comparing Meta-Analytic Moderator Estimation Techniques Under Realistic Conditions

By Piers D. Steel and John D. Kammeyer-mueller


One of the most problematic issues in contemporary meta-analysis is the estimation and interpretation of moderating effects. Monte Carlo analyses are developed in this article that compare bivariate correlations, ordinary least squares and weighted least squares (WLS) multiple regression, and hierarchical subgroup (HS) analysis for assessing the influence of continuous moderators under conditions of multicollinearity and skewed distribution of study sample sizes (heteroscedasticity). The results show that only WLS is largely unaffected by multicollinearity and heteroscedasticity, whereas the other techniques are substantially weakened. Of note, HS, one of the most popular methods, typically provides the most inaccurate results, whereas WLS, one of the least popular methods, typically provides the most accurate results. The use of meta-analysis as a mode for theory testing has grown considerably in recent years, a growth that is verging on maturation. In the beginning, the great challenge for meta-analysis was mere acceptance. There was substantial doubt that such a technique was statistically sound, and consequently many of the initial publications in this area were focused on education as well a

Year: 2013
OAI identifier: oai:CiteSeerX.psu:
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