Meta-Analysis with Heteroscedastic Effects

Abstract

To enhance the utility of meta-analysis as an integrative tool for marketing research, heteroscedastic MLE (HMLE), a maximum-likelihood-based estimation procedure, is proposed as a method that overcomes heteroscedasticity, a problem known to impair OLS estimates and threaten the validity of meta-analytic findings. The results of a Monté Carlo simulation experiment reveal that, under a wide range of heteroscedastic conditions, HMLE is more efficient and powerful than OLS and achieves these performance advantages without inflating type I error. Further, the relative performance of HMLE increases as heteroscedasticity becomes more severe. An empirical analysis of a meta-analytic dataset in marketing confirmed and extended these findings by illustrating how the enhanced efficiency and power of HMLE improve the ability to detect moderator variables and by demonstrating how the theoretical generalizations emerging from a meta-analysis are affected by the choice of the analytic procedure.

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