A Test of Two Refinements in Procedures for Meta-Analysis

نویسندگان

  • Kenneth S. Law
  • Frank L. Schmidt
  • John E. Hunter
چکیده

This study used Monte Carlo simulation to examine the increase in accuracy resulting from 2 statistical refinements of the interactive Schmidt-Hunter procedures for meta-analysis: the use of the mean correlation instead of individual correlations in the estimation of sampling error variance, and a procedure that takes into account the nonlinear nature of the range-restriction correction. In all of the cases examined, these refinements increased the accuracy of the interactive procedure in estimating the variance of population correlations and resulted in more accuracy than other procedures examined. The use of the mean correlation in the sampling error variance formula also increased the accuracy of variance estimates for the multiplicative and Taylor Series procedures.

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تاریخ انتشار 2004