The originally proposed multivariate meta-analysis approach for correlation matrices--analyze Pearson correlations, with each study's observed correlations replacing their population counterparts in its conditional-covariance matrix--performs poorly. Two refinements are considered: Analyze Fisher Z-transformed correlations, and substitute better estimates of correlations in the conditional covariances. Fixed-effects methods with and without each refinement were examined in a Monte Carlo study; number of studies and the distribution of within-study sample sizes were varied. Both refinements improved element-wise point and interval estimates, as well as Type I error control for homogeneity tests, especially with many small studies. Practical recommendations and suggestions for future methodological work are offered. An appendix describes how to transform Fisher-Z (co)variances to the Pearson-r metric. (Contains 5 figures and 6 notes.)