Meta-analytic structural equation modeling (MASEM) consists of pooling correlation matrices across primary studies and estimating a SEM model using the pooled correlation matrix to examine a theory-driven model. While MASEM combines the advantages of meta-analysis and structural equation modeling, there are some challenges associated with combining correlation estimates across studies (e.g., missing data and within-study dependency) in MASEM. As such, this dissertation addresses which of four multivariate meta-analytic techniques [i.e., multivariate three-level meta-analysis model (MLM), robust variance estimation (RVE), the combination of MLM and RVE, and two-stage SEM (TSSEM)] provided the least bias in the elements of the pooled correlation matrix, with and without missing data. First, an empirical data analysis was conducted to demonstrate the discrepancies among the four synthesis methods in terms of pooled correlation estimates, their standard error (SE) estimates, and their 95% confidence interval estimates. Subsequently, a simulation study was conducted to examine the optimal synthesis method among the four methods for combining correlation estimates across studies in terms of the parameter and SE recovery and the parameter coverage rates. The number of studies, the within-study sample size, the degree of missingness, and the value of the ratio of sampling error: within-study: between-studies were manipulated in the simulation study. The accuracy of pooled correlation estimates was compared across the four synthesis methods in terms of relative parameter bias, relative SE bias, and coverage rates. The findings from this study indicate that the combination of the multivariate three-level meta-analysis model and the robust variance estimation is the optimal method for aggregating correlation estimates across studies under the simulation conditions considered in this dissertation. The implications for applied MASEM researchers, as well as, directions for future methodological research are discussed in this dissertation