Two new methods for the meta-analysis of factor loadings are introduced and evaluated by Monte Carlo simulations. The direct method pools each factor loading individually, whereas the indirect method synthesizes correlation matrices reproduced from factor loadings. The results of the two simulations demonstrated that the accuracy of meta-analytical derived factor loadings is primarily affected by characteristics of the pooled factor structures (e.g., model error, communality) and to a lesser degree by the sample size of the primary studies and the number of included samples. The choice of the meta-analytical method had a minor impact. In general, the indirect method produced somewhat less biased estimates, particularly for small-sample studies. Thus, the indirect method presents a viable alternative for the meta-analysis of factor structures that could also address moderator hypotheses.