Bayesian algorithms have been used successfully in the social and behavioral sciences to analyze dichotomous data particularly with complex structural equation models. In this study, we investigate the use of the Polya-Gamma data augmentation method with Gibbs sampling to improve estimation of structural equation models with dichotomous variables. An empirical example is provided to illustrate the performance of different estimation approaches followed by a simulation study to evaluate the proposed method. The Polya-Gamma method is shown to provide stable results with larger effective sample size than standard Gibbs sampling.