The use of structural equation modeling (SEM) has grown substantially over the past 40 years within organizational research and beyond. There have been many different developments in SEM that make it increasingly useful for a variety of data types, research designs, research questions, and research contexts in the organizational sciences. To give researchers a better understanding of how and why SEM is used, our article (a) presents a review of SEM applications within organizational research; (b) discusses SEM best practices; and (c) explores advanced SEM applications, including instrumental variable methods, latent variable interactions and nonlinear measurement models, multilevel SEM, cross-lagged panel models and dynamic structural equation models, and meta-analytic SEM. We conclude by discussing concerns and debates that are both methodological (i.e., cross-validation and regularization) and theoretical (i.e., understanding causal evidence) as they relate to SEM and its application in organizational research and beyond.