Structural equation modeling (SEM) is one of the most prominent and advanced methods of data analysis in the human and social sciences. This guide provides information on common problems and difficulties encountered when applying this methodology. All explanations are practical and problem-oriented. Formal statistical backgrounds are deliberately described as concisely as possible. The contents of the guide include, among other things, information on the correct determination of measurement models, the selection of appropriate estimation methods, and the interpretation of estimator results. Additionally, tips for various variants of SEM analysis are provided, including longitudinal models, bootstrapping, and Monte Carlo simulations, estimation of latent means, non-recursive models, and multi-group analysis.