Crossing Borders: Merging multilevel and structural equation modeling Joop Hox Utrecht University, the Netherlands Structural equation modeling (SEM) is a very general analysis technique, which is widely used in the social and behavioral sciences. It is a combination of factor analysis and regression analysis, and includes as special cases several traditional multivariate techniques, such factor analysis, regression analysis, Manova, and others. The interest in SEM is often on theoretical constructs that are represented by latent factors. The relationships between the theoretical constructs are represented by regression or path coefficients between the factors. Multilevel Modeling (MLM) is an analysis technique developed for hierarchically nested data. Examples are pupils who are nested within classes and schools, family members who are nested in families, and residents who are nested within neighborhoods. Researchers are often interested in the question how individual characteristics and characteristics of the context (class, family, neighborhood) interact and influence important individual outcome variables. Multilevel regression analysis offers a tool to analyze hierarchical data and provide answers to this question. An ongoing development is that multilevel models and structural equation models are merging. Theoretical work has shown that from an abstract statistical view multilevel models can be looked at as a special structural equation model. In addition, most important SEM software now allows for multilevel structures. As a result, we can now analyze multilevel factor models and multilevel path models, for example multilevel models for mediation effects.