Introduction to Multilevel Modeling is a two-day (2/26 and 2/28) workshop focused on the application and interpretation of multilevel models, also known as hierarchical linear models and mixed models, for the analysis of nested data structures. Nesting can arise from hierarchical data structures (e.g., siblings nested within family; patients nested within therapist), longitudinal data structures (repeated measures nested within individual), or both (repeated measures nested within patient and patient nested within therapist). It is well known that the analysis of nested data structures using traditional general linear models (e.g., ANOVA or regression) is flawed, oftentimes substantially so: Tests of significance are likely biased and within- and between-group effects are confounded with one another. All of these limitations can be addressed within the multilevel model. In this workshop, we provide an introduction to the application of multilevel models with nested data, including software implementation in SAS, SPSS and Stata.