June 8-12, 2020
Chapel Hill, North Carolina
Instructors: Dan Bauer and Patrick Curran
Software Demonstrations: R, SAS, SPSS, and Stata
Register for the Workshop
*To be eligible, participant must be actively enrolled in a degree-granting graduate or professional school program at the time of the workshop. Post-doctoral fellows are not eligible for the student rate.
Multilevel Modeling is a five-day 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. Not only are tests of significance likely biased, but many important within-group and between-group relations cannot be evaluated. All of these limitations can be addressed within the multilevel model. In this workshop we provide a comprehensive exploration of multilevel models with topics ranging from introductory to advanced, as described below.