Applied Multilevel Models for Cross-Sectional Data (Boulder, CO)
Multilevel models are powerful statistical models that partition multiple sources of variation that may be present due to dependencies in data. Also known as hierarchical linear models or mixed effects models, multilevel models extend traditional linear models (such as regression or analysis of variance) to analyses where data structures are clustered, nested, or hierarchical in nature. This workshop presents an introduction to multilevel models featuring their use in cross-sectional analyses. By attending the workshop, participants will gain an understanding of the multilevel modeling approach and will be able to evaluate and conduct basic multilevel model analyses.
This workshop will span topics in an integrated framework, with the first day being a review of general linear models beginning with unconditional models and the rules of model comparisons. The second day will feature two-level models: adding random components and adding single predictors, including a discussion of predictor centering techniques. The third and fourth day will be spent on multilevel models with multiple predictors and models with three or more levels. The final day will be spent discussing advanced topics: multilevel models with multivariate predictors and crossed random effects models.
Software: The primary software package used for instruction will be SAS, but some reference examples using SPSS, Mplus, and R will be provided. The course will also include daily opportunities for hands-on practice and individual consultation. Participants should be familiar with ANOVA and regression, but no prior experience with multilevel models or knowledge of advanced mathematics is assumed.
Fee: Members = $1500; Non-members = $3000