Applied Multilevel Models for Longitudinal and Clustered Data (Boulder, CO)

Instructor(s):

Multilevel models are known by many synonyms (i.e., hierarchical linear models, general linear mixed models). The defining feature of these models is their capacity to provide quantification and prediction of random variance due to multiple sampling dimensions (across occasions, persons, or groups). Multilevel models offer many advantages for analyzing longitudinal data, such as flexible strategies for modeling change and individual differences in change, the examination of time-invariant or time-varying predictor effects, and the use of all available complete observations. Multilevel models are also useful in analyzing clustered data (e.g., persons nested in groups), in which one wishes to examine predictors pertaining to individuals or to groups. This workshop will serve as an applied introduction to multilevel models, beginning with longitudinal data and continuing onto clustered data.

The first day will be spent reviewing general linear models and then introducing the multilevel model. The second day will be spent fitting unconditional longitudinal models and on the rules of model comparisons. The third day will be spent on two-level conditional (predictor) models for longitudinal data. The fourth day will be spent examining two-level conditional models for clustered data. The fifth day will be spent on three-level models for clustered longitudinal data or other special topics. The primary software package utilized for instruction will be SAS, but examples using SPSS and STATA will also be provided. The course will also include daily opportunities for hands-on practice in which participants may use any of these programs (SAS, SPSS, or STATA). Participants should be familiar with the general linear model (e.g., ANOVA and regression), but no prior experience with multilevel models or knowledge of advanced mathematics (e.g., matrix algebra) is assumed.

Fee: Members = $1500; Non-members = $3000

Tags: multilevel models, mixed models, hierarchical linear models

Course Sections

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