Generalized Linear Mixed Models (GLMM)
June 27-28, 2016
Instructor: Ann A. O’Connell
This short-course (2 days) covers extensions of mixed and hierarchical linear models for outcome variables that represent dichotomous, ordinal, multinomial, or count data. We being with a review of single-level generalized linear models in terms of estimation, interpretation, and model fit. We then expand on this foundation to build and interpret generalized linear mixed models. Emphasis will be on model building, interpretation, comparison, and the use of analysis adjustments for the limited nature of these kinds of dependent variables. Emphasis is on application and interpretation, with hands-on analyses and examples from the education, health, and social/behavioral sciences literature. Participant background should include regression, analysis of variance, and some exposure to multilevel modeling. Software for examples will include HLM, SAS, and SPSS. Be sure to register soon-- the early registration discount ends on May 1st. For more information and to register, go to www.datic.uconn.edu
For more information, contact: Betsy at betsy