This course explains the basic theory of linear and non-linear mixed-effects models, including hierarchical linear models (HLM). A key feature of mixed models is that, by introducing random effects in addition to fixed effects, they allow you to address multiple sources of variation when analyzing correlated data. The course provides a basic understanding and knowledge of mixed-effect models that will enable you to put what you learn into practice. You will use several software programs to fit mixed-effects models to real data sets; outcomes will be presented and discussed.
Dr. James Hardin
Dr. James Hardin is an Associate Dean of Faculty Affairs and Curriculum Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr. Hilbe is the developer of the GLM command.