Date(s) - 12/02/2016 - 12/03/2016
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This Statistical Horizons class, Multilevel and Mixed Models, is taught by Stephen Vaisey, Ph.D., and runs from December 2-3 in Raleigh, North Carolina.
Multilevel models are a class of regression models for data that have a hierarchical (or nested) structure. Common examples of such data structures are students nested within schools or classrooms, patients nested within hospitals, or survey respondents nested within countries. Using regression techniques that ignore this hierarchical structure (such as ordinary least squares) can lead to incorrect results because such methods assume that all observations are independent. Perhaps more important, using inappropriate techniques (like pooling or aggregating) prevents researchers from asking substantively interesting questions about how processes work at different levels.
This two-day seminar provides an intensive introduction to multilevel models. After a brief conceptual introduction (including a discussion of the difference between random and fixed effects), we will begin with simple variance components models that can tell us how much of the variation in a measure can be assigned to different levels. We will then move on to mixed models (random effects models with fixed covariates) that allow us to ask how both individual-level and higher-level factors affect an outcome. Next, we will investigate how using random coefficients can help us model how individual-level processes work differently in different social contexts. Finally, we will use the example of hierarchical age-period-cohort models to explore how we can use crossed random effects to model more complicated forms of dependence.
The fee of $995 includes all course materials. The early registration rate of $895 is available until November 2.