This workshop introduces hierarchical/multilevel regression-style linear models (and a few basic nonlinear forms) in a manner accessible to social and behavioral scientists. These models account for levels of aggregation that are typical in social science data in which individuals are nested in groups, and possibly multiple groups. Since these specifications are inherently Bayesian in nature this workshop will also introduce the basic principles of Bayesian statistics to students in the social and behavioral sciences without requiring an extensive background in mathematical statistics. Most of the examples will be drawn from sociology, political science, economics, marketing, psychology, public policy, and anthropology.
The prerequisites for this workshop are a linear regression course and knowledge of matrix algebra. The emphasis will be on applying the principles to actual data-analytic problems of interest to participants rather than through textbook examples. The workshop will make extensive use of software that is in the public domain, yet is high in quality (including R and JAGS).