A Bayesian Hierarchical Regression Approach to Longitudinal Data in Empirical Legal Studies
Cornell University - School of Operations Research and Information Engineering
Martin T. Wells
Cornell University - Law School
August 26, 2009
The various forms of regression are a dominant feature of modern data analysis today. This is hardly surprising since the basic premises of regression are well understood in many different areas of research, and basic regression analysis is a standard component in many statistical software packages. However, researchers do not have to venture very far in their applications of regression analysis to run into trouble from a computational and modeling point of view. This is especially apparent when modeling longitudinal or repeated measures data using classical regression. We introduce a Bayesian hierarchical modeling approach to longitudinal data. These hierarchical models overcome many of the limitations of classical regression and are well suited to handle longitudinal data. The intuitive concepts of hierarchical models are introduced via the Donohue and Levitt (DL) abortion-crime data set, using the statistical software packages R and STATA. We show that when properly modeled, there is no empirical relationship between abortion and crime using the DL data set.
Number of Pages in PDF File: 25
Date posted: August 27, 2009 ; Last revised: November 17, 2009