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A Bayesian Hierarchical Regression Approach to Longitudinal Data in Empirical Legal Studies

25 Pages Posted: 27 Aug 2009 Last revised: 17 Nov 2009

William Anderson

Cornell University - School of Operations Research and Information Engineering

Martin T. Wells

Cornell University - Law School

Date Written: August 26, 2009

Abstract

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.

Suggested Citation

Anderson, William and Wells, Martin T., A Bayesian Hierarchical Regression Approach to Longitudinal Data in Empirical Legal Studies (August 26, 2009). Available at SSRN: https://ssrn.com/abstract=1462554 or http://dx.doi.org/10.2139/ssrn.1462554

William Anderson (Contact Author)

Cornell University - School of Operations Research and Information Engineering ( email )

414A Rhodes Hall
Ithaca, NY 14850
United States
607-254-4875 (Phone)

HOME PAGE: http://www.orie.cornell.edu

Martin T. Wells

Cornell University - Law School ( email )

Comstock Hall
Ithaca, NY 14853
United States
607-255-8801 (Phone)

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