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Modeling Heterogeneity in Pooled Event History Analysis

Published online by Cambridge University Press:  25 January 2021

Rebecca J. Kreitzer*
Affiliation:
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Frederick J. Boehmke
Affiliation:
University of Iowa, Iowa City, IA, USA
*
Rebecca J. Kreitzer, University of North Carolina at Chapel Hill, Public Policy Department, Campus Box #3435, UNC-Chapel Hill, NC 27599, USA. Email: rkreit@email.unc.edu

Abstract

Pooled event history analysis (PEHA) allows researchers to study the effects of variables across multiple policies by stacking the data and estimating the parameters in a single model. Yet this approach to modeling policy diffusion implies assumptions about homogeneity that are often violated in reality, such that the effect of a given variable is constant across policies. We relax this assumption and use Monte Carlo simulations to compare common strategies for modeling heterogeneity, testing these strategies with increasing levels of variance. We find that multilevel models with random coefficients produce the best estimates and are a significant improvement over other models. In addition, we show how modeling similar policies as multilevel structures allows researchers to more precisely explore the theoretical implications of heterogeneity across policies. We provide an empirical example of these modeling approaches with a unique data set of 29 antiabortion policies.

Type
Research Article
Copyright
Copyright © The Author(s) 2016

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