Citation

Fisher WH, Hartwell SW, Deng X. Crime Delinq. 2017; 63(1): 77-87.

Copyright

(Copyright © 2017, SAGE Publishing)

DOI

10.1177/0011128716679796

PMID

unavailable

Abstract

Poisson and negative binomial regression procedures have proliferated, and now are available in virtually all statistical packages. Along with the regression procedures themselves are procedures for addressing issues related to the over-dispersion and excessive zeros commonly observed in count data. These approaches, zero-inflated Poisson and zero-inflated negative binomial models, use logit or probit models for the "excess" zeros and count regression models for the counted data. Although these models are often appropriate on statistical grounds, their interpretation may prove substantively difficult. This article explores this dilemma, using data from a study of individuals released from facilities maintained by the Massachusetts Department of Correction.

Keywords count regression models, mental health, offending

Language: en