Managing Inflation: On the Use and Potential Misuse of Zero-Inflated Count Regression Models. (January 2017)

• Record Type: Journal Article
• Authors: Fisher, William H.; Hartwell, Stephanie W.; Deng, Xiaogang
• Is Part Of: Crime and delinquency. Volume 63:Number 1(2017)
• Page Start: 77
• Page End: 87
• Publication Date: 2017-01
• Subjects: count regression models -- mental health -- offending; Crime -- United States -- Periodicals; Juvenile delinquency -- United States -- Periodicals; 364.05
• DOI: 10.1177/0011128716679796
• Languages: English
• ISSNs: 0011-1287

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.