The Analysis of Bounded Count Data in Criminology

Publication Title

Journal of Quantitative Criminology

Document Type

Article

Department or Program

Sociology

Publication Date

6-2018

Keywords

Binomial regression, Count data, Negative binomial regression, Poisson regression, Variety scores

Abstract

Background: Criminological research utilizes several types of delinquency scales, including frequency counts and, increasingly, variety scores. The latter counts the number of distinct types of crimes an individual has committed. Often, variety scores are modeled via count regression techniques (e.g., Poisson, negative binomial), which are best suited to the analysis of unbounded count data. Variety scores, however, are inherently bounded. Methods: We review common regression approaches for count data and then advocate for a different, more suitable approach for variety scores—binomial regression, and zero-inflated binomial regression, which allow one to consider variety scores as a series of binomial trials, thus accounting for bounding. We provide a demonstration with two simulations and data from the Fayetteville Youth Study. Conclusions: Binomial regression generally performs better than traditional regression models when modeling variety scores. Importantly, the interpretation of binomial regression models is straightforward and related to the more familiar logistic regression. We recommend researchers use binomial regression models when faced with variety delinquency scores.

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