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Journal of Quantitative Criminology

, Volume 34, Issue 2, pp 591–607 | Cite as

The Analysis of Bounded Count Data in Criminology

  • Chester L. Britt
  • Michael RocqueEmail author
  • Gregory M. Zimmerman
Original Paper

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.

Keywords

Binomial regression Variety scores Count data Poisson regression Negative binomial regression 

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Chester L. Britt
    • 1
  • Michael Rocque
    • 2
    Email author
  • Gregory M. Zimmerman
    • 3
  1. 1.Department of SociologyIowa State UniversityAmesUSA
  2. 2.Department of SociologyBates CollegeLewistonUSA
  3. 3.School of Criminology and Criminal JusticeNortheastern UniversityBostonUSA

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