Abstract
The purpose of this paper is to use criminological theories to explain chronic drunk driving. There is little criminological research explaining recidivist drunk driving with criminological theories. Instead, most researchers posit that repeat drunk driving is explained as a byproduct of substance abuse. Although substance abuse is likely correlated to chronic drunk driving, theoretical explanations need to go further to understand a broader set of social and psychological predictors. Factor analysis and linear regression techniques are used to estimate the relationship between items from two assessment instruments with a number of drunken driving offenses. The sample consists of nearly 3,500 individuals on probation and parole in a Southwestern state. The findings support our contention that criminological frameworks are helpful to understand chronic DUI. We found significant results for volatility, antisocial friends, teenage deviance, and negative views of the law, while controlling for age, gender, marital status, and race. DUIs are a serious problem for the criminal justice system and understanding the individual level correlates of repetitive DUI is crucial for policy development. Further, chronic DUI offers criminologists an opportunity to determine the ability of criminological theories to explain this type of behavior.
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Notes
We refer to drunk driving as DUI throughout this article. No doubt, different states and jurisdictions refer to this crime in various ways such as impaired driving, driving while drunk, driving while intoxicated, and other names. DUI is mean as a synonym of these other terms to identify individuals arrested for driving after consuming illegal amounts of alcohol.
Probation and parole are administered through the Department of Corrections as a unified system in Oklahoma.
The data were extracted and delivered to the researchers in December 2009, and they are a snapshot of the drunken driving population on a given day in 2009. This study is retrospective and it lacks duration or longitudinal data, so we are unable to study timing or occurrence of new DUIs. Instead, our analysis focuses on the total number of DUI arrests.
Risk assessment total scores can range from 1–54.
This discussion relies on several sources, but mostly works from Long and Freese’s (2006) discussion of Poisson models in chapter 8. Those interested in a more in thorough treatment of Poisson models and count data are referred to their book as well as Cameron and Trivedi (1998). We follow Long and Freese’s equation symbol of the estimated rate of a count, μ, as opposed to the symbol more commonly used in criminological texts, λ (see Lattimore et al., 2004; Nagin & Land, 1993; Osgood, 2000), but the equations are identical, merely the symbol is different.
The observation is still Poisson when using a negative binomial regression model, and each will tend to give close point estimates, but the Poisson model tends to deflate standard errors causing inflated z-tests and incorrect p-values (Osgood, 2000). Thus, the Poisson regression model could lead to incorrect hypothesis testing (Cameron & Trivedi, 1998). We estimated and compared models using a Poisson and negative binomial regression and found this to be true, with similar estimates and widely varying standard errors (analyses available upon request).
Initial comparisons between the Poisson and negative binomial regression models produced consistent significant chi-square tests confirming overdispersion (p < .000).
We were concerned with the high presence of 0 in our dependent variable, and investigated zero-inflated negative binomial regression models. The zero-inflated models fit models for the negative binomial as well as fit those not experiencing the outcome (no prior DUI arrests) with a unique set of covariates simultaneously (Lambert, 1992). Long and Freese (2006) developed the countfit Stata procedure that compares these models, and the fit tests did not show that the zero-inflated negative binomial regression model was a better fit for the data (Voung test, p = .283).
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Acknowledgments
The authors thank Penn State University’s Justice Center for Research for supporting the development of this paper, and we thank Doris MacKenzie for helpful comments throughout. Additionally, we thank the American Probation and Parole Association for their ongoing commitment to facilitate this research and advance research in community corrections. None of these individuals or organizations are responsible for any of the conclusions or errors.
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DeMichele, M., Lowe, N.C. & Payne, B.K. A Criminological Approach to Explain Chronic Drunk Driving. Am J Crim Just 39, 292–314 (2014). https://doi.org/10.1007/s12103-013-9216-4
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DOI: https://doi.org/10.1007/s12103-013-9216-4