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Erschienen in: Journal of Gambling Studies 2/2021

17.03.2020 | Original Paper

Confounding Bias in the Relationship Between Problem Gambling and Crime

verfasst von: Christopher R. Dennison, Jessica G. Finkeldey, Gregory C. Rocheleau

Erschienen in: Journal of Gambling Studies | Ausgabe 2/2021

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Abstract

Although the relationship between problem gambling and criminal behavior has been widely researched, concerns over the causal nature of this association remain. Some argue that problem gambling does not lead to crime; instead, the same background characteristics that predict problem gambling also predict criminal behavior. Yet, studies suggestive of a spurious association often rely on small, non-random, and cross-sectional samples; thus, the extent to which the findings are generalizable to the broader population is unknown. With this in mind, the present study uses data from The National Longitudinal Study of Adolescent to Adult Health and a series of propensity score weighting and matching techniques to examine the role of confounding bias in the relationship between problem gambling and criminal behavior in young adulthood. On the surface, results show a positive and significant relationship between problem gambling and a range of criminal behaviors. However, after statistically balancing differences in several background measures between problem gamblers and non-problem gamblers, such as low self-control, past substance use, and juvenile delinquency, we find no significant relationship between problem gambling and crime. These patterns are consistent across several propensity score weighting and matching algorithms. Our results therefore parallel those in support of the “generality of deviance” framework, whereby a similar set of covariates known to be associated with criminal behavior also predict problem gambling.
Fußnoten
1
For more information about the Add Health data, see https://​www.​cpc.​unc.​edu/​projects/​addhealth.
 
2
Coefficients (i.e., \(\bar{\beta }\)) from the imputed datasets are combined as follows: \(\bar{\beta } = \frac{{\sum_{i = 1}^{m} \beta_{i} }}{m}\), where \(\beta_{i}\) is the coefficient for the respective covariate \(i\) for each imputed dataset, and \(m\) is the number of imputed datasets. Standard errors for the combined estimates are based on the combination of within- and between-imputation variance: \(Variance_{within} = \frac{{\sum_{i = 1}^{m} SE_{i}^{2} }}{m}\) and \(Variance_{between} = \frac{{\sum_{i = 1}^{m} (\beta_{i} - \bar{\beta })^{2} }}{m - 1}\), where \(m\) is the number of imputed datasets, \(SE_{i}\) is the standard error of the respective covariate \(i\), \(\beta_{i}\) is the parameter estimate of the respective covariate for each imputed dataset, and \(\bar{\beta }\) is the average of the parameter estimates across all of the imputed datasets. The standard error for the combined estimate (i.e., \(\bar{\beta }\)) is as follows: \(SE_{{\bar{\beta }}} = \sqrt {Variance_{within} + Variance_{between} + \frac{{Variance_{between} }}{M}}\), where \(m\) is again the number of imputed datasets.
 
3
Supplemental analyses (available upon request) examined alternative operationalization strategies, such as crime counts and frequency measures, and the results were substantively similar to those presented here.
 
4
We restrict our analytic sample to those who fall within the region of common support (i.e., the range of propensity scores where problem gamblers and non-problem gamblers overlap). Following this restriction, there are 421 treated respondents and 11,260 controlled respondents.
 
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Metadaten
Titel
Confounding Bias in the Relationship Between Problem Gambling and Crime
verfasst von
Christopher R. Dennison
Jessica G. Finkeldey
Gregory C. Rocheleau
Publikationsdatum
17.03.2020
Verlag
Springer US
Erschienen in
Journal of Gambling Studies / Ausgabe 2/2021
Elektronische ISSN: 1573-3602
DOI
https://doi.org/10.1007/s10899-020-09939-0

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