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931.
Richard Berk Lawrence Brown Andreas Buja Edward George Linda Zhao 《Journal of Quantitative Criminology》2018,34(3):633-655
Objectives
Conventional statistical modeling in criminology assumes proper model specification. Very strong and unrebutted criticisms have existed for decades. Some respond that although the criticisms are correct, there is for observational data no alternative. In this paper, we provide an alternative.Methods
We draw on work in econometrics and statistics from several decades ago, updated with the most recent thinking to provide a way to properly work with misspecified models.Results
We show how asymptotically, unbiased regression estimates can be obtained along with valid standard errors. Conventional statistical inference can follow.Conclusions
If one is prepared to work with explicit approximations of a “true” model, defensible analyses can be obtained. The alternative is working with models about which all of the usual criticisms hold.932.
Parole has long been a linchpin of correctional practices but few studies have examined discretionary parole release in a female population. The current study examines factors, both static risk and dynamic needs, that influence parole decision making in a rural female jail population. The researchers collected data on parole releases from a rural county jail over a 3-year period beginning in 2012 (N?=?138). Offenders obtained a recommendation for parole release from a reentry assessment team that met at the jail each month to evaluate cases for parole eligibility. Logistic regression was used to explain variance in the factors considered by the reentry assessment team and it was evident that both static risk factors and dynamic needs play a role in discretionary parole release. We also examined obstacles or challenges that female offenders face when paroling to a rural location. Several policy recommendation related to the study are addressed. 相似文献