A Tale of Two Margins: Exploring the Probabilistic Processes that Generate Prison Visits in the First Two Years of Incarceration

Hickert, Audrey; Tahamont, Sarah; Bushway, Shawn D.

doi:10.1007/s10940-017-9351-z

Cited by 24 publications

(22 citation statements)

References 32 publications

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“…The LCGA were estimated using the inmate sanctioning subsample (i.e., only those individuals who received at least one guilty misconduct during their incarceration) and the “LCMM” command in the statistical program R. As a result of the strict convergence criteria associated with this package, the five‐group linear model and the four‐group quadratic model did not converge upon a local norm . As noted by various scholars (e.g., Hickert et al., ; Nagin, ; Nagin, ; Nagin and Land, ; Nagin and Odgers, ), the best fit model can be designated by the lowest values on the ‐2loglikelihood, BIC, and AIC fit statistics. Following the criteria set forth by prior scholarship (Nagin, ), the four‐group linear model appeared to be the best fit model (–2loglikelihood = –139576.28; BIC = 279370.56; AIC = 279176.55).…”

Section: Resultsmentioning

confidence: 99%

“…As a result of the statistical limitations of LCGA, scholars must approximate multiple models to determine the number of behavioral clusters theoretically and empirically appropriate for the sample (Nagin and Land, ; Muthén, ). Generally, empirical appropriateness has been established through reliance on fit criteria statistics such as ‐2loglikelihood, Bayesian information criterion (BIC), and the Akaike information criterion (AIC; Hickert, Tahamont, and Bushway, ; Nagin, ).…”

Section: Methodsmentioning

confidence: 99%

“…Generally, the findings from prior corrections scholarship in which LCGA was employed have indicated that unbalanced data designs might bias the identification of clusters (e.g., Cochran and Mears, ; Hickert et al., ). At the forefront of these concerns is the misclassification of inmates into the longitudinal clusters resulting from the variation in sentence length (Cochran and Mears, ).…”

Section: Methodsmentioning

confidence: 99%

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Ensnarement During Imprisonment

Silver

Nedelec

2018

Criminology & Public Policy

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Research Summary We used data collected during the Evaluation of Ohio's Prison Programs. The analytical sample of N = 63,772 inmates represents one of the largest samples used to assess the association between within‐prison sanctioning and recidivism. Latent class growth analysis (LCGA) demonstrated that five guilty sanctioning clusters existed within the data: Persistent (0.72%), Very High Decline (0.11%), High Decline (1.38%), Moderate Decline (27.03%), and Abstainers (70.75%). The examination of sanctioning cluster classification on post‐release recidivism suggested that greater exposure to formal sanctions during imprisonment predicted recidivism 1, 2, and 3 years post‐release. Policy Implications The outcome of the empirical analysis suggested an association between within‐prison sanctioning clusters and recidivism. Furthermore, longitudinal sanctioning clusters exposed to higher levels of within‐prison sanctioning possessed a greater probability of recidivism 1, 2, and 3 years post‐release. If replicated, two policy implications could be derived from the contextualization of these results within the ensnarement framework. First, it is recommended that the frequency and severity of sanctions be reduced, to the extent possible, during imprisonment. Second, increased access to prosocial opportunities (e.g., rehabilitation programs) during sanctioning efforts is also encouraged based on the analyses.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Ensnarement During Imprisonment

Silver

Nedelec

2018

Criminology & Public Policy

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“…The benefits of visitation may also be temporary, with a fleeting influence on prison misconduct over time (Siennick et al, 2013). In addition, because visitation studies consistently find that only a small proportion of inmates actually receive visits (Cochran, 2012; Hickert et al, 2018), nonvisited inmates may be distinctly different from the visited group with regard to disciplinary infractions, particularly serious and violent misconduct.…”

Section: The Influence Of Visitation On Inmate Misconductmentioning

confidence: 99%

Visitation and Misconduct Among Maximum-Security Inmates

Reidy

Sorensen

2020

The Prison Journal

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This study adds to the literature by clarifying the effects of visitation on serious and violent misconduct among maximum-security inmates through the application of propensity score matching (PSM). Findings demonstrate that once the visited and nonvisited groups were matched on covariates of visitation, major violations were significantly influenced by visitation from family and friends. The visitation group experienced a 25% reduction in major, violent, and injurious acts of misconduct. Results are discussed from the perspective of social support theory.

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“…При использовании методов теории вероятностей все полученные итоговые значения будут иметь вероятностный характер. Ве-роятностные модели успешно применяются для решения достаточно широкого круга криминологических задач [15][16][17]. Ученые-криминологи уже доказали вероятностный характер формируемых на основе анализа количественных параметров преступности результатов ее прогноза [18, с.…”

Section: Introductionunclassified

The Digitization of Criminal Policy as a Tool of Overcoming its Unsystematicity

Максимов

Vasin

Valuyskov

et al. 2019

Russian Journal of Criminology

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The authors examine the necessity and relevance of using mathematical models for the digitization of criminal policy while simultaneously taking into account the results of qualitative analysis of the criminogenic situation (theoretical modeling) and using probabilistic prediction models of crime counteraction results (empirical modeling). They determine the chronological matrix of key criminogenic events of modern Russian political and economic history, which forms the basis for developing a mathematical model of predicting changes in the criminal policy. The authors conclude that contemporary criminal policy contains objective signs of manipulative character and that criminal policy decisions cannot be based on it exclusively. It is noted that the presented prediction models based on the theory of relativity allow to make verifiable quantitative evaluations of the situation in counteracting negative socio-legal phenomena and to take into account not only the latent component of these phenomena, but also the differences caused by changes in their criminal law assessment. The presented approach could be used to build a triplex system of official monitoring of criminogenic situation at the federal and regional levels based on the collection, analysis, assessment and prediction of information on criminality from three sources: criminal statistics, public opinion polls’ results, expert polls’ results. According to the authors, the creation of this system is a mandatory prerequisite for the transition from a dominantly repressive model of crime counteraction (typical for most contemporary states) to the preventive model. As the probable effectiveness of preventive model of crime counteraction could be first calculated on the basis of planned expenses and available resources and then compared with the actually achieved effectiveness of such counteraction, the state and the society could gain a considerably more effective instrument of controlling the criminal policy’s quality in comparison with the one used today, which will make it possible to ensure a gradual transition from the high cost criminal policy implemented through trial and error to the digital criminal policy based on the results of preliminary and verified calculations.

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A Tale of Two Margins: Exploring the Probabilistic Processes that Generate Prison Visits in the First Two Years of Incarceration

Cited by 24 publications

References 32 publications

Ensnarement During Imprisonment

Ensnarement During Imprisonment

Visitation and Misconduct Among Maximum-Security Inmates

The Digitization of Criminal Policy as a Tool of Overcoming its Unsystematicity

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