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Alexander M. Holsinger

Other affiliations: University of Missouri
Bio: Alexander M. Holsinger is an academic researcher from University of Missouri–Kansas City. The author has contributed to research in topics: Predictive validity & Risk assessment. The author has an hindex of 18, co-authored 33 publications receiving 1665 citations. Previous affiliations of Alexander M. Holsinger include University of Missouri.

Papers
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Journal ArticleDOI
TL;DR: The Level of Service Inventory-Revised (LSI-R) is a risk/need assessment instrument that was designed to assist correctional agencies in classifying offenders based upon risk of re-offending, thereby allowing agencies to assign appropriate levels of risk and develop intervention/case-plans accordingly as mentioned in this paper.

88 citations

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TL;DR: In this paper, the Level of Service Inventory-Revised (LSI-R) risk/need assessment for Native American and non-native American offenders as well as male and female offenders were investigated and compared.

86 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the relationship between aggressive police strategies and serious crime and found that the strategy was unrelated to levels of aggravated burglary and robbery in the target area and there was no spatial displacement of crime.
Abstract: This paper adds to a growing body of research which explores the relationship between aggressive police strategies and serious crime. For one month, police enforced disorder crime in a small section of one community. An interrupted time series analysis was utilized to evaluate the effects of this intervention on robbery and aggravated burglary in a target area and a control area. The strategy was found to be unrelated to levels of aggravated burglary and robbery in the target area. There was no spatial displacement of crime. Explanations for the findings are offered.

81 citations

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TL;DR: In this paper, the authors examined the effectiveness of fifty-eight intensive supervision programs and sought to determine whether program philosophy and treatment integrity are associated with reductions in recidivism, and the results indicated that treatment integrity and program philosophy vary independently of one another.

81 citations

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TL;DR: In this article, supervision officers were trained in core correctional skills and the risk-need-responsivity (RNR) model, in conjunction with core correctional practices, and the results showed that trained probation officers demonstrated greater use of the skills taught during training and their clients had lower failure rates.

79 citations


Cited by
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Journal ArticleDOI
TL;DR: Andrews et al. as discussed by the authors reviewed the progress of risk assessment in criminal justice and assess progress since Andrews, Bonta, and Hoge's (1990; Andrews, Zinger, et al., 1990) statement of the human service principles of risk-needresponsivity and professional discretion.
Abstract: The history of risk assessment in criminal justice has been written on several occasions (Andrews & Bonta, 2003; Clements, 1996; Hollin, 2002). Here we assess progress since Andrews, Bonta, and Hoge’s (1990; Andrews, Zinger, et al., 1990) statement of the human service principles of risk-needresponsivity (RNR) and professional discretion. In those articles, the corrections-based terms of risk and need were transformed into principles addressing the major clinical issues of who receives treatment (higher risk cases), what intermediate targets are set (reduce criminogenic needs), and what treatment strategies are employed (match strategies to the learning styles and motivation of cases: the principles of general and specific responsivity). General responsivity asserts the general power of behavioral, social learning, and cognitive-behavioral strategies. Specific responsivity suggests matching of service with personality, motivation, and ability and with demographics such as age, gender, and ethnicity. Nonadherence is possible for stated reasons under the principle of professional discretion. Expanded sets of principles now include consideration of case strengths, setting of multiple criminogenic needs as targets, community-based, staff relationship and structuring skills, and a management focus on integrity through the selection, training, and clinical supervision of staff and organizational supports (Andrews, 2001). The review is conducted in the context of the advent of the fourth generation of offender assessment. Bonta (1996) earlier described three generations of risk assessment. The first generation (1G) consisted mainly of unstructured professional judgments of the probability of offending behavior. A

1,302 citations

Journal ArticleDOI
TL;DR: The present meta-analysis integrates research from 1,435 studies on associations of parenting dimensions and styles with externalizing symptoms in children and adolescents to predict change in Externalizing problems over time, with associations of externalizing problems with warmth, behavioral control, harsh control, psychological control, and authoritative parenting being bidirectional.
Abstract: The present meta-analysis integrates research from 1,435 studies on associations of parenting dimensions and styles with externalizing symptoms in children and adolescents. Parental warmth, behavioral control, autonomy granting, and an authoritative parenting style showed very small to small negative concurrent and longitudinal associations with externalizing problems. In contrast, harsh control, psychological control, authoritarian, permissive, and neglectful parenting were associated with higher levels of externalizing problems. The strongest associations were observed for harsh control and psychological control. Parental warmth, behavioral control, harsh control, psychological control, autonomy granting, authoritative, and permissive parenting predicted change in externalizing problems over time, with associations of externalizing problems with warmth, behavioral control, harsh control, psychological control, and authoritative parenting being bidirectional. Moderating effects of sampling, child's age, form of externalizing problems, rater of parenting and externalizing problems, quality of measures, and publication status were identified. Implications for future research and practice are discussed. (PsycINFO Database Record

711 citations

Posted Content
TL;DR: It is argued that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce, rather than requiring that algorithms satisfy popular mathematical formalizations of fairness.
Abstract: The nascent field of fair machine learning aims to ensure that decisions guided by algorithms are equitable. Over the last several years, three formal definitions of fairness have gained prominence: (1) anti-classification, meaning that protected attributes---like race, gender, and their proxies---are not explicitly used to make decisions; (2) classification parity, meaning that common measures of predictive performance (e.g., false positive and false negative rates) are equal across groups defined by the protected attributes; and (3) calibration, meaning that conditional on risk estimates, outcomes are independent of protected attributes. Here we show that all three of these fairness definitions suffer from significant statistical limitations. Requiring anti-classification or classification parity can, perversely, harm the very groups they were designed to protect; and calibration, though generally desirable, provides little guarantee that decisions are equitable. In contrast to these formal fairness criteria, we argue that it is often preferable to treat similarly risky people similarly, based on the most statistically accurate estimates of risk that one can produce. Such a strategy, while not universally applicable, often aligns well with policy objectives; notably, this strategy will typically violate both anti-classification and classification parity. In practice, it requires significant effort to construct suitable risk estimates. One must carefully define and measure the targets of prediction to avoid retrenching biases in the data. But, importantly, one cannot generally address these difficulties by requiring that algorithms satisfy popular mathematical formalizations of fairness. By highlighting these challenges in the foundation of fair machine learning, we hope to help researchers and practitioners productively advance the area.

685 citations

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TL;DR: The effects of correctional interventions on recidivism have important public safety implications when offenders are released from probation or prison as discussed by the authors, and hundreds of studies have been conducted on those effects, some investigating punitive approaches and some investigating rehabilitation treatments.
Abstract: The effects of correctional interventions on recidivism have important public safety implications when offenders are released from probation or prison. Hundreds of studies have been conducted on those effects, some investigating punitive approaches and some investigating rehabilitation treatments. Systematic reviews (meta-analyses) of those studies, while varying greatly in coverage and technique, display remarkable consistency in their overall findings. Supervision and sanctions, at best, show modest mean reductions in recidivism and, in some instances, have the opposite effect and increase reoffense rates. The mean recidivism effects found in studies of rehabilitation treatment, by comparison, are consistently positive and relatively large. There is, however, considerable variability in those effects associated with the type of treatment, how well it is implemented, and the nature of the offenders to whom it is applied. The specific sources of that variability have not been well explored, but some princ...

659 citations