How Do Criminologists Interpret Statistical Explanation of Crime? A Review of Quantitative Modeling in Published Studies

Weisburd, David; Cave, Breanne

doi:10.1002/9781118512449.ch21

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…Next, we turn to two multivariate analyses beginning with an assessment of the highest level of force , where we employ an ordered logit regression model using SPSS version 26.0 with results presented in Table 4. 9 As shown, the overall model is statistically significant as evidenced by the chi-square statistic with 15.2 percent of the variance explained, although caution is required as ordered logit only generates a pseudo R-squared statistic (Weisburd et al, 2015; Weisburd & Piquero, 2008).…”

Section: Analyses and Findingsmentioning

confidence: 97%

Police Officer Use of Force Mindset and Street-Level Behavior

2021

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Police use of force has been the focus of a number of external assessments of the occupation for over 50 years. Recent concerns have, once again, prompted calls for additional research on the correlates of this behavior, especially as it relates to officer use of force mindset. Relying on a framework articulated as part of a use of force symposium of academics and practitioners, the current study utilizes survey and behavioral data from officers in six police agencies to examine dimensions of use of force mindset among officers, and the degree to which attitudinal mindset influences use of force behavior. The implications for police scholarship and practice are discussed.

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Section: Analyses and Findingsmentioning

confidence: 97%

Police Officer Use of Force Mindset and Street-Level Behavior

2021

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“…This reveals the limits of the positivist perspective. Weisburd et al (2015; see also Weisburd & Piquero, 2008) have lamented about how little explained variance in key outcomes criminological research has been able to achieve and that, despite more data and enhanced computational capacities, typical coefficients of determination have not grown over time. Does this suggest Cullen reconsider his exclusively positivist arsenal?…”

Section: Cullen's Responsementioning

confidence: 99%

Paternoster on Human Agency and Crime: a Rejoinder to Critics on His Behalf

Thomas

Pogarsky

Loughran

2021

J Dev Life Course Criminology

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“…We derive a general probabilistic model that can capture generic features across space and that can consider spatial correlations using a non-parametric component. As noted by Weisburd et al [33], quantitative studies in criminology focus on 'mechanical' reporting of estimates and predictive power. These studies ignore the uncertainty around these estimates.…”

Section: Related Workmentioning

confidence: 99%

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

et al. 2018

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Objectives: This paper describes the use of machine learning techniques to implement a Bayesian approach to modelling the dependency between offence data and environmental factors such as demographic characteristics and spatial location. The main goal of this paper is to provide a fully probabilistic approach to modelling crime which reflects all uncertainties in the prediction of offences as well as the uncertainties surrounding model parameters. Methods:The proposed method is based on a Bayesian framework, with a Gaussian Process prior and MCMC, allowing uncertainties in prediction and inference to be quantified via the posterior distributions of interest. By using Bayesian updating, these predictions and inferences are dynamic in the sense that they change as new information becomes available. Results:We applied the proposed methodology to particular offence data, such as domestic violence-related assaults, burglary and motor vehicle theft, in the state of New South Wales (NSW), Australia. Our results demonstrate the strength of the technique by validating the factors that are associated with high and low criminal activity, including bounds on the degree of the relation. Conclusions:We argue that this fully probabilistic approach will improve prediction, in the sense that the uncertainties are more accurately quantified, with attendant benefits to policymakers and policing organisations seeking to deploy limited criminal justice resources to prevent and control crime. While limitations and areas for potential improvement are identified, the success of the Bayesian approach, implemented using machine learning techniques, in a criminological context represents an exciting development.

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How Do Criminologists Interpret Statistical Explanation of Crime? A Review of Quantitative Modeling in Published Studies

Cited by 7 publications

References 38 publications

Police Officer Use of Force Mindset and Street-Level Behavior

Police Officer Use of Force Mindset and Street-Level Behavior

Paternoster on Human Agency and Crime: a Rejoinder to Critics on His Behalf

Applying machine learning to criminology: semi-parametric spatial-demographic Bayesian regression

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