Four principles of classification for effective rehabilitation are reviewed: risk, need, responsivity, and professional override. Many examples of Case x Treatment interactions are presented to ill...

Classification for effective rehabilitation: Rediscovering psychology.

Statistical Analysis With Missing Data (2nd ed.) (Book)

Prediction of firm bankruptcies have been extensively studied in accounting, as all stakeholders in a firm have a vested interest in monitoring its financial performance. This paper presents an exploratory study which compares the predictive capabilities for firm bankruptcy of neural networks and classical multivariate discriminant analysis. The predictive accuracy of the two techniques is presented within a comprehensive, statistically sound framework, indicating the value added to the forecasting problem by each technique. The study indicates that neural networks perform significantly better than discriminant analysis at predicting firm bankruptcies. Implications of our results for the accounting professional, neural networks researcher and decision support system builders are highlighted.

Bankruptcy prediction using neural networks

A cumulative protection model is proposed to explain why chronic juvenile delinquency may be amenable to prevention and how early family support and education may help achieve this important societal goal. A comprehensive review of early risk factors for chronic delinquency is presented with special attention to interactive effects. Interventions combining comprehensive family support with early education may bring about long-term prevention through short-term protective effects on multiple risks. A review of the early intervention literature reveals that the family support component is associated with effects on family risks, while the early education component is associated with effects on child risks. Both components may be necessary for effects on multiple risks and later reductions in delinquency. Implications for social policy and improvement of Head Start are discussed.

Prevention as Cumulative Protection: Effects of Early Family Support and Education on Chronic Delinquency and Its Risks

The prediction of violence occupies a prominent and controversial place in public mental health practice. Productive debate about the validity of violence predictions has been hampered by the use of methods for quantifying accuracy that do not control for base rates or biases in favor of certain outcomes. This article describes these problems and shows how receiver-operating characteristic analysis can be used to solve them. The article also reanalyzes 58 data sets from 44 published studies of violence prediction. Taken together, these data strongly suggest that mental health professionals' violence predictions are substantially more accurate than chance. Short-term (1-7 day) clinical predictions seem no more accurate than long-term (> 1 year) predictions. Past behavior alone appears to be a better long-term predictor of future behavior than clinical judgments and may also be a better indicator than cross-validated actuarial techniques.

Assessing predictions of violence: Being accurate about accuracy.

This paper examines how crime reporting has changed over time by comparing findings from the 2007/08 British Crime Survey with the results obtained from the last major enquiry reported in 1994. International research conducted since that date is also reviewed. The paper finds that seriousness of the offence is still the most important factor influencing victims' decisions. But reporting rates have changed: broadly, property crime is less likely to be reported whereas violent crime is more likely to be reported. As property crime predominates, the overall trend has been downward. There has been a very notable shift in that crimes committed by family members and others well known to the victim are much more likely to be reported than previously.

Reporting Crime to the Police

Prediction in criminology

1. Statistical Modelling: An Overview 2. Research Designs and Data 3. Statistical Preliminaries 4. Multiple Regression for Continuous Response Variables 5. Logistic Regression for Binary Response Variables 6. Multinomial Logistic Regression for Multinomial Response Variables 7. Loglinear Modelling 8. Ordinal Logistic Regression for Ordered Categorical Response Variables 9. Multilevel Modelling 10. Latent Variables and Factor Analysis 11. Causal Modelling: Simultaneous Equation and Structural Equation Models 12. Longitudinal Data Analysis 13. Event History Models

Statistical Modelling for Social Researchers: Principles and Practice

Growth in advanced capitalist economies, 1950-1970

Drawing on information from a random sample of over 1,000 cases this paper highlights the variety and complexity of missing person cases, reported to the police. The overwhelming majority of missin...

http://journals.sagepub.com/doi/pdf/10.1350/ijps.6.1.16.34459

Roger Tarling

Papers

Reporting Crime to the Police

Prediction in criminology

Statistical Modelling for Social Researchers: Principles and Practice

Growth in advanced capitalist economies, 1950-1970

The Nature and Outcome of Going Missing: The Challenge of Developing Effective Risk Assessment Procedures