Jordan M Hyatt

This Handbook uses methodologies and cases to discover how and when to evaluate social policy, and looks at the possible impacts of evaluation on social policy decisions. The contributors present a detailed analysis on how to conduct social policy evaluation, how to be aware of pitfalls and dilemmas and how to use evidence effectively. Organized into three thematic sections, this new resource includes contributions from a variety of researchers from a range of disciplines and countries. The first section explores evaluation and examples of methods used; the second focuses on the intersection between evaluation and policy-making; and the third delves into current social policy in order to discover the use of evaluation within central welfare state policies. One conclusion found is that welfare states are increasingly using evidence, but that it varies from and within different welfare areas. Researchers and students with an interest in evaluation and social policy analysis, as well as policy-makers and administrators in need of evidence and analysis on the subject, will find much value in this clear and precise overview of the use and misuse of evidence.

Study results demonstrate that the project's risk-forecasting models were able to increase the Philadelphia Adult Probation and Parole Department's (APPD's) ability to predict recidivism, leading to the restructuring of agency supervision protocols. The project resulted in the construction of three different prediction models based on a statistical process known as 'random forest.' One benefit of random forest modeling is that there is no theoretical limit on the number of predictors that can be included in the model. Throughout the duration of the project, hundreds of different predictor variables were drawn from electronically available administrative records, and they were tested for possible use in these models. The most recent version of APPD's model produced an accurate forecast for 79,299 of the 119,935 probation case starts in the construction sample. These estimates suggest that this model can be correct nearly two-thirds (66.1 percent) of the time; however, a more reasonable method of measuring the model's accuracy is to re-examine forecasted and actual outcomes separately, focusing on each of the three different outcome categories. The power and promise of the random forest forecasting methods is clear in Philadelphia; their introduction has allowed the agency to stratify offenders by the risk they pose, to tailor supervision requirements, to focus resources in accordance with policy directives, and to balance caseload sizes in the face of budgetary constraints. Twelve recommendations for building random forest prediction models in any jurisdiction are offered. Figures, tables, and references. Tables, Figures, References.