Public Policy and Statistics

Case Studies from RAND

Edited by Sally C. Morton, John E. Rolph

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This casebook describes the varied analytical techniques and substantive applications that typify how statistical thinking has been applied at RAND over the past two decades. The philosophy is to be critical yet constructive. Case studies of public policy problems are useful for teaching because they are familiar. Almost everyone knows something about health insurance, global warming, and capital punishment, to name a few of the applications covered in this casebook. Each case study has a common format that describes the policy questions, the statistical questions, and the successful and unsuccessful analytic strategies. This book is designed for statistics courses in areas ranging from economics to health policy to the law at both the advanced undergraduate and graduate levels. Empirical researchers and policymakers should also find this casebook informative.

"This excellent casebook describes the varied analytical techniques and substantive applications that typify how 'statistical think' has been applied at RAND since its statistics group was established in 1976. This collection of 10 case studies is organized into three sections, each reflecting the three major tasks in empirical research: collecting data, detecting effects, and understanding relationships. Each case study has seven main sections: (1) an introduction to the policy problem, the research questions, the statistical questions, and a summary of data and methods; (2) the study design, data collection, data sources, and data elements; (3) the dataset creation, including the file construction and variable derivation, descriptive statistics, and results of any exploratory data analysis undertaken; (4) the statistical methods and model as applicable; (5) the results of the analysis, including model validation and sensitivity analysis; (6) a discussion of the results, how they were used, their limitations, and their implications in terms of policy; and (7) exercises, including datasets obtainable on the editor's website…"

- Martin T. Wells, Cornell University

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