Hypothesis Testing and Model Selection in the Social Sciences

Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital][/ital]) supplies data and syntax files for the book's examples.

“"Weakliem offers a principled discussion of statistical methods for model selection and demonstrates them on applied problems in the social sciences. This thoughtful work should influence both statistical theory and social science practice."--Andrew Gelman, PhD, Department of Statistics, Columbia University "One of the most difficult and complicated problems in any statistical analysis is model selection. In this comprehensive book, Weakliem provides a cogent and accessible presentation of existing thinking and methods. A 'must read' for any sociologist who is a serious applied quantitative researcher."--Christopher Winship, PhD, Diker-Tishman Professor of Sociology, Harvard University "I especially appreciate this book's careful treatment of the philosophical arguments underlying hypothesis testing and the historical approaches that have been taken to the model selection problem. The question addressed here is not 'Which statistical test or approach should I use?' but rather, 'How can model specification, estimation, and statistical estimation advance what is known about a particular problem?' The book makes a convincing case for the utility of both traditional and Bayesian approaches--instead of calling for a Bayesian revolution--and leads quite logically to a number of ways that conventional practice can be improved. Rich bibliographies at the end of each chapter provide sources for further reading."--Phillip K. Wood, PhD, Department of Psychological Sciences, University of Missouri”

"Weakliem offers a principled discussion of statistical methods for model selection and demonstrates them on applied problems in the social sciences. This thoughtful work should influence both statistical theory and social science practice."--Andrew Gelman, PhD, Department of Statistics, Columbia University

"One of the most difficult and complicated problems in any statistical analysis is model selection. In this comprehensive book, Weakliem provides a cogent and accessible presentation of existing thinking and methods. A 'must read' for any sociologist who is a serious applied quantitative researcher."--Christopher Winship, PhD, Diker–Tishman Professor of Sociology, Harvard University

"I especially appreciate this book's careful treatment of the philosophical arguments underlying hypothesis testing and the historical approaches that have been taken to the model selection problem. The question addressed here is not 'Which statistical test or approach should I use?' but rather, 'How can model specification, estimation, and statistical estimation advance what is known about a particular problem?' The book makes a convincing case for the utility of both traditional and Bayesian approaches--instead of calling for a Bayesian revolution--and leads quite logically to a number of ways that conventional practice can be improved. Rich bibliographies at the end of each chapter provide sources for further reading."--Phillip K. Wood, PhD, Department of Psychological Sciences, University of Missouri -

David L. Weakliem, PhD, is Professor of Sociology at the University of Connecticut. He has been a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University and at the Australian National University. Dr. Weakliem is Editor-in-Chief of Comparative Sociology and a past Deputy Editor of the American Sociological Review.

Examining the major approaches to hypothesis testing and model selection, this book blends statistical theory with recommendations for practice, illustrated with real-world social science examples. It systematically compares classical (frequentist) and Bayesian approaches, showing how they are applied, exploring ways to reconcile the differences between them, and evaluating key controversies and criticisms. The book also addresses the role of hypothesis testing in the evaluation of theories, the relationship between hypothesis tests and confidence intervals, and the role of prior knowledge in Bayesian estimation and Bayesian hypothesis testing. Two easily calculated alternatives to standard hypothesis tests are discussed in depth: the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The companion website ([ital][/ital]) supplies data and syntax files for the book's examples.

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