This textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. All of the analyses included are done in R with code routinely provided.
This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of “big data” on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
Through the use of specific examples to illustrate evaluation research goals and methods, this book provides readers with an overview of the science and politics of evaluation research. The Second Edition includes coverage of meta-analysis, selection models and instrumental variables.
Money, Work, and Crime: Experimental Evidence presents the complete details of the Department of Labor’s $3.4 million Transitional Aid Research Project (TARP), a large-scale field experiment which attempted to reduce recidivism on the part of ex-felons. Beginning in January 1976, some prisoners released from state institutions in Texas and Georgia were offered financial aid for periods of up to six months post-release. Payments were made in the form of Unemployment Insurance benefits. The ex-prisoners who were eligible for payments were compared with control groups released at the same time from the same institutions. The control groups were not eligible for benefits. The assumption that modest levels of financial help would ease the transition from prison life to civilian life was partially supported. Ex-prisoners who received financial aid under TARP had lower rearrest rates than their counterparts who did not receive benefits and worked comparable periods of time. Those receiving financial aid were also able to obtain better-paying jobs than the controls. However, ex-prisoners receiving benefits took longer to find jobs than those who did not receive benefits. The TARP experiment makes a strong contribution both to an important policy area—the reduction of crime through reducing recidivism—and to the further development of the field and experiment as a policy research instrument.
Berk has incisively identified the various strains of regression abuse and suggests practical steps for researchers who desire to do good social science while avoiding such errors." --Peter H. Rossi, University of Massachusetts, Amherst "I have been waiting for a book like this for some time. Practitioners, especially those doing applied work, will have much to gain from Berk′s volume, regardless of their level of statistical sophistication. Graduate students in sociology, education, public policy, and any number of similar fields should also use it. It will also be a useful foil for conventional texts for the teaching of the regression model. I plan to use it for my students as a text, and hope others will do the same." --Herbert Smith, Professor of Demography & Sociology, University of Pennsylvania Regression is often applied to questions for which it is ill equipped to answer. As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. The problem, though, is that researchers typically want more: they want tests, confidence intervals and the ability to make causal claims. However, these capabilities require information external to that data themselves, and too often that information makes implausible demands on how nature is supposed to function. Convenience samples are treated as if they are random samples. Causal status is given to predictors that cannot be manipulated. Disturbance terms are assumed to behave not as nature might produce them, but as required by the model. Regression Analysis: A Constructive Critique identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved. Regression is most useful for data reduction, leading to relatively simple but rich and precise descriptions of patterns in a data set. The emphasis on description provides readers with an insightful rethinking from the ground up of what regression analysis can do, so that readers can better match regression analysis with useful empirical questions and improved policy-related research. "An interesting and lively text, rich in practical wisdom, written for people who do empirical work in the social sciences and their graduate students." --David A. Freedman, Professor of Statistics, University of California, Berkeley
This concluding volume of The Vietnam War and International Law focuses on the last stages of America's combat role in Indochina. The articles in the first section deal with general aspects of the relationship of international law to the Indochina War. Sections II and III are concerned with the adequacy of the laws of war under modern conditions of combat, and with related questions of individual responsibility for the violation of such laws. Section IV deals with some of the procedural issues related to the negotiated settlement of the war. The materials in Section V seek to reappraise the relationship between the constitutional structure of the United States and the way in which the war was conducted, while the final section presents the major documents pertaining to the end of American combat involvement in Indochina. A supplement takes account of the surrender of South Vietnam in spring 1975. Contributors to the volume—lawyers, scholars, and government officials—include Dean Rusk, Eugene V. Rostow, Richard A. Falk, John Norton Moore, and Richard Wasserstrom. Originally published in 1976. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.
The eighth volume in this series concentrates on developments of enormous importance to all of social science. Through such techniques as meta-analysis, the findings of very different studies can be given different mathematical weights and combined. Thus literature review becomes a way of consolidating past work in order to build upon it genuinely. In this volume, methodological questions are dealt with and a range of examples of reviews of research in education, mental health and medicine are presented.
This book puts in one place and in accessible form Richard Berk’s most recent work on forecasts of re-offending by individuals already in criminal justice custody. Using machine learning statistical procedures trained on very large datasets, an explicit introduction of the relative costs of forecasting errors as the forecasts are constructed, and an emphasis on maximizing forecasting accuracy, the author shows how his decades of research on the topic improves forecasts of risk. Criminal justice risk forecasts anticipate the future behavior of specified individuals, rather than “predictive policing” for locations in time and space, which is a very different enterprise that uses different data different data analysis tools. The audience for this book includes graduate students and researchers in the social sciences, and data analysts in criminal justice agencies. Formal mathematics is used only as necessary or in concert with more intuitive explanations.
Provides coverage of state and local institutions, political behaviour, and policy-making. Users cite the text's strengths as its policy orientation and it's unifying theme of the increased capacity and responsibility of state and local government.
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