Applied Regression Models in the Social Sciences

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· Cambridge University Press
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This accessible and practical textbook gives students the perfect guide to the use of regression models in testing and evaluating hypotheses dealing with social relationships. A range of statistical methods suited to a wide variety of dependent variables is explained, which will allow students to read, understand, and interpret complex statistical analyses of social data. Each chapter contains example applications using relevant statistical methods in both Stata and R, giving students direct experience of applying their knowledge. A full suite of online resources - including statistical command files, datasets and results files, homework assignments, class discussion topics, PowerPoint slides, and exam questions - supports the student to work independently with the data, and the instructor to deliver the most effective possible course. This is the ideal textbook for advanced undergraduate and beginning graduate students taking courses in applied social statistics.

Tietoja kirjoittajasta

Dudley L. Poston, Jr., is Emeritus Professor of Sociology at Texas A&M University. He held adjunct professor positions at People's (Renmin) University of China, Fuzhou University, and Nanjing Normal University. He has published twenty-two books and over 380 journal articles, book chapters, and related reports, and has taught demography and statistics classes to around 1,000 graduate and 5,000 undergraduate students.

Eugenia Conde is a statistical consultant at the H. W. Odum Institute for Research in Social Science at the University of North Carolina at Chapel Hill. She provides consultations to students and faculty on research methods and statistics.

Layton M. Field is an associate professor at Mount St. Mary's University, and Director of the Mount Community Advancement Resources in Training, Assessment, and Service program. He specializes in research methods and statistics, and has published articles in the areas of demography, sociology, and criminal justice.

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