Bültmann & Gerriets
Applied Regression Models in the Social Sciences
von Dudley L Poston Jr, Eugenia Conde, Layton M Field
Verlag: Greenwich Medical Media
Gebundene Ausgabe
ISBN: 978-1-108-83102-4
Erschienen am 19.10.2023
Sprache: Englisch
Format: 256 mm [H] x 180 mm [B] x 34 mm [T]
Gewicht: 1293 Gramm
Umfang: 400 Seiten

Preis: 116,50 €
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Inhaltsverzeichnis

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.



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.



List of figures; List of tables; Preface; 1. Introduction; 2. Undertaking statistical analysis using Stata; 3. Undertaking statistical analysis using R; 4. Descriptive statistics and the normal distribution; 5. Statistical tables and cross-tabulations; 6. Bivariate regression and correlation and statistical inference; 7. Multiple regression and correlation; 8. Regression assumptions and diagnostics and robust regression; 9. Missing data; 10. Issues of survey design; 11. Binomial logistic regression; 12. Ordinal logistic regression; 13. Multinomial logistic regression; 14. Count regression; 15. Survival analysis; 16. Multilevel models; 17. Other issues and final; References; Index.


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