Applied Regression Models in the Social Sciences
Published on 2. August 2023
978-1-108-92480-1 (ISBN)
Description
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.
Reviews / Votes
'This textbook on applied regression by Poston, Conde, and Field is well suited for teaching graduate students in all of the social science fields, but is especially suited for demography, population studies, and public health. It is an excellent contribution to the teaching and practice of regression analysis in the twenty-first century that also can serve as a useful reference book for practitioners.' David Swanson, University of California, Riverside 'This textbook successfully combines theoretical statistical concepts and empirical research examples of applied regression models, from the basic to advanced ones. The best part is the detailed interpretations of the results of statistical analyses. Readers can truly understand the meanings and usages of these statistical numbers.' Lang-Wen Huang, Soochow University, Taipei 'Applied Regression Models in the Social Sciences excels in its focus on the application and interpretation of various regression models and its inclusion of commands in Stata and R. Its sequencing and topical coverage set it apart from others in that the reader is guided through the entire research process with a multitude of examples. This will be a valuable resource to faculty, students, and applied researchers alike.' Ginny Garcia-Alexander, University of Texas at San Antonio 'This textbook fills a critical void in the market for statistical tutorials by bridging the gap between elementary and advanced statistics and providing real-world examples in both Stata and R, allowing students to develop proficiency in the statistical software environments in highest demand. It strikes the perfect balance, neither oversimplifying nor overwhelming with complex mathematics, making it the ideal companion for graduate students seeking a solid foundation in the skills needed to generate social science findings with advanced insights. From correlation analysis to multi-level modeling, this comprehensive and versatile book covers a wide range of regression techniques, equipping learners with a diverse toolkit for trustworthy data analysis and allowing them to transition from the classroom to the laboratory with confidence. The authors' expertise shines through in this clear, comprehensive, and engaging book that is destined to be an indispensable resource.' Stephanie Bohon, University of TennesseeMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises
File size
7,04 MB
ISBN-13
978-1-108-92480-1 (9781108924801)
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Schweitzer Classification
Other editions
Additional editions
Dudley L. Poston, Jr | Eugenia Conde | Layton M. Field
Applied Regression Models in the Social Sciences
Book
08/2023
Cambridge University Press
€127.50
Shipment within 15-20 days
Persons
Content
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.
