This title offers an accessible introduction to the use of regression analysis in the social sciences. It has the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners. Covering techniques that span the full spectrum of levels of measurement for both continuous and limited response variables, and using examples taken from such disciplines as sociology, psychology, political science, and public health, the author succeeds in demystifying an academically rigorous subject and making it accessible to a wider audience. Content includes coverage of: logit, probit, scobit, truncated, and censored regressions; multiple regression with ANOVA and ANCOVA models; binary and multinomial response models; poisson, negative binomial, and other regression models for event-count data; and, survival analysis using multistate, multiepisode, and interval-censored survival models.
This title offers an accessible introduction to the use of regression analysis in the social sciences. It has the most complete and fully integrated coverage of regression modeling currently available for graduate-level behavioral science students and practitioners. Covering techniques that span the full spectrum of levels of measurement for both continuous and limited response variables, and using examples taken from such disciplines as sociology, psychology, political science, and public health, the author succeeds in demystifying an academically rigorous subject and making it accessible to a wider audience. Content includes coverage of: logit, probit, scobit, truncated, and censored regressions; multiple regression with ANOVA and ANCOVA models; binary and multinomial response models; poisson, negative binomial, and other regression models for event-count data; and, survival analysis using multistate, multiepisode, and interval-censored survival models.
Preface.
1. Introduction to Regression Modeling.
2. Simple Linear Regression.
3. Introduction to Multiple Regression.
4. Multiple Regression with Categorical Predictors: ANOVA and ANCOVA Models.
5. Modeling Nonlinearity.
6. Advanced Issues in Multiple Regression.
7. Regression with a Binary Response.
8. Advanced Topics in Logistic Regression.
9. Truncated and Censored Regression Models.
10. Regression Models for an Event Count.
11. Introduction to Survival Analysis.
12. Multistate, Multiepisode, and Interval-Censored Models in Survival Analysis.
Appendix A: Mathematics Tutorials.
Appendix B: Answers to Selected Exercises.
References.
Index.
ALFRED DEMARIS, PHD, is Professor of Sociology at Bowling Green State University, Ohio. In addition to consulting on a regular basis, Dr. DeMaris has published well over fifty research papers and is a member of several local university and community boards.
"This book is intended to serve as a text for intermediate-level social science graduate courses on regression and as a reference book for practicing researchers. I recommend it for both purposes." (Sociological Methods & Research, 2009) "I would have left 'social data' out of the title of this excellent book.... The book has value for any statistician for its inclusion of all possible types of response measurement in a single regression textbook." (Technometrics, August 2005) "Regression with Social Data includes features desirable of a textbook for classroom use (e.g., datasets, 275 end-of-chapter exercises with partial solutions) but also has the depth and sophistication of a reference book. As a reference book, it is useful to anyone in statistical consulting or social science research." (The American Statistician, May 2006)
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