© 2010 – Routledge
The book provides graduate students in the social sciences with the basic skills that they need to estimate, interpret, present, and publish basic regression models using contemporary standards.
Key features of the book include:
This book is for a one-semester course. For a two-semester course, see www.routledge.com/books/details/9780415875363/
"Regression Analysis for the Social Sciences gives graduate students and their teachers an exceptionally well-written introduction to statistical concepts along with precise, step-by-step instructions for putting those concepts into practice. By interweaving conceptual discussion with illustrations from social science literature and how-to examples using Stata, SAS, Excel and national data sets, Gordon has created a uniquely effective teaching tool."—Margaret Usdansky, Sociology, Syracuse University
"At last, an author who recognizes that demystifying statistics is the first step in teaching statistics, who realizes that teaching students to understand statistics is not the same thing as teaching them to do statistics. Rachel Gordon offers just the right mix of statistical theory and statistical training in a straightforward, accessible manner that will leave graduate students grateful that their instructor picked her textbook for their class."—Robert Crosnoe, Sociology, University of Texas at Austin
"Regression Analysis for the Social Sciences is a masterpiece that I predict will be widely used in statistics courses in multiple disciplines. The contemporary, diverse, and policy-relevant illustrations are bound to intrigue and instruct students from an array of backgrounds. This book will undoubtedly become an invaluable resource."—Lindsay Chase-Lansdale, Education and Social Policy, Northwestern University
1. Examples of Social Science Research Using Regression Analysis 2. Planning a Quantitative Research Project With Existing Data 3. Basic Features of Statistical Packages and Data Documentation 4. Basics of Writing Batch Programs with Statistical Packages 5. Basic Concepts of Bivariate Regression 6. Basic Concepts of Multiple Regression 7. Dummy Variables 8. Interactions 9. Nonlinear Relationships 10. Indirect Effects and Omitted Variable Bias 11. Outliers, Heteroskedasticity, and Multicollinearity 12. Putting It All Together and Thinking About Where to Go Next