This is the companion website for Regression Analysis for the Social Sciences by Rachel A. Gordon, University of Illinois at Chicago.
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:
- Interweaving the teaching of statistical concepts with examples developed for the course from publicly-available social science data or drawn from the literature.
- Thorough integration of teaching statistical theory with teaching data processing and analysis.
- Teaching of both SAS and Stata “side-by-side” and use of chapter exercises in which students practice programming and interpretation on the same data set and course exercises in which students can choose their own research questions and data set.
The public area of this website contains the following major elements:
- Programs and results for examples based on the National Survey of Families and Households.
- Programs and results from other examples used within chapters.
The restricted area of this website contains the following major elements:
- The full-text of articles used in the Literature Excerpts.
- The answer keys for end-of-chapter Review Questions, Review Exercises, and Chapter Exercises (chapter exercises use the National Organizations Survey).
- Power-point slides for instructor use in lecturing.
Rachel A. Gordon is an Associate Professor in the Department of Sociology and the Institute of Government and Public Affairs at the University of Illinois at Chicago. Professor Gordon has multidisciplinary substantive and statistical training and a passion for understanding and teaching applied statistics.
What instructors are saying about the book?
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, accessibly 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
“A remarkable book! Every quantitative graduate student in a social science discipline needs to master a diverse arsenal of regression-based tools. By combining clear explanation with real-world SAS and STATA examples and exercises, Gordon provides just what the doctor ordered.”— Greg Duncan, Education, University of California, Irvine
“This book moves from quite simple techniques to more sophisticated regression methods with ease, and the guidance on SAS and STATA form a particularly valuable component of the text. The material is covered in an accessible yet thorough manner and will be a valuable resource for both students and more experienced researchers.”—Nathalie Noret, Health and Life Sciences, York St. John University, UK
“This is one of the best regression textbooks for social sciences on the market. All beginning quantitative researchers in social sciences—entering graduate students with different mathematical backgrounds and experienced researchers formerly not familiar with regression analysis—will greatly benefit from this book. I would highly recommend this book to my students and my fellow regression course professors.”—Tianfu Wang, Sociology, Tsinghua University, China