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Regression Analysis for the Social Sciences, by Rachel A. Gordon
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Provides graduate students in the social sciences with the basic skills 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 Stata and use of chapter exercises in which students practice programming and interpretation on the same data set. A separate set of exercises allows students to select a data set to apply the concepts learned in each chapter to a research question of interest to them, all updated for this edition.
- Sales Rank: #1047656 in Books
- Published on: 2015-03-19
- Original language: English
- Number of items: 1
- Dimensions: 9.20" h x 1.10" w x 7.40" l, .0 pounds
- Binding: Paperback
- 566 pages
Review
Regression Analysis for the Social Sciences is a well-designed textbook for upper-level undergraduate and graduate-level courses in social statistics. The textbook achieves a seamless balance between theory and practice. It provides a gentle yet thorough review of statistical theory of regression models, all the while focusing on practical application of regression models. The textbook provides an indispensable guide for learning the complexities and mechanics of regression models and analysis in the social sciences.
-Steven Prus, Sociology and Anthropology, Carleton University
Teaching graduate statistics in the social sciences is challenging because our students arrive with such diverse levels of mathematics preparation. Rachel Gordon does an exceptional job of explaining complex statistical concepts using straightforward language and intuitive, relevant, examples. This book allows students with a range of quantitative backgrounds and comfort levels to understand and apply the material, and builds their confidence in using statistics.
-Amy Kate Bailey, Sociology, University of Illinois at Chicago
Gordon’s textbook provides an insightful and contemporary approach to methodological instruction in the social sciences. It engages current strategies for modelling social behavior using regression, while at the same time providing plenty of applied orientation to allow the student to acquire pragmatic knowledge of how best to tackle contemporary research problems. The book is distinguished for its ample use of excerpts from recent social science literature and its attention to a key range of pragmatic issues faced by investigators. The thorough integration of a major statistical package (Stata), including examples of code, links to datasets, and illustrative results, is another feature that will be widely appreciated. For instructors and students often frustrated by other texts – too simplistic or too formal, or emphasizing examples far from current social science – Gordon’s Regression Analysis for the Social Sciences will likely fit the bill.
- Michael J White, Sociology, Brown University
About the Author
Rachel A. Gordon is�Professor in the Department of Sociology and Associate Director of 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.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Highly recommend
By Michael F.
This book goes over all aspects of basic ordinary least squares (OLS) regression, assuming a basic statistics background. Gordon uses Stata as her software of choice, but includes an appendix of translating commands across SPSS, SAS, and Stata for users of those other programs. Unlike some other statistics books that I have used as a graduate student in the social sciences, this book combines both software commands and actual theory to teach you not only how to execute a regression, but also to fundamentally understand where these coefficients come from and how to interpret them. Gordon covers bivariate and multivariate regression, dummy variables, interactions, nonlinearity, and regression assumptions as well as a basic introduction to Stata.
This book requires no previous knowledge of Stata or regression, and so is a great book for beginners looking to sink their teeth into something. Gordon does include some basic proofs, which some students may find confusing, but it is often clear that the student should understand the underlying principle instead of the algebraic proof.
This book does NOT cover over forms of regression (logit, probit, etc.), so just be aware.
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