Regression Analysis for the Social Sciences / Edition 1

Regression Analysis for the Social Sciences / Edition 1

by Rachel A. Gordon
     
 

ISBN-10: 0415991544

ISBN-13: 9780415991544

Pub. Date: 02/11/2010

Publisher: Taylor & Francis

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

Overview

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.

This book is for a one-semester course. For a two-semester course, see www.routledge.com/books/details/9780415875363/

Product Details

ISBN-13:
9780415991544
Publisher:
Taylor & Francis
Publication date:
02/11/2010
Edition description:
Older Edition
Pages:
632
Product dimensions:
7.60(w) x 9.30(h) x 1.30(d)

Table of Contents

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

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