Regression analysis for the social sciences
"This book provides graduate students in the social sciences with the basic skills that they need in order 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 analys…
Read More ↓Title Statements
- Full title :Regression analysis for the social sciences / Rachel A. Gordon, University of Illinois at Chicago.
Summary
"This book provides graduate students in the social sciences with the basic skills that they need in order 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 using Stata; - 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"--
Read More ↓Edition Statements
- Publication Date:2015.
- Edition:Second edition
Publication Statements
- Publisher(s):Routledge Taylor & Francis Group
- Place of Publication:New York
- Language:eng
- Publication Date:2015.
The Physical Object
- Description:xxi, 543 pages : : illustrations ; : 25 cm
Edition Identifiers
- ISBN:9781138810532, 1138810533, 9781138812512, 113881251X
- oclc:ocn888401160
- LC Classification:H61
- Dewey Decimal:300
Table of Contents
Examples of social science research using regression analysis -- Planning a quantitative research project with existing data -- Basic features of statistical packages and data documentation -- Basics of writing batch programs with statistical packages -- Basic concepts of bivariate regression -- Basic concepts of multiple regression -- Dummy variables -- Interactions -- Nonlinear relationships -- Indirect effects and omitted variable bias -- Outliers, heteroskedasticity, and multicollinearity -- Putting it all together and thinking about where to go next.
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