Regression Analysis for Social SciencesBy
- Alexander von Eye, Michigan State University, East Lansing, U.S.A.
- Christof Schuster, University of Michigan, Ann Arbor, U.S.A.
Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text.
Academics, researchers, and students in the social sciences including psychology and sociology.
Paperback, 386 Pages
Published: June 1998
Imprint: Academic Press
"Individuals in the social and behavioral sciences as well as those with biological and biometrical backrounds would benefit from this book. Recommended. Upper-division undergraduates through faculty."
--D.J. Gougeon, University of Scranton, in CHOICE, February 1999
- Preface.Introduction.Simple Linear Regression.Multiple Linear Regression.Categorical Predictors.Outlier Analysis.Residual Analysis.Polynomial Regression.Multicollinearity.Multiple Curvilinear Regression.Interaction Terms in Regression.Robust Regression.Symmetric Regression.Variable Selection Techniques.Regression for Longitudinal Data.Piecewise Regression.Dichotomous Criterion Variables.Computational Issues.Elements of Matrix Algebra.Basics of Differentiation.Basics of Vector Differentiation.Polynomials.Data Sets.References.Index.