Regression Analysis for Social Sciences

Regression analysis is the most frequently employed method in applied statistics, and is in wide use by social scientists. There are many books on regression analysis, but most of them are aimed at statisticians, or are very large, expensive, handbooks that contain far more information, and in greater depth than might be wanted by the average social scientist. Regression Analysis for Social Scientists presents methods of regression analysis in an accessible way, with illustrations and examples for each method. A broad spectrum of methods are included. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate levels 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.

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.

• Presents accessible methods of regression analysis
• Includes a broad spectrum of methods
• Techniques are explained step-by-step
• Provides sample command and result files for SYSTAT

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