Regression Analysis for Social Sciences

Erscheinungsjahr 1998
ISBN: 978-0-12-724955-1
Verlag: Academic Press

Buch, Englisch, 386 Seiten, Format (B × H): 152 mm x 229 mm

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

Zielgruppe

Academics, researchers, and students in the social sciences including psychology and sociology.

Autoren/Hrsg.

von Eye, Alexander

Schuster, Christof

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Gesellschaftstheorie
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

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