Ebook: Multiple Regression in Behavioral Research: Explanation and Prediction

Wadsworth Publishing, 1997. – 1072 p. – 3rd ed. – ISBN: 0030728312, 9780030728310Multiple Regression in Behavioral Research adopts a data-analysis approach to multiple regression. Pedhazur integrates design and analysis, and emphasizes learning by example and critiques of published research.Features:
Pedhazur places strong emphasis on the application of regression analysis to various research problems. He discusses major research studies and journal articles in conjunction with specific methods presented. This emphasis gives students a critical appreciation of how methods are used and abused in various research areas.
Chapters 11-15 discuss various coding methods for categorical variables. This detailed explanation teaches students the properties of different coding methods and their suitability for specific tasks.
Multiple Regression introduces path analysis and structural equation models, including indices of fit and decomposition of effects. This information, in Part 3, exposes students to recent developments in the field. New to this edition:
Pedhazur demonstrates applications of all analytic approaches with detailed analyses of simple numerical examples. He uses the most recent versions of BMDP, MINITAB, SAS, and SPSS, provides examples of the computer outputs, and comments on them in detail.
The latest versions of EQS and LISREL programs for the structural equation modeling are also introduced and used for analyzing causal models with single indicators (path analysis) and multiple indicators or unobserved variables.New chapters include:
Regression Diagnostics
Elements of Multilevel Analysis
Categorical Dependent Variable: Logistic Regression.Contents
Preface to the Third Edition
Preface to the Second Edition
Preface to the First Edition
Foundations of Multiple Regression Analysis
Overview
Simple Linear Regression and Correlation
Regression Diagnostics
Computers and Computer Programs
Elements of Multiple Regression Analysis: Two Independent Variables
General Method of Multiple Regression Analysis: Matrix Operations
Statistical Control: Partial and Semipartial Correlation
Prediction
Multiple Regression Analysis
Variance Partitioning
Analysis of Effects
A Categorical Independent Variable: Dummy, Effect, and Orthogonal Coding
Multiple Categorical Independent Variables and Factorial Designs
Curvilinear Regression Analysis
Continuous and Categorical Independent Variables - I: Attribute-Treatment Interaction; Comparing Regression Equations
Continuous and Categorical Independent Variables-II: Analysis of Covariance
Elements of Multilevel Analysis
Categorical Dependent Variable: Logistic Regression
Structural Equation Models
Structural Equation Models with Observed Variables: Path Analysis
Structural Equation Models with Latent Variables
Multivariate Analysis
Regression, Discriminant, and Multivariate Analysis of Variance: Two Groups
Canonical, Discriminant, and Multivariate Analysis of Variance: Extensions
Appendix: Matrix Algebra: An Introduction
Appendix: Tables
References
Index of Names
Index of Subjects