Applied Multiple Regression - Correlation Analysis for the Behavioral Sciences

ISBN-10: 0805822232
ISBN-13: 9780805822236
Edition: 3rd 2011 (Revised)
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Description: This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of  More...

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Book details

List price: $110.00
Edition: 3rd
Copyright year: 2011
Publisher: Taylor & Francis Group
Publication date: 8/1/2002
Binding: Hardcover
Pages: 736
Size: 7.25" wide x 10.25" long x 1.75" tall
Weight: 3.300
Language: English

This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT. Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on multiple regression and correlational methods.

Stephen G. West (PhD, University of Texas at Austin) is Professor of Quantitative and Social Psychology at the University of Arizona. His current quantitative research interests include field research methods, structural equation modeling, multiple regression analysis, mediational analysis, graphics and exploratory data analysis, and longitudinal data analysis. Current social psychology research interests include personality research, applied social, prevention-related issues in health, mental health. He is the editor ofnbsp; Psychological Methods, published by APA.

Leona S. Aiken (PhD, Purdue University)nbsp;is Professor and Chair of Social and Quantitative Psychology at Arizona State University. Her research interests include both quantitative methods and health psychology. In quantitative methods, she is known for her work in continuous variable interactions in multiple regression.nbsp;She is also interested in the use of design approaches and mediational analysis to untangle the effects of individual components in multi-component interventions. In health psychology, she is interested in adoption of health protective behaviors across the life span, particularly among women, both from the perspectives of psychosocial models of the putative determinants of health protective behavior and from the perspective of interventions to increase health protective behavior.

Preface
Introduction
Multiple Regression/Correlation as a General Data-Analytic System
A Comparison of Multiple Regression/Correlation and Analysis of Variance Approaches
Multiple Regression/Correlation and the Complexity of Behavioral Science
Orientation of the Book
Computation, the Computer, and Numerical Results
The Spectrum of Behavioral Science
Plan for the Book
Summary
Bivariate Correlation and Regression
Tabular and Graphic Representations of Relationships
The Index of Linear Correlation Between Two Variables: The Pearson Product Moment Correlation Coefficient
Alternative Formulas for the Product Moment Correlation Coefficient
Regression Coefficients: Estimating Y From X
Regression Toward the Mean
The Standard Error of Estimate and Measures of the Strength of Association
Summary of Definitions and Interpretations
Statistical Inference With Regression and Correlation Coefficients
Precision and Power
Factors Affecting the Size of r
Summary
Multiple Regression/Correlation With Two or More Independent Variables
Introduction: Regression and Causal Models
Regression With Two Independent Variables
Measures of Association With Two Independent Variables
Patterns of Association Between Y and Two Independent Variables
Multiple Regression/Correlation With k Independent Variables
Statistical Inference With k Independent Variables
Statistical Precision and Power Analysis
Using Multiple Regression Equations in Prediction
Summary
Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I
Introduction
Some Useful Graphical Displays of the Original Data
Assumptions and Ordinary Least Squares Regression
Detecting Violations of Assumptions
Remedies: Alternative Approaches When Problems Are Detected
Summary
Data-Analytic Strategies Using Multiple Regression/Correlation
Research Questions Answered by Correlations and Their Squares
Research Questions Answered by B Or [beta]
Hierarchical Analysis Variables in Multiple Regression/Correlation
The Analysis of Sets of Independent Variables
Significance Testing for Sets
Power Analysis for Sets
Statistical Inference Strategy in Multiple Regression/Correlation
Summary
Quantitative Scales, Curvilinear Relationships, and Transformations
Introduction
Power Polynomials
Orthogonal Polynomials
Nonlinear Transformations
Nonlinear Regression
Nonparametric Regression
Summary
Interactions Among Continuous Variables
Introduction
Centering Predictors and the Interpretation of Regression Coefficients in Equations Containing Interactions
Simple Regression Equations and Simple Slopes
Post Hoc Probing of Interactions
Standardized Estimates for Equations Containing Interactions
Interactions as Partialed Effects: Building Regression Equations With Interactions
Patterns of First-Order and Interactive Effects
Three-Predictor Interactions in Multiple Regression
Curvilinear by Linear Interactions
Interactions Among Sets of Variables
Issues in the Detection of Interactions: Reliability, Predictor Distributions, Model Specification
Summary
Categorical or Nominal Independent Variables
Introduction
Dummy-Variable Coding
Unweighted Effects Coding
Weighted Effects Coding
Contrast Coding
Nonsense Coding
Coding Schemes in the Context of Other Independent Variables
Summary
Interactions With Categorical Variables
Nominal Scale by Nominal Scale Interactions
Interactions Involving More Than Two Nominal Scales
Nominal Scale by Continuous Variable Interactions
Summary
Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II
Introduction
Outliers: Introduction and Illustration
Detecting Outliers: Regression Diagnostics
Sources of Outliers and Possible Remedial Actions
Multicollinearity
Remedies for Multicollinearity
Summary
Missing Data
Basic Issues in Handling Missing Data
Missing Data in Nominal Scales
Missing Data in Quantitative Scales
Summary
Multiple Regression/Correlation and Causal Models
Introduction
Models Without Reciprocal Causation
Models With Reciprocal Causation
Identification and Overidentification
Latent Variable Models
A Review of Causal Model and Statistical Assumptions
Comparisons of Causal Models
Summary
Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model
Ordinary Least Squares Regression Revisited
Dichotomous Outcomes and Logistic Regression
Extensions of Logistic Regression to Multiple Response Categories: Polytomous Logistic Regression and Ordinal Logistic Regression
Models for Count Data: Poisson Regression and Alternatives
Full Circle: Parallels Between Logistic and Poisson Regression, and the Generalized Linear Model
Summary
Random Coefficient Regression and Multilevel Models
Clustering Within Data Sets
Analysis of Clustered Data With Ordinary Least Squares Approaches
The Random Coefficient Regression Model
Random Coefficient Regression Model and Multilevel Data Structure
Numerical Example: Analysis of Clustered Data With Random Coefficient Regression
Clustering as a Meaningful Aspect of the Data
Multilevel Modeling With a Predictor at Level 2
An Experimental Design as a Multilevel Data Structure: Combining Experimental Manipulation With Individual Differences
Numerical Example: Multilevel Analysis
Estimation of the Multilevel Model Parameters: Fixed Effects, Variance Components, and Level 1 Equations
Statistical Tests in Multilevel Models
Some Model Specification Issues
Statistical Power of Multilevel Models
Choosing Between the Fixed Effects Model and the Random Coefficient Model
Sources on Multilevel Modeling
Multilevel Models Applied to Repeated Measures Data
Summary
Longitudinal Regression Methods
Introduction
Analyses of Two-Time-Point Data
Repeated Measure Analysis of Variance
Multilevel Regression of Individual Changes Over Time
Latent Growth Models: Structural Equation Model Representation of Multilevel Data
Time Varying Independent Variables
Survival Analysis
Time Series Analysis
Dynamic System Analysis
Statistical Inference and Power Analysis in Longitudinal Analyses
Summary
Multiple Dependent Variables: Set Correlation
Introduction to Ordinary Least Squares Treatment of Multiple Dependent Variables
Measures of Multivariate Association
Partialing in Set Correlation
Tests of Statistical Significance and Statistical Power
Statistical Power Analysis in Set Correlation
Comparison of Set Correlation With Multiple Analysis of Variance
New Analytic Possibilities With Set Correlation
Illustrative Examples
Summary
Appendices
The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements
Determination of the Inverse Matrix and Applications Thereof
Appendix Tables
References
Glossary
Statistical Symbols and Abbreviations
Author Index
Subject Index

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