Interaction Effects in Multiple Regression: (72 Quantitative Applications in the Social Sciences)

Interaction Effects in Multiple Regression has provided students and researchers with a readable and practical introduction to conducting analyses of interaction effects in the context of multiple regression. The new addition will expand the coverage on the analysis of three way interactions in multiple regression analysis.

Table of Contents:
Series Editor's Introduction Preface Chapter 1: Introduction The Concept of Interaction Simple Effects and Interaction Contrasts Simple Effects Interaction Contrasts A Review of Multiple Regression The Linear Model Hierarchical Regression Categorical Predictors and Dummy Variables Predicted Values in Multiple Regression Transformations of the Predictor Variables Overview of Book Chapter 2: Two-Way Interactions Regression Models with Product Terms Two Continuous Predictors The Traditional Regression Strategy The Form of the Interaction Interpreting the Regression Coefficients for the Product Term Interpreting the Regression Coefficients for the Component Terms Significance Tests and Confidence Intervals Multicollinearity Strength of the Interaction Effect A Numerical Example Graphical Presentation A Qualitative Predictor and a Continuous Predictor A Qualitative Moderator Variable A Continuous Moderator Variable More Than Two Groups for the Qualitative Variable Form of the Interaction Summary Chapter 3: Three-Way Interactions Three Continuous Predictors Qualitative and Continuous Predictors A Continuous Focal Independent Variable A Qualitative Focal Independent Variable Qualitative Variables with More than Two Levels Summary Chapter 4: Additional Considerations Selected Issues The BiLinear Nature of Interactions for Continuous Variables Calculating Coefficients of Focal Independent Variables at Different Moderator Values Partialing the Component Terms Transformations Multiple Interaction Effects Standardized and Unstandardized Coefficients Metric Properties Measurement Error Robust Analyses and Assumption Violations Within-Subject and Repeated-Measure Designs Ordinal and Disordinal Interactions Regions of Significance Confounded Interactions Optimal Experimental Designs and Statistical Power Covariates Control for Experimentwise Errors Omnibus Tests and Interaction Effects Some Common Misapplications Interaction Models with Clustered Data and Random Coefficient Models Continuous Versus Discrete Predictor Variables The Moderator Framework Revisited References Notes About the Authors

About the Author :
Dr. James Jaccard is Professor of Social Work at New York University Silver School of Social Work. He received his doctoral degree from the University of Illinois, Urbana, in 1976. Dr. Jaccard’s research focuses on adolescent and young adult problem behaviors, particularly those related to unintended pregnancy and substance use, broadly defined. He has developed parent-based interventions to teach parents how to more effectively communicate and parent their adolescent children so as to reduce the risk of unintended pregnancies and problems due to substance use. Dr. Jaccard has written numerous books and articles on the analysis of interaction effects in a wide range of statistical models and teaches advanced graduate courses on structural equation modeling. He has written influential articles on the issue of arbitrary metrics in social science research. Dr. Jaccard also has written about theory construction and how to build conceptual models in a book published by Guilford Press.