Interaction Effects in Multiple Regression (Quantitative Applications in the Social Sciences v. 72 2nd Revised edition)
By: Robert Turrisi (author), Choi K. Wan (author), James J. Jaccard (author)Paperback
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"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 Second Edition will expand the coverage on the analysis of three-way interactions in multiple regression analysis.
James Jaccard is currently Professor of Social Work; Associate Dean for Research; Co-director, Center for Latino Adolescent and Family Health in the NYU Silver School of Social Work. He received his doctoral degree from the University of Illinois in 1976 and is the director of the Institute for Child Health and Development at Florida International University in Miami. He previously was a distinguished research professor at the State University of New York in Albany, where he was in both the department of psychology and the School of Social Welfare. Dr. Jaccard was trained as a social-developmental scientist with specialties in attitude change and decision making, particularly as applied to young adolescents. Dr. Jaccard's research focuses on adolescent problem behaviors related to unintended pregnancy and substance use. He has developed programs for parents of adolescents to teach parents how to more effectively communicate and parent their children so as to reduce the risk of unintended pregnancies and problems due to substance use. He was involved in the seminal work on the influential Theory of Reasoned Action and has developed several effective parent-based interventions to prevent adolescent risk behaviors. Dr. Jaccard was one of the designers of the National Longitudinal Study of Adolescent Health (Add Health), which interviewed over 20,000 adolescents and their mothers in a multi-wave wave panel design. Add Health is one of the largest and most influential secondary data bases on adolescent health in the United States. Dr. Jaccard also has an extensive background in psychometrics and statistical methods. He 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 is currently developing a general framework for statistical analysis that eschews p values and focuses instead on magnitude estimation and margins of error. He in on the editorial board of the Journal of the Society for Social Work and reviews quantitative applications to social work research for the journal. Finally, Dr. Jaccard has written about theory construction and how to build conceptual models. He recently completed a book with Professor Jacob Jacoby that gives social scientists practical, hands-on approaches for generating ideas and translating them into coherent theories.
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
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- ID: 9780761927426
2nd Revised edition
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