Dyadic Data Analysis: (Methodology in the Social Sciences)

Interpersonal phenomena such as attachment, conflict, person perception, helping, and influence have traditionally been studied by examining individuals in isolation, which falls short of capturing their truly interpersonal nature. This book offers state-of-the-art solutions to this age-old problem by presenting methodological and data-analytic approaches useful in investigating processes that take place among dyads: couples, coworkers, or parent-child, teacher-student, or doctor-patient pairs, to name just a few. Rich examples from psychology and across the behavioral and social sciences help build the researcher's ability to conceptualize relationship processes; model and test for actor effects, partner effects, and relationship effects; and model the statistical interdependence that can exist between partners. The companion website provides clarifications, elaborations, corrections, and data and files for each chapter.

Table of Contents:
1. Basic Definitions and Overview Nonindependence Basic Definitions Data Organization A Database of Dyadic Studies 2. The Measurement of Nonindependence Interval Level of Measurement Categorical Measures Consequences of Ignoring Nonindependence What Not to Do Power Considerations 3. Analyzing Between- and Within-Dyads Independent Variables Interval Outcome Measures and Categorical Independent Variables Interval Outcome Measures and Interval Independent Variables Categorical Outcome Variables 4. Using Multilevel Modeling to Study Dyads Mixed-Model ANOVA Multilevel-Model Equations Multilevel Modeling with Maximum Likelihood Adaptation of Multilevel Models to Dyadic Data 5. Using Structural Equation Modeling to Study Dyads Steps in SEM Confirmatory Factor Analysis Path Analyses with Dyadic Data SEM for Dyads with Indistinguishable Members 6. Tests of Correlational Structure and Differential Variance Distinguishable Dyads Indistinguishable Dyads 7. Analyzing Mixed Independent Variables: The Actor–Partner Interdependence Model The Model Conceptual Interpretation of Actor and Partner Effects Estimation of the APIM: Indistinguishable Dyad Members Estimation of the APIM: Distinguishable Dyads Power and Effect Size Computation Specification Error in the APIM 8. Social Relations Designs with Indistinguishable Members The Basic Data Structures Model Details of an SRM Analysis Model Social Relations Analyses: An Example 9. Social Relations Designs with Roles SRM Studies of Family Relationships Design and Analysis of Studies The Model Application of the SRM with Roles Using Confirmatory Factor Analysis The Four-Person Design Illustration of the Four-Person Family Design The Three-Person Design Multiple Perspectives on Family Relationships Means and Factor Score Estimation Power and Sample Size 10. One-with-Many Designs Design Issues Measuring Nonindependence The Meaning of Nonindependence in the One-with-Many Design Univariate Analysis with Indistinguishable Partners Univariate Estimation with Distinguishable Partners The Reciprocal One-with-Many Design 11. Social Network Analysis Definitions The Representation of a Network Network Measures The p1 12. Dyadic Indexes Item Measurement Issues Measures of Profile Similarity Mean and Variance of the Dyadic Index Stereotype Accuracy Differential Endorsement of the Stereotype Pseudo-Couple Analysis Idiographic versus Nomothetic Analysis Illustration 13. Over-Time Analyses: Interval Outcomes Cross-Lagged Regressions Over-Time Standard APIM Growth-Curve Analysis Cross-Spectral Analysis Nonlinear Dynamic Modeling 14. Over-Time Analyses: Dichotomous Outcomes Sequential Analysis Statistical Analysis of Sequential Data: Log-Linear Analysis Statistical Analysis of Sequential Data: Multilevel Modeling Event-History Analysis 15. Concluding Comments Specialized Dyadic Models Going Beyond the Dyad Conceptual and Practical Issues The Seven Deadly Sins of Dyadic Data Analysis The Last Word

About the Author :
David A. Kenny, PhD, is Board of Trustees Professor in the Department of Psychology at the University of Connecticut, and he has also taught at Harvard University and Arizona State University. He served as first quantitative associate editor of Psychological Bulletin. Dr. Kenny was awarded the Donald Campbell Award from the Society of Personality and Social Psychology. He is the author of five books and has written extensively in the areas of mediational analysis, interpersonal perception, and the analysis of social interaction data. Deborah A. Kashy, PhD, is Professor of Psychology at Michigan State University (MSU). She is currently senior associate editor of Personality and Social Psychology Bulletin and has also served as associate editor of Personal Relationships. In 2005 Dr. Kashy received the Alumni Outstanding Teaching Award from the College of Social Science at MSU. Her research interests include models of nonindependent data, interpersonal perception, close relationships, and effectiveness of educational technology. William L. Cook, PhD, is Associate Director of Psychiatry Research at Maine Medical Center and Spring Harbor Hospital, and Clinical Associate Professor of Psychiatry at the University of Vermont College of Medicine. Originally trained as a family therapist, he has taken a lead in the dissemination of methods of dyadic data analysis to the study of normal and disturbed family systems. Dr. Cook’s contributions include the first application of the Social Relations Model to family data, the application of the Actor-Partner Interdependence Model to data from experimental trials of couple therapy, and the development of a method of standardized family assessment using the Social Relations Model.

Review :
'This book breaks entirely new ground and, for the first time, offers social scientists a detailed methodological armamentarium for the analysis of dyadic data that appear in a broad range of research contexts. The development of original and creative solutions to some of the most vexing problems in dyadic research is presented in a clear, accessible manner by these talented authors. Dyadic Data Analysis is destined to become a classic, and will be essential reading for advanced students and researchers studying dyadic phenomena.'" - Tom Malloy, Rhode Island College, USA" 'An excellent, accessible, and instructive guide to dyadic data analysis. The authors clearly explain why interdependent data is problematic when approached with classical statistical techniques. More importantly, however, they enlighten the reader about the hidden treasures and opportunities that are inherent in dyadic data. This book provides a clear survey of various analytic techniques that researchers can use to ask and answer questions about the dynamics of interpersonal interactions, and it provides an engaging review of interdisciplinary applications of dyadic data designs.' -" Todd D. Little, Department of Psychology and Schiefelbusch Institute for Life Span Studies, University of Kansas, USA"