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"
