Dyadic Data Analysis

ISBN-10: 1572309865

ISBN-13: 9781572309869

Edition: 2006

Authors: David A. Kenny, Deborah A. Kashy, William L. Cook, Jeffry A. Simpson

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Part of the 'Methodology in the Social Sciences' series, this text covers clear definitions and intruitive examples in dyadic data.
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Book details

List price: $75.00
Copyright year: 2006
Publisher: Guilford Publications
Publication date: 7/28/2006
Binding: Hardcover
Pages: 458
Size: 6.25" wide x 9.25" long x 1.25" tall
Weight: 1.936
Language: English

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 ofPsychological 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. nbsp; Deborah A. Kashy, PhD, is Professor of Psychology at Michigan State University (MSU). She is currently senior associate editor ofPersonality and Social Psychology Bulletinand has also served as associate editor ofPersonal 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. nbsp; 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.

W. Steven Rholes, PhD, is Professor and Head of the Department of Psychology at Texas A&M University. He has conducted research programs in social cognition, children's social development, and adult attachment since receiving a degree in psychology from Princeton University in 1978. In 1992, along with his colleague Jeffry Simpson, Dr. Rholes published one of the first studies to confirm predictions about avoidant attachment style, using behavioral observations as evidence. For the past decade, the impact of attachment styles on emotional support sought and provided by members of romantic couples has been the central focus of his research program. Dr. Rholes has also served in two administrative positions, department chair and associate dean, during this period. Jeffry A. Simpson, PhD, is Professor of Psychology at the University of Minnesota. Formerly, he was Professor of Psychology at Texas A&M University. Dr. Simpson received his doctorate in psychology from the University of Minnesota in 1986. His research focuses on interpersonal relationships, evolution and social behavior, and social influence, and he serves as Associate Editor for the Journal of Personality and Social Psychology.

Basic Definitions and Overview
Basic Definitions
Distinguishability[superscript 1]
Between-Dyads, Within-Dyads, and Mixed Variables
Level of Measurement
Idiographic and Nomothetic Analyses
Dyadic Designs
Data Organization
Standard Design
Other Designs
A Database of Dyadic Studies
Overview of the Book
Summary and Conclusions
The Measurement of Nonindependence
Interval Level of Measurement
Distinguishable Members
Indistinguishable Members
Categorical Measures
Distinguishable Members
Indistinguishable Members
Consequences of Ignoring Nonindependence
What Not to Do
Power Considerations
Power of the Test of r
Power of Kappa
Summary and Conclusions
Analyzing Between- and Within-Dyads Independent Variables
Interval Outcome Measures and Categorical Independent Variables
One Between-Dyads Independent Variable
Multiple Between-Dyads Independent Variables
One Within-Dyads Independent Variable
Multiple Within-Dyads Variables
One Between-Dyads and One Within-Dyads Variable
General Case
Interval Outcome Measures and Interval Independent Variables
Between-Dyads Independent Variable
Within-Dyads Independent Variable
General Case
Categorical Outcome Variables
Within-Dyads Independent Variable
Between-Dyads Independent Variable
Summary and Conclusions
Using Multilevel Modeling to Study Dyads
Mixed-Model ANOVA
Multilevel-Model Equations
Multilevel Modeling with Maximum Likelihood
Adaptation of Multilevel Models to Dyadic Data
Computing the Intraclass Correlation and Variance Explained with Multilevel Models
Random Effects with Dyads
Distinguishable Dyads
Summary and Conclusions
Using Structural Equation Modeling to Study Dyads
Steps in SEM
Confirmatory Factor Analysis
Path Analyses with Dyadic Data
SEM for Dyads with Indistinguishable Members
Model Fit and Model Comparisons
Double-Entry Method
Summary and Conclusions
Tests of Correlational Structure and Differential Variance
Distinguishable Dyads
Equality of Variances
Equality of Correlations
Latent-Variable Correlations
Omnibus Test of Distinguishability
Categorical Variables
Indistinguishable Dyads
Latent Variable Correlations
Comparing Intraclass Correlations
Summary and Conclusions
Analyzing Mixed Independent Variables: The Actor-Partner Interdependence Model
The Model
Conceptual Interpretation of Actor and Partner Effects
The Relative Size of Actor and Partner Effects
Partner-Oriented Interaction Effects
Actor-Partner Interactions
Estimation of the APIM: Indistinguishable Dyad Members
Pooled-Regression Method
Estimating the APIM with Multilevel Analysis
Estimating the APIM with SEM
Estimation of the APIM: Distinguishable Dyads
Pooled-Regression Method
Multilevel Modeling
Structural Equation Modeling
Power and Effect Size Computation
Specification Error in the APIM
Summary and Conclusions
Social Relations Designs with Indistinguishable Members
The Basic Data Structures
Round-Robin Design
Block Design
Block-Round-Robin Designs
Other Designs
The SRM Components
Estimation of SRM Effects
The SRM Variances
The SRM Reciprocity Correlations
Self-Actor and Self-Partner Correlations
Actor and Partner Correlations with Individual-Difference Variables
Dyadic Analysis of Relationship Effects
Multivariate Correlations
Details of an SRM Analysis
Statistical Issues
Significance Testing
Model Assumptions
Social Relations Analyses: An Example
Summary and Conclusions
Social Relations Designs with Roles
SRM Studies of Family Relationships
Design and Analysis of Studies
The Model
The Components
Generalized and Dyadic Reciprocity
Estimation of Variance in the SRM with Roles
Family Subsystems
Application of the SRM with Roles Using Confirmatory Factor Analysis
The Four-Person Design
The Base Model
Illustration of the Four-Person Family Design
Model Evaluation
Parameter Estimates
Respecifications of the Base Model
The Three-Person Design
Base Model
Alternative Three-Person Models
Multiple Perspectives on Family Relationships
Means and Factor Score Estimation
Constraints on the SRM Effects
Estimation of SRM Effects for a Particular Family
Power and Sample Size
Summary and Conclusions
One-with-Many Designs
Design Issues
Measuring Nonindependence
Indistinguishable Partners
Distinguishable Partners
The Meaning of Nonindependence in the One-with-Many Design
Univariate Analysis with Indistinguishable Partners
Naive Analysis
Between-Within Analysis
Multilevel Analysis
Univariate Estimation with Distinguishable Partners
Repeated-Measures Analysis of Variance and MANOVA
Multilevel Analysis
Structural Equation Modeling
The Reciprocal One-with-Many Design
Indistinguishable Partners
Distinguishable Partners
Summary and Conclusions
Social Network Analysis
The Representation of a Network
Network Measures
The p[subscript 1] Model
The Model
Relation to the SRM
Another Look at the Sampson Data
Summary and Conclusions
Dyadic Indexes
Item Measurement Issues
Measures of Profile Similarity
Which Measure to Use?
Mean and Variance of the Dyadic Index
Average Measure
Tests of Heterogeneity
Stereotype Accuracy
Differential Endorsement of the Stereotype
Pseudo-Couple Analysis
Idiographic versus Nomothetic Analysis
Summary and Conclusions
Over-Time Analyses: Interval Outcomes
Cross-Lagged Regressions
Analysis Model
Computer Programs
Over-Time Standard APIM
Specification of Error Structure
Measurement of the Causal Variable
Growth-Curve Analysis
Definition of Time Zero
Functional Form
Structural Equation Modeling
Multilevel Modeling
General Error Models
Cross-Spectral Analysis
Nonlinear Dynamic Modeling
Summary and Conclusions
Over-Time Analyses: Dichotomous Outcomes
Sequential Analysis
The Model
Statistical Analysis of Sequential Data: Log-Linear Analysis
Logit Model
Significance Testing and Aggregation across Dyads
The Assumption of Stationarity in Time-Series Analysis
Beyond the Basic Analysis
Statistical Analysis of Sequential Data: Multilevel Modeling
Event-History Analysis
Censored Scores
Individual-Level Events
Discrete Analysis
Summary and Conclusions
Concluding Comments
Specialized Dyadic Models
Mutual Influence
Common-Fate Models
Going Beyond the Dyad
Network Autocorrelation
Conceptual and Practical Issues
The Meaning of Nonindependence
Design and Measurement Issues in Dyadic Research
Recruitment and Retention
The Seven Deadly Sins of Dyadic Data Analysis
The Last Word
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