Dyadic Data Analysis

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Overview

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.
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Editorial Reviews

From the Publisher

"Everyone who studies interpersonal processes should have this book on their shelves. Researchers following the analytical strategies laid out in this book need only to cite this book and its authors to validate their analyses. In addition, the authors describe the analyses under various kinds of conditions (for example, distinguishable versus nondistinguishable dyads), using different estimation techniques (ordinary least squares, maximum likelihood, etc.) and different software packages."--Linda Albright, Westfield State College

"If any researcher (faculty or student) asked me for advice on dyadic data, I would send him or her to this book first. It provides clear definitions, accessible reviews of topics that appear in research journals, intuitive examples, and illustrations with computer code. The authors are to be commended for taking such difficult topics and communicating them in an accessible manner."--Richard Gonzalez, University of Michigan

"An excellent, accessible, and instructive guide to dyadic data analysis. The authors clearly explain why interdependent data are 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, University of Kansas

"A wonderful addition to every researcher's tool chest for studying social relations and social interaction. The authors provide a systematic treatment of a wide variety of statistical and methodological issues that arise in handling research data gathered in the context of two-person interactions. What makes their book so useful is the array of subtle issues they discuss, from when to treat dyadic members as distinguishable or as indistinguishable, to how to array data for dyadic analyses. The kinds of questions examined--from the minute to the sweeping--indicate that this book is written by people with substantial experience in social relations research. Of special value, the authors provide useful guidance on the question of nonindependence by showing how the issue can be treated both within mixed models from the analysis of variance and in newer multilevel models. They do not avoid adding the complication of replicated observations, providing a book that ultimately covers nearly all the complexities of analyzing two-person social relations data. I predict this book will be a long-lived reference tool that all serious researchers in social relations will consult regularly."--Joseph N. Cappella, University of Pennsylvania

"This is a well-written and thoroughgoing discussion of issues and approaches in the analysis of dyadic data, written by leaders in the field. Dyadic data is a commonly found data structure in social psychology and social relations research. The authors describe and demonstrate several statistical methods, including multilevel and structural equation modeling approaches. The book would be appropriate for advanced undergraduate social psychology methods classes, as well as graduate seminars. I strongly recommend this text to every social relations and social psychology researcher. I expect it will soon become a widely cited classic."--Bruno D. Zumbo, University of British Columbia
 
"I have relied on the work of Kenny and his colleagues for many years. For anyone who studies family and relationships and who wants to stay up to date on the most effective ways to analyze quantitative data, this book is a 'must read.'"--Suzanne Bartle-Haring, PhD, Director, Couple and Family Therapy Program, The Ohio State University

 

Journal of Social and Clinical Psychology

"It will help researchers to formulate new ways of addressing old research questions in a more elegant and comprehensive manner....Destined to become a classic methodology text and will hold valued space on the bookshelves of methodologists and researchers of social concerns."--Journal of Social and Clinical Psychology
Journal of Anthropological Research

"An important source for any social scientist who has ever analyzed data involving pairs of people. Journal editors and reviewers are also recommended to know the contents of this book, because it will likely result in new requirements for publishing dyadic research that are essential as reliability and effect-size....[A] clear and purposeful text for detailed how-to instructions on specific analytical techniques....Kenny et al. make an invaluable contribution by walking readers through ANOVA, SEM, regression, and time-series analyses involving dyads, complete with SPSS and SAS syntax....The book ends with an eye-opening summary of 'the seven deadly sins of dyadic data analysis' (p. 421) that may serve as an excellent starting point for readers with immediate analytic concerns....Thorough, comprehensive, and clear."--Journal of Anthropological Research
PsycCRITIQUES

"By providing readers with a well-written, nontechnical, description of various forms of interdependence and how they can be quantitatively analyzed, the authors make a tremendous advancement of our field. Simply put, this book has the potential to drastically advance the ways that researchers conceptualize, design studies, and analyze data involving interdependent processes....The most important contribution of this book lies in the fact that it is the first comprehensive treatment of how researchers can quantitatively analyze various patterns of interdependent data....The book is exceptionally well written. The authors have clearly made great efforts to ensure that the book is straightforward and accessible by writing in an extremely clear and engaging manner....I consider this book essential reading for any psychologist who claims to have adequate training in data analysis....Valuable reading both early in psychologists' training, perhaps immediately after introductory courses in multivariate statistics, and later in psychologists' training after they have more exposure to advanced quantitative methods....More seasoned researchers will also find this book valuable and may even wish this book had been available earlier in their careers....This book has the very real possibility of sparking an important change in how researchers conceptualize, study, and quantitatively analyze interdependent processes....I expect that this book will have a substantial impact on the field and highly recommend it to all psychologists who study interdependent phenomena."--PsycCRITIQUES
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Product Details

  • ISBN-13: 9781572309869
  • Publisher: Guilford Publications, Inc.
  • Publication date: 8/14/2006
  • Series: Methodology In The Social Sciences Series
  • Edition description: 1ST
  • Pages: 458
  • Sales rank: 645,206
  • Product dimensions: 6.00 (w) x 9.10 (h) x 1.20 (d)

Meet 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.

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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

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