- Inbunden (Hardback)
- Antal sidor
- illustrated ed
- Guilford Publications
- Simpson, Jeffrey A. (foreword)
- 235 x 156 x 28 mm
- Antal komponenter
- 760 g
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Dyadic Data Analysis
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"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
Bloggat om Dyadic Data Analysis
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.
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