This book reviews methods of conceptualizing, measuring, and analyzing interdependent data in developmental and behavioral sciences. Quantitative and developmental experts describe best practices for modeling interdependent data that stem from interactions within families, relationships, and peer groups, for example. Complex models for analyzing longitudinal data, such as growth curves and time series, are also presented.
Many contributors are innovators of the techniques and all are able to clearly explain the methodologies and their practical problems including issues of measurement, missing data, power and sample size, and the specific limitations of each method.
Featuring a balance between analytic strategies and applications, the book addresses:
This book is intended for graduate students and researchers across the developmental, social, behavioral, and educational sciences. It is an excellent research guide and a valuable resource for advanced methods courses.
"There are relatively few guides for researchers who explore the interdependence of human functioning… This book will clearly rectify that limitation… This book… [is] …of great value to many psychologists… [and] for doctoral seminars in developmental psychology or biostatistics…I highly recommend this book." -Theresa Thorkildsen, University of Illinois, Chicago
"In its groundbreaking translation of multiple methods to its topic, this is a very important book for those who conduct developmental research on dyads and other interdependent groups. The book is essential for those planning to study development in dyadic or group relationships. As the authors cogently argue, to fail to account for change in the study of relationships is to misunderstand relationships, while the failure to account for relationships in the study of change just as reliably results in a failure to understand change. Thus, the book positions itself to guide researchers in a direction essential for the field of developmental psychology." - Clifton R. Emery, PsycCRITIQUES
N.A. Card, T.D. Little, J.P. Selig, Modeling Dyadic and Interdependent Data in Developmental Research: An Introduction. B. Laursen, D. Popp, W.J. Burk, M. Kerr, H. Stattin, Incorporating Interdependence into Developmental Research: Examples from the Study of Homophily and Homogeneity. W.L. Cook, Application of the Social Relations Model Formulas to Developmental Research. A.H.N. Cillessen, C. Borch, Analyzing Social Networks in Adolescence. N. Ram, A.B. Pedersen, Dyadic Models Emerging from the Longitudinal Structural Equation Modeling Tradition: Parallels with Ecological Models of Interspecific Interactions. E. Ferrer, K.F. Widaman, Multilevel Structural Equation Models for Contextual Factors with Inter-Group Differences. P. Sadler, E. Woody, It Takes Two: A Dyadic, SEM-Based Perspective on Personality Development. D.A. Kashy, M.B. Donnellan, Comparing MLM and SEM Approaches to Analyzing Developmental Dyadic Data: Growth Curve Models of Hostility in Families. J.P. Selig, K.A. McNamara, N.A. Card, T.D. Little, Techniques for Modeling Dependency in Interchangeable Dyads. T.E. Malloy, A.H.N. Cillessen, Variance Component Analysis of Generalized and Dyadic Peer Perceptions in Adolescence. N.A. Card, T.D. Little, J.P. Selig, Using the Bivariate Social Relations Model to Study Dyadic Relationships: Early Adolescents’ Perceptions of Friends’ Aggression and Prosocial Behavior. S.J.T. Branje, C. Finkenauer, W.H.J. Meeus, Modeling Interdependence Using the Social Relations Model: The Investment Model in Family Relationships. J. Templin, Methods for Detecting Subgroups in Social Networks. T.A. Kindermann, Can We Use Causal Inferences about the Influence of Children's Naturally-Existing Social Networks on their School Motivation? B.J.H. Zijlstra, R. Veenstra, M.A.J. Van Duijn, An Application of the Multilevel Model for Binary Network Data on Bully-Victim Relationships. C.F. Bond, Jr., D. Cross, Beyond the Dyad: Prospects for Social Development. D.A. Kenny, Thinking about the Developmental Course of Relationships.