Modeling Contextual Effects in Longitudinal Studies

Todd D. Little is Director of the Research Design Unit and the Quantitative Psychology Doctoral training Program and a Professor of Psychology at the University of Kansas. He received his Ph.D. in developmental and quantitative psychology at the University of California - Riverside. Dr. Little has extensive experience in the use of longitudinal research methods, and he has edited several LEA books on the subject.James A. Bovaird is an Assistant Professor in Educational Psychology at the University of Nebraska - Lincoln. He received his Ph.D. in quantitative psychology at the University of Kansas. His quantitative interests are in the application of latent variable methodologies to novel substantive areas and the evaluation of these methodologies in situations of limited inference.Noel A. Card is an Assistant Professor in the Division of Family Studies and Human Development at the University of Arizona. He received his Ph.D. in clinical psychology from St. John’s University. His quantitative interests are structural equation modeling, longitudinal design and analysis, meta-analysis, and analyzing interdependent data.

• Contents: Preface. N.A. Card, T.D. Little, J.A. Bovaird, Modeling Ecological and Contextual Effects in Longitudinal Studies of Human Development. S.M. Hofer, L. Hoffman, Statistical Analysis With Incomplete Data: A Developmental Perspective. K.J. Preacher, L. Cai, R.C. MacCullum, Alternatives to Traditional Model Comparison Strategies for Covariance Structure Models. S.E. Embretson, Impact of Measurement Scale in Modeling Developmental Processes and Ecological Factors. P.J. Curran, M.C. Edwards, R.J. Wirth, A.M. Hussong, L. Chassin, The Incorporation of Categorical Measurement Models in the Analysis of Individual Growth. T.D. Little, N.A. Card, D.W. Slegers, E.C. Ledford, Representing Contextual Effects in Multiple-Group MACS Models. J.A. Bovaird, Multilevel Structural Equation Models for Contextual Factors. D. Hedeker, R.J. Mermelstein, Mixed-Effects Regression Models With Heterogeneous Variance: Analyzing Ecological Momentary Assessment (EMA) Data of Smoking. T.D. Little, N.A. Card, J.A. Bovaird, K.J. Preacher, C.S. Crandel, Structural Equation Modeling of Mediation and Moderation With Contextual Factors. D.B. Flora, S.T. Khoo, L. Chassin, Moderating Effects of a Risk Factor: Modeling Longitudinal Moderated Mediation in the Development of Adolescent Heavy Drinking. D.J. Bauer, M.J. Shanahan, Modeling Complex Interactions: Person-Centered and Variable-Centered Approaches. N. Bolger, P.E. Shrout, Accounting for Statistical Dependency in Longitudinal Data on Dyads. S.M. Boker, J-P. Laurenceau, Coupled Dynamics and Mutually Adaptive Context. N. Ram, J.R. Nesselroade, Modeling Intraindividual and Intracontextual Change: Rendering Developmental Contextualism Operational. J.L. Rodgers, The Shape of Things to Come: Using Developmental Curves From Adolescent Smoking and Drinking Reports to Diagnose the Type of Social Process that Generated the Curves. K.J. Grimm, J.J. McArdle, A Dynamic Structural Analysis of the Impacts of Context on Shifts in Lifespan Development. K.F. Widaman, Intrauterine Environment Affects Infant and Child Intellectual Outcomes: Environment as Direct Effect. H. Jelicic, C. Theokas, E. Phelps, R.M. Lerner, Conceptualizing and Measuring the Context Within Person Context Models of Human Development: Implications for Theory, Research, and Application.