This volume reviews the challenges and alternative approaches to modeling how individuals change across time and provides methodologies and data analytic strategies for behavioral and social science researchers. This accessible guide provides concrete, clear examples of how contextual factors can be included in most research studies. Each chapter can be understood independently, allowing readers to first focus on areas most relevant to their work. The opening chapter demonstrates the various ways contextual factors are represented—as covariates, predictors, outcomes, moderators, mediators, or mediated effects. Succeeding chapters review "best practice" techniques for treating missing data, making model comparisons, and scaling across developmental age ranges. Other chapters focus on specific statistical techniques such as multilevel modeling and multiple-group and multilevel SEM, and how to incorporate tests of mediation, moderation, and moderated mediation. Critical measurement and theoretical issues are discussed, particularly how age can be represented and the ways in which context can be conceptualized. The final chapter provides a compelling call to include contextual factors in theorizing and research.
This book will appeal to researchers and advanced students conducting developmental, social, clinical, or educational research, as well as those in related areas such as psychology and linguistics.
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.