Longitudinal data are critical for understanding how individuals change across time. Researchers are faced with a complex task when modeling the contexts in which longitudinal processes unfold. Modeling Contextual Effects in Longitudinal Studies reviews the challenges and alternative approaches to modeling these influences 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 representedas 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.
Modeling Contextual Effects in Longitudinal Studies 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.
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. Johns University. His quantitative interests are structural equation modeling, longitudinal design and analysis, meta-analysis, and analyzing interdependent data.
