Modeling Contextual Effects in Longitudinal Studies

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 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. 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.