Researchers are faced with a complex task when modeling the contexts in which longitudinal processes unfold. This work reviews the challenges and alternative approaches to modeling these influences and provides methodologies and data analytic strategies for behavioral and social science researchers.
This volume reviews the various 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 examples of how contextual factors can be included in research studies. 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 techniques such as multilevel modeling, multiple-group and multilevel SEM, and how to incorporate tests of mediation. Critical measurement and theoretical issues are discussed, particularly how age can be represented and the ways in which context can be conceptualized. 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.
