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Full Description
This volume presents a collection of chapters focused on the study of multivariate change. As people develop and change, multivariate measurement of that change and analysis of those measures can illuminate the regularities in the trajectories of individual development, as well as time-dependent changes in population averages. As longitudinal data have recently become much more prevalent in psychology and the social sciences, models of change have become increasingly important. This collection focuses on methodological, statistical, and modeling aspects of multivariate change and applications of longitudinal models to the study of psychological processes.
The volume is divided into three major sections: Extension of latent change models, Measurement and testing issues in longitudinal modeling, and Novel applications of multivariate longitudinal methodology. It is intended for advanced students and researchers interested in learning about state-of-the-art techniques for longitudinal data analysis, as well as understanding the history and development of such techniques.
Contents
Preface
John Nesselroade
Introduction
Emilio Ferrer, Steve Boker, & Kevin Grimm
Section I: Extensions of latent change models
CH 01: Sy-Miin Chow: Methodological issues and extensions to the latent difference score framework
CH 02: Emilio Ferrer: Discrete- and semi-continuous time latent change score models of fluid reasoning development from childhood to adolescence
CH 03: Kevin Grimm & Ross Jacobucci: Individually-varying time metrics in latent change score models
CH 04: Aki Hamagami: Latent change score models with curvilinear constant bases
CH 05: Ross Jacobucci & Kevin Grimm: Regularized estimation of multivariate latent change score models
CH 06: Steve Boker: The Reticular Action Model: A remarkably lasting achievement
Section II: Measurement and testing issues in longitudinal modeling
CH 07: Sarfaraz Serang: Small sample corrections to model fit criteria for latent change score models
CH 08: Lijuan Wang & Miao Yang: Effects of over-simplified covariance structures on fixed effects inference in linear growth curve modeling
CH 09: Zhiyong Zhang & Haiyan Liu: Sample size and measurement occasion planning for latent change score models through Monte Carlo simulation
CH 10: Tim Hayes: Investigating the performance of CART- and random forest-based procedures for dealing with longitudinal dropout in small sample designs under MNAR missing data
CH 11: Ryne Estabrook: From factors of curves to factors of change
Section III: Novel applications of multivariate longitudinal methodology
CH 12: Ryan Bowles: The role of interval measurement in developmental studies
CH 13: Nilam Ram: Growth modeling using the differential form: Translations from study of fish growth
CH 14: Mike Neale: Modeling change with data collected from relatives
CH 15: Tom Paskus & Todd Petr: Making the cut: How a quantitative psychologist changed college sports
CH 16: Earl Hishinuma et al.: A successful consultation "team" model applying contemporary advanced statistics to minority research centers
Summary and General Conclusions
Emilio Ferrer, Steve Boker, & Kevin Grimm
