Longitudinal Multivariate Psychology Multivariate Applications Series

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.

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

Emilio Ferrer is Professor in the Department of Psychology at the University of California, Davis, and a member of the Graduate Groups in Biostatistics, Education, and Human Development. His research interests include methods to examine multivariate change and developmental processes.

Steven M. Boker is Professor of Quantitative Psychology at the University of Virginia, Director of the Human Dynamics Lab, Speaker-Director of the LIFE Academy, and Director of the Center for Dynamics of Healthy Development. His research concerns dynamical systems modeling, interpersonal communication, and methods for the study of development over the lifespan.

Kevin J. Grimm is Professor of Psychology at Arizona State University, and head of the Quantitative Research Methods PhD program. His research interests include longitudinal data analysis and data mining.