Longitudinal Models in the Behavioral and Related Sciences

Written by distinguished experts in the field, this book presents longitudinal models and analysis procedures for use in the behavioral and social sciences and the technical problems that may be encountered along the way. The book opens with an overview of the latest theoretical developments, situations that arise due to the categorical nature of the data, issues related to state space modeling, and potential problems that may arise from network analysis and/or growth-curve data. The remainder of the book focuses on applications of longitudinal modeling in a variety of disciplines such as heterogeneity on the patterns of a firm's profit, on house prices, and on delinquent behavior; non-linearity in growth in assessing cognitive aging; measurement error issues in longitudinal research; and distance association for the analysis of change. Ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.

Kees van Montfort, PhD, is a Full Professor of Quantitative Research Techniques at the Free University in Amsterdam and at the Nyenrode Business University in the Netherlands. Johan Oud is an Associate Professor in Longitudinal Data Analysis at the Behavioural Science Institute at Radboud University Nijmegen, in the Netherlands. Albert Satorra is a Full Professor of Statistics at the University of Pompeu Fabra in Barcelona, Spain.

Preface. Part 1. Theoretical Developments. A. Mooijaart, K. van Montfort, Latent Markov Models for Categorical Variables and Time-Dependent Covariates. J. Oud, Comparison of Four Procedures to Estimate the Damped Linear Differential Oscillator for Panel Data. T. Snijders, C. Steglich, M. Schweinberger, Modeling the Coevolution of Networks and Behavior. H. Singer, Stochastic Differential Equation Models With Sampled Data. S-M. Chow, Factor Score and Parameter Estimations in Nonlinear Dynamical Systems Models. J. Vermunt, Growth Models for Categorical Response Variables: Standard, Latent-Class, and Hybrid Approaches. J.J. McArdle, Dynamic Structural Equation Modeling in Longitudinal Experimental Studies. S. Blozis, A Second-Order Structured Latent Curve Model for Longitudinal Data. Part 2. Applications. J-C. Bou, A. Satorra, Patterns of Persistence of Abnormal Returns: A Finite Mixture Distribution Approach. J. Reinecke, The Development of Deviant and Delinquent Behavior of Adolescents: Applications of Latent Class Growth Curves and Growth Mixtures Models. K.J. Grimm, J.J. McArdle, F. Hamagami, Nonlinear Growth Mixture Models in Research on Cognitive Aging. U. Engel, A. Gattig, J. Simonson, Longitudinal Multilevel Modelling: A Comparison of Growth Curve Models and Structural Equation Modelling Using Panel Data From Germany. E. Schlueter, E. Davidov, P. Schmidt, Applying Autoregressive Cross-Lagged and Latent Growth Curve Models to a Three-Wave Panel Study. I. Visser, V. Schmittmann, M.E.J. Raijmakers, Markov Process Models for Discrimination Learning. M. de Rooij, The Use of Covariates in Distance Association Models for the Analysis of Change. A. Scherpenzeel, W. Saris, Multitrait-Multimethod Models for Longitudinal Research. N. Longford, I. McCarthy, G. Dowse, Patterns of House-Price Inflation in New Zealand.