This volume reviews longitudinal models and analysis procedures for use in the behavioral and social sciences. Written by distinguished experts in the field, the book presents the most current approaches and theories, and the technical problems that may be encountered along the way. Readers will find new ideas about the use of longitudinal analysis in solving problems that arise due to the specific nature of the research design and the data available.
Longitudinal Models in the Behavioral and Related Sciences opens with the latest theoretical developments. In particular, the book addresses 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 focus of part two is on the application of longitudinal modeling in a variety of disciplines. The book features applications 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. Part two clearly demonstrates the caution that should be taken when applying longitudinal modeling as well as in the interpretation of the results. This new volume is ideal for advanced students and researchers in psychology, sociology, education, economics, management, medicine, and neuroscience.
Table of Contents
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.
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.