Longitudinal Research with Latent Variables

Since Charles Spearman published his seminal paper on factor analysis in 1904 and Karl Joresk ¨ og replaced the observed variables in an econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social and behavioural sciences. Indeed, the central va- ables that social and behavioural theories deal with, can hardly ever be identi?ed as observed variables. Statistical modelling has to take account of measurement - rors and invalidities in the observed variables and so address the underlying latent variables. Moreover, during the past decades it has been widely agreed on that serious causal modelling should be based on longitudinal data. It is especially in the ?eld of longitudinal research and analysis, including panel research, that progress has been made in recent years. Many comprehensive panel data sets as, for example, on human development and voting behaviour have become available for analysis. The number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only experience rejection just for that reason.

This book offers a detailed explanation of longitudinal research
methodology with latent variables, and shows how this methodology is implemented in practice with current software and real datasets. Covers specific approaches, their histories and their uses.

The main purpose of the book is to give a state of the art explanation of longitudinal research methodology with latent variables and to show how this methodology is implemented in practice with current state of art software and real datasets.

Loglinear Latent Variable Models for Longitudinal Categorical Data.- Random Effects Models for Longitudinal Data.- Multivariate and Multilevel Longitudinal Analysis.- Longitudinal Research Using Mixture Models.- An Overview of the Autoregressive Latent Trajectory (ALT) Model.- State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood.- Continuous Time Modeling of Panel Data by means of SEM.- Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data.- Structural Interdependence and Unobserved Heterogeneity in Event History Analysis.