This book combines longitudinal research and latent variable research, i.e. it explains how longitudinal studies with objectives formulated in terms of latent variables should be carried out, with an emphasis on detailing how the methods are applied. Because longitudinal research with latent variables currently utilizes different approaches with different histories, different types of research questions, and different computer programs to perform the analysis, the book is divided into nine chapters. Starting from (a) some background information about the specific approach (a short history and the main publications), each chapter then (b) describes the type of research questions the approach is able to answer, (c) provides statistical and mathematical explanations of the models used in the data analysis, (d) discusses the input and output of the programs used, and (e) provides one or more examples with typical data sets, allowing the readers to apply the programs themselves. 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 combines longitudinal research and letent variable research, i.e. it explains how longitudinal studies with objectives formulated in terms of latent variable should be carried out, with an emphasis on detailing how the methods are applied. Because longitudinal research with latent variables currently utilizes different approaches with different histories, different types of research questions, and different computer programs to perform the analysis, the book is divided into nine chapters. Starting with (a) some back-ground information about the specific approach (a short history and the main publications), each chapter then (b) describes the type of research questions the approach is able to answer, (c) provides statistical and mathematical explanations of the models used in the data analysis, (d) discusses the input and output of the programs used, and (e) provides one or more examples with typical data sets, allowing the readers to apply the programs themselves. --Book Jacket Front Matter....Pages i-xi Loglinear Latent Variable Models for Longitudinal Categorical Data....Pages 1-36 Random Effects Models for Longitudinal Data....Pages 37-96 Multivariate and Multilevel Longitudinal Analysis....Pages 97-117 Longitudinal Research Using Mixture Models....Pages 119-152 An Overview of the Autoregressive Latent Trajectory (ALT) Model....Pages 153-176 State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood....Pages 177-199 Continuous Time Modeling of Panel Data by means of SEM....Pages 201-244 Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data....Pages 245-273 Structural Interdependence and Unobserved Heterogeneity in Event History Analysis....Pages 275-301
