Longitudinal Research with Latent Variables

1. Front Matter

   Pages i-xi

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2. Loglinear Latent Variable Models for Longitudinal Categorical Data

   • Jacques A. Hagenaars

   Pages 1-36

3. Random Effects Models for Longitudinal Data

   • Geert Verbeke, Geert Molenberghs, Dimitris Rizopoulos

   Pages 37-96

4. Multivariate and Multilevel Longitudinal Analysis

   • Nicholas T. Longford

   Pages 97-117

5. Longitudinal Research Using Mixture Models

   • Jeroen K. Vermunt

   Pages 119-152

6. An Overview of the Autoregressive Latent Trajectory (ALT) Model

   • Kenneth A. Bollen, Catherine Zimmer

   Pages 153-176

7. State Space Methods for Latent Trajectory and Parameter Estimation by Maximum Likelihood

   • Jacques J. F. Commandeur, Siem Jan Koopman, Kees van Montfort

   Pages 177-199

8. Continuous Time Modeling of Panel Data by means of SEM

   • Johan H. L. Oud, Marc J. M. H. Delsing

   Pages 201-244

9. Five Steps in Latent Curve and Latent Change Score Modeling with Longitudinal Data

   • John J. McArdle, Kevin J. Grimm

   Pages 245-273

10. Structural Interdependence and Unobserved Heterogeneity in Event History Analysis

    • Daniel J. Blake, Janet M. Box-Steffensmeier, Byungwon Woo

    Pages 275-301

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.

• Dynamic models
• Latent variable model
• Latent variables
• Likelihood
• Longitudinal research
• Models for panel data
• Structural equation models
• data analysis

From the reviews:

“In this volume the 19 contributors presents 9 papers which combine longitudinal and latent variable research. … The book addresses the great majority of researchers in the behavioural and related sciences, in academic as well as non-academic environments. It also addresses methodologists and statisticians, who are professionally dealing with longitudinal research, to provide standards for state of the art practices, and is very useful for PhD students in mathematical statistics or in biostatistics.” (T. Postelnicu, Zentralblatt MATH, Vol. 1210, 2011)

• Dept. Econometrics & Operations, Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

  Kees van Montfort

• Behavioural Science Institute, Radboud University Nijmegen, Nijmegen, Netherlands

  Johan H.L. Oud

• , Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain

  Albert Satorra

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