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Longitudinal Research with Latent Variables

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  • © 2010

Overview

  • 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.

  • Includes supplementary material: sn.pub/extras

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Table of contents (9 chapters)

Keywords

About this book

Since Charles Spearman published his seminal paper on factor analysis in 1904 and Karl Joresk ยจ 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.

Reviews

From the reviews:

โ€œIn this volume the 19 contributors presents 9 papers which combine longitudinal and latent variable research. โ€ฆ 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.โ€ (T. Postelnicu, Zentralblatt MATH, Vol. 1210, 2011)

Editors and Affiliations

  • 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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