Loglinear Models With Latent Variables
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Loglinear Models with Latent Variables
Sociologists with a quantitative bent will doubtless find it useful. . . . well-written, with a wealth of explanation. . . --Dougal Hutchison in Educational Research "Loglinear Models with Latent Variables, by Jacques A. Hagenaars, is a timely contribution to the literature that serves to inform researchers of the richness of loglinear approaches to analyzing latent categorical variables. . . . The author provides a clear exposition of the loglinear model." --Scott L. Hershberger in Structural Equation Modeling Since the 1980s, the loglinear model has become the dominant form of categorical data analysis as researchers have expanded it into new directions. Jacques A. Hagenaars′ book shows researchers the applications of one of these new developments--how uniting ordinary loglinear analysis and latent class analysis into a general loglinear model with latent variables can result in a modified LISREL approach. This modified LISREL model will enable researchers to analyze categorical data in the same way that they have been able to use LISREL to analyze continuous data. Beginning with an introduction to ordinary loglinear modeling and standard latent class analysis, Hagenaars explains the general principles of loglinear modeling with latent variables; the application of loglinear models with latent variables as a causal model, as well as a tool for the analysis of categorical longitudinal data; the strengths and limitations of this technique; and lastly, a summary of computer programs that are available for executing this technique.
Longitudinal Research with Latent Variables
Author: Kees van Montfort
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-05-17
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.