Longitudinal data analysis with structural equations
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8034
comunitat-uji-handle3:10234/8637
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Título
Longitudinal data analysis with structural equationsFecha de publicación
2008Editor
Hogrefe & HuberISSN
16142241Tipo de documento
info:eu-repo/semantics/articleVersión
info:eu-repo/semantics/submittedVersionPalabras clave / Materias
Resumen
In this paper we review different structural equation models for the analysis of
longitudinal data: (a) univariate models of observable variables, (b) multivariate models
of observable variables, (c) models with ... [+]
In this paper we review different structural equation models for the analysis of
longitudinal data: (a) univariate models of observable variables, (b) multivariate models
of observable variables, (c) models with latent variables, (d) models that are
unconditioned or conditioned to other variables (depending on the variability of the
independent variables: time-varying or time-invariant, and depending on the type of
independent variables: of latent variables or of observable variables), (e) models with
interaction of variables, (f) models with non-linear variables, (g) models with a
constant, (h) with single level and multilevel measurement, and (i) other advances in SEM of longitudinal data (latent growth curve model, latent difference score, etc.).
We have paid more attention to the interaction of variables and to non-linear
transformations of variables because they are not frequently used in empirical
investigation. They do, however, offer interesting possibilities to researchers who wish
to verify relations between the variables they obtain. Potential applications are
described, with their advantages and disadvantages. [-]
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info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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