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Brandmaier, Andreas M.; von Oertzen, Timo; McArdle, John J.; Lindenberger, Ulman – Psychological Methods, 2013
In the behavioral and social sciences, structural equation models (SEMs) have become widely accepted as a modeling tool for the relation between latent and observed variables. SEMs can be seen as a unification of several multivariate analysis techniques. SEM Trees combine the strengths of SEMs and the decision tree paradigm by building tree…
Descriptors: Structural Equation Models, Multivariate Analysis, Computation, Factor Analysis
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Mehta, Paras D.; Neale, Michael C.; Flay, Brian R. – Psychological Methods, 2004
A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the…
Descriptors: Structural Equation Models, Intervals, Multivariate Analysis
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Mehta, Paras D.; Neale, Michael C. – Psychological Methods, 2005
The article uses confirmatory factor analysis (CFA) as a template to explain didactically multilevel structural equation models (ML-SEM) and to demonstrate the equivalence of general mixed-effects models and ML-SEM. An intuitively appealing graphical representation of complex ML-SEMs is introduced that succinctly describes the underlying model and…
Descriptors: Scripts, Factor Analysis, Structural Equation Models, Modeling (Psychology)