Confirmatory and Structural Categorical Latent Variables Models

Confirmatory and Structural Categorical Latent Variables Models The paper proposes a general framework for modeling multiple categorical latent variables (MCLV). The MCLV models extend latent class analysis or latent transition analysis to allow flexible measurement and structural components between endogenous categorical latent variables and exogenous covariates. Therefore, modeling frameworks in conventional structural equation models, for example, CFA and MIMIC models are feasible in the MCLV circumstances. Parameter estimations for the MCLV models are performed by using generalized expectation–maximization (E–M) algorithm. In addition, the adjusted Bayesian information criterion provides help for model selections. A substantive study of reading development is analyzed to illustrate the feasibility of MCLV models. Quality & Quantity Springer Journals

Confirmatory and Structural Categorical Latent Variables Models

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Springer Netherlands
Copyright © 2006 by Springer
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
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