Confirmatory and Structural Categorical Latent Variables Models |
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Authors: | Chih-chiang Yang |
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Affiliation: | (1) Department of Education, National Taipei University of Education, 134 Sec 2, Ho-Pin East Rd, Taipei, 106, Taiwan |
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Abstract: | 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. |
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Keywords: | categorical latent variable latent class analysis MIMIC-LCA latent transition analysis generalized E– M algorithms reading development |
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