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From Steven Culpepper
Discussion of identifiability and estimation of second-order CFA models with an application using lavaan. -
From Steven Culpepper
Introduction to Reilly's (1995) rank rule for evaluating the identifiability of factor-complexity-one CFA models with non-diagonal error covariance matrices. The… -
From Steven Culpepper
An overview of Conti et al. (2014) for our discussion of the proof of Theorem 1. -
From Steven Culpepper
Introduction to confirmatory factor analysis with discussion of identifiability, model fit, and estimation and interpretation using the lavaan R package. -
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From Steven Culpepper
An introduction to restricted latent class models and an associated collapsed Gibbs sampler. -
From Steven Culpepper
We discuss Dirichlet processes as a mechanism for inferring the number of latent classes. An R application is provided. -
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From Steven Culpepper
Bayesian inference for mixture models is reviewed with focus on priors for latent classes and structural parameters. -
From Steven Culpepper
Introduction to finite mixture models with examples in regression, density estimation, and clustering. Identifiability theory is reviewed and R examples are discussed. -
From Steven Culpepper
Reviewing the Bayesian approach to polytomous EFA via the cumulative link probit. The discussion concludes with an application using the R bayesefa package. -
From Steven Culpepper
Introduces limited information methods for polytomous EFA using polychoric correlations. An application is presented using R. -
From Steven Culpepper
Introduction to EFA for multivariate polytomous response data. The video introduces the cumulative link probit formulation and presents an application using the R mirt… -
From Steven Culpepper
An application of the Bayesian EFA mode-jumping approach using the bayesefa R package. -
From Steven Culpepper
Discussing priors for loadings with emphasis on the positive lower triangular restriction to deal with rotational indeterminacy and the recent mode-jumping algorithm.… -
From Steven Culpepper
Introduction to Bayesian inference for EFA with a focus on prior and full conditional distributions for latent intercepts, uniquenesses, and factors. -
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From Steven Culpepper
The EFA likelihood function, tests of model fit and selection, factor rotations, and factor scores. -
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