LAWBL-package: LAWBL: Latent (Variable) Analysis with Bayesian Learning

In LAWBL: Latent (Variable) Analysis with Bayesian Learning

LAWBL: Latent (Variable) Analysis with Bayesian Learning

Description

This package is to provide a variety of models to analyze latent variables based on Bayesian learning.

Details

LAWBL represents a partially confirmatory / exploratory approach to model latent variables based on Bayesian learning. Built on the power of statistical learning, it can address psychometric challenges such as parameter specification, local dependence, and factor extraction. Built on the scalability and flexibility of Bayesian inference and resampling techniques, it can accommodate modeling frameworks such as factor analysis, item response theory, cognitive diagnosis modeling and causal or explanatory modeling. The package can also handle different response formats or a mix of them, with or without missingness. The variety of models provide a partial approach covering a wide range of the exploratory-confirmatory continuum under the context of latent variable modeling.

Towards the confirmatory end, this package includes the Partially Confirmatory Factor Analysis (PCFA) model for continuous data (Chen, Guo, Zhang, & Pan, 2020), the generalized PCFA (GPCFA) model covering continuous, categorical, and mixed-type data, and the partially confirmatory item response model (PCIRM) for continuous and dichotomous data with intercept terms (Chen, 2020). For PCFA, GPCFA, and PCIRM, there are two major model variants with different constraints for identification. One assumes local independence (LI) with a more exploratory tendency, which can be also called the E-step. The other allows local dependence (LD) with a more confirmatory tendency, which can be also called the C-step.

Towards the exploratory end, the Bayesian regularized EFA (BREFA) with factor extraction and parameter estimation in one step (Chen 2021) is offered. It's further improved as the Fully and partially EFA with better performance and partial knowledge.

Parameters are obtained by sampling from the posterior distributions with the Markov chain Monte Carlo (MCMC) techniques. Different Bayesian learning methods are used to regularize the loading pattern, local dependence, and/or factor identification.

Note

This package is under development. You are very welcome to send me any comments or suggestions for improvements, and to share with me any problems you may encounter with the use of this package.

Author(s)

Jinsong Chen, [e-mail]

References

Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. Psychometrika. 85(3), 738-774. DOI:10.1007/s11336-020-09724-3.

Chen, J., Guo, Z., Zhang, L., & Pan, J. (2021). A partially confirmatory approach to scale development with the Bayesian Lasso. Psychological Methods. 26(2), 210–235. DOI: 10.1037/met0000293.

Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. Multivariate Behavioral Research. DOI: 10.1080/00273171.2021.1925520.

Chen, J. (2021). A Bayesian regularized approach to exploratory factor analysis in one step. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/10705511.2020.1854763.

Chen, J. (2022). Partially confirmatory approach to factor analysis with Bayesian learning: A LAWBL tutorial. Structural Equation Modeling: A Multidisciplinary Journal. DOI: 10.1080/00273171.2021.1925520.

Chen, J. (In Press). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. Behavior Research Methods.

LAWBL documentation built on May 16, 2022, 9:06 a.m.