Blcfa: An R Package for Bayesian Model Modification in Confirmatory Factor Analysis. Issue 4 (4th July 2021)

• Record Type: Journal Article
• Authors: Zhang, Lijin; Pan, Junhao; Dubé, Laurette; Ip, Edward Haksing
• Is Part Of: Structural equation modeling. Volume 28:Issue 4(2021)
• Page Start: 649
• Page End: 658
• Publication Date: 2021-07-04
• Subjects: Bayesian method -- blcfa package -- confirmatory factor analysis -- covariance Lasso; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1867862
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: In confirmatory factor analysis (CFA), post hoc model modification (PMM) indexes are often used to adjust for possible residual correlations between items. Although the approach is useful for improving model goodness-of-fit, it requires an iterative, one-item-pair-at-a-time procedure that can be tedious and prone to error. This paper provides a didactic discussion in the form of a tutorial of a more efficient and practical alternative and its implementation using an R-based package. The tutorial contains (1) the Bayesian covariance Lasso (least absolute shrinkage and selection operator) approach as an alternative to the PMM method, and (2) the R package blcfa, which implements the Bayesian covariance lasso and directly interfaces with M plus . It adopts a two-step approach by first estimating the entire residual covariance matrix, and then identifying the nonzero entries and seamlessly feeding them into M plus . Two examples were used to illustrate package implementation.