PolychoricRM: A Computationally Efficient R Function for Estimating Polychoric Correlations and their Asymptotic Covariance Matrix. Issue 2 (4th March 2022)

• Record Type: Journal Article
• Authors: Zhang, Guangjian; Trichtinger, Lauren A.; Lee, Dayoung; Jiang, Ge
• Is Part Of: Structural equation modeling. Volume 29:Issue 2(2022)
• Page Start: 310
• Page End: 320
• Publication Date: 2022-03-04
• Subjects: Factor analysis -- ordinal data -- polychoric correlations -- tetrachoric correlations; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2021.1929996
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: Many applications of structural equation modeling involve ordinal (e.g., Likert) variables. A popular way of dealing with ordinal variables is to estimate the model with polychoric correlations rather than Pearson correlations. Such an estimation also requires the asymptotic covariance matrix of polychoric correlations. It is computationally intensive to estimate polychoric correlations and their asymptotic covariance matrices. We describe a computationally efficient R function PolychoricRM to estimate polychoric correlations and their asymptotic covariance matrix. The function invokes the computing power of modern Fortran and exploits multiple-core (multiple-thread) CPUs on nearly all current computers.