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ERIC Number: EJ1459100
Record Type: Journal
Publication Date: 2022
Pages: 11
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1070-5511
EISSN: EISSN-1532-8007
Available Date: N/A
Polychoricrm: A Computationally Efficient R Function for Estimating Polychoric Correlations and Their Asymptotic Covariance Matrix
Guangjian Zhang; Lauren A. Trichtinger; Dayoung Lee; Ge Jiang
Structural Equation Modeling: A Multidisciplinary Journal, v29 n2 p310-320 2022
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.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A