The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling

In a confirmatory investigation, researchers are mandated to employ covariance-based structural equation modeling (CB-SEM). A crucial assumption inherent in CB-SEM is the multivariate normality of the data. However, real-world data rarely conforms to a perfectly normal distribution. To address this,...

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Published in:International Research Journal of Multidisciplinary Scope
Main Author: Zulkifli N.R.; Aimran N.; Deni S.M.
Format: Article
Language:English
Published: Iquz Galaxy Publisher 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208948140&doi=10.47857%2firjms.2024.05i04.01411&partnerID=40&md5=11cdd1ff32b49ba81cc918532b8b0129
id 2-s2.0-85208948140
spelling 2-s2.0-85208948140
Zulkifli N.R.; Aimran N.; Deni S.M.
The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
2024
International Research Journal of Multidisciplinary Scope
5
4
10.47857/irjms.2024.05i04.01411
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208948140&doi=10.47857%2firjms.2024.05i04.01411&partnerID=40&md5=11cdd1ff32b49ba81cc918532b8b0129
In a confirmatory investigation, researchers are mandated to employ covariance-based structural equation modeling (CB-SEM). A crucial assumption inherent in CB-SEM is the multivariate normality of the data. However, real-world data rarely conforms to a perfectly normal distribution. To address this, unweighted least squares (ULS) is specifically tailored for handling non-normally distributed data in SEM. Nonetheless, ULS often yields unsatisfactory outcomes, such as negative or boundary estimates of unique variances, as it accounts for measurement errors in observed variables. In the realm of SEM, unique variance manifests as disturbance, arising from unreliability or measurement error and reliable variation in items indicating latent causes that are not explicitly known. One common cause of improper solutions in SEM is non-convergence, wherein the estimation fails to reach a minimum fit function. To address this challenge, the present study proposes the regularization of the ULS estimator to rectify inadequacies in model fit. Multivariate non-normally distributed data, with predetermined population parameters and sample sizes, were generated through Pro-Active Monte Carlo simulation and subsequently analyzed using the R Programming Environment. The results reveal the effectiveness of the regularized ULS in enhancing model fit indices such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). © 2024, Iquz Galaxy Publisher. All rights reserved.
Iquz Galaxy Publisher
2582631X
English
Article
All Open Access; Gold Open Access
author Zulkifli N.R.; Aimran N.; Deni S.M.
spellingShingle Zulkifli N.R.; Aimran N.; Deni S.M.
The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
author_facet Zulkifli N.R.; Aimran N.; Deni S.M.
author_sort Zulkifli N.R.; Aimran N.; Deni S.M.
title The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
title_short The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
title_full The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
title_fullStr The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
title_full_unstemmed The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
title_sort The Effect of Regularized Unweighted Least Squares on CFI, TLI, RMSEA and TLI in Structural Equation Modeling
publishDate 2024
container_title International Research Journal of Multidisciplinary Scope
container_volume 5
container_issue 4
doi_str_mv 10.47857/irjms.2024.05i04.01411
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208948140&doi=10.47857%2firjms.2024.05i04.01411&partnerID=40&md5=11cdd1ff32b49ba81cc918532b8b0129
description In a confirmatory investigation, researchers are mandated to employ covariance-based structural equation modeling (CB-SEM). A crucial assumption inherent in CB-SEM is the multivariate normality of the data. However, real-world data rarely conforms to a perfectly normal distribution. To address this, unweighted least squares (ULS) is specifically tailored for handling non-normally distributed data in SEM. Nonetheless, ULS often yields unsatisfactory outcomes, such as negative or boundary estimates of unique variances, as it accounts for measurement errors in observed variables. In the realm of SEM, unique variance manifests as disturbance, arising from unreliability or measurement error and reliable variation in items indicating latent causes that are not explicitly known. One common cause of improper solutions in SEM is non-convergence, wherein the estimation fails to reach a minimum fit function. To address this challenge, the present study proposes the regularization of the ULS estimator to rectify inadequacies in model fit. Multivariate non-normally distributed data, with predetermined population parameters and sample sizes, were generated through Pro-Active Monte Carlo simulation and subsequently analyzed using the R Programming Environment. The results reveal the effectiveness of the regularized ULS in enhancing model fit indices such as the Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual (SRMR). © 2024, Iquz Galaxy Publisher. All rights reserved.
publisher Iquz Galaxy Publisher
issn 2582631X
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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