An extensive comparison of cb-sem and pls-sem for reliability and validity

Structural Equation Modeling (SEM) includes measurement and structural model for hypothesis testing. The results yielded from structural model is unlikely to be valid if a poor loading of an indicator is selected. The impact of these erroneous result on standardized loading is disregard. Thus, knowi...

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Bibliographic Details
Published in:International Journal of Data and Network Science
Main Author: Afthanorhan A.; Awang Z.; Aimran N.
Format: Article
Language:English
Published: Growing Science 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094616686&doi=10.5267%2fj.ijdns.2020.9.003&partnerID=40&md5=f6cff530d18db5661c7f0bba4d4bfa57
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Summary:Structural Equation Modeling (SEM) includes measurement and structural model for hypothesis testing. The results yielded from structural model is unlikely to be valid if a poor loading of an indicator is selected. The impact of these erroneous result on standardized loading is disregard. Thus, knowing how poor loading can affect the validity of measurement model is a crucial issue. This paper attempts to compare the standardized loadings result between two prominent SEM methods (CBSEM and PLS-SEM) using three varied of simulation models (TRA, Loyalty and UTAUT model) to investigate their effects on reliability and validity of measurement model. The data for each model were generated using R software by setting the value of standardized loading and the construct correlations (N=50, 100, 200 and 500). The value of standardized loadings was set to 0.60 for each construct in the model while the construct correlations were set in the range between 0.45 to 0.65. Then, the AMOS 21.0 and ADANCO 2.0 were used to perform the statistical analysis. It shows that good standardized loading can increase the reliability and validity of construct representation. CBSEM is particularly yielded valid and unbiased estimation under confirmatory condition (established theory) compared with PLS-SEM. The results are illustrated with empirical examples. This paper provides updated evidence about CBSEM and PLS-SEM when assessing the measurement model. © 2020 by the authors; licensee Growing Science, Canada.
ISSN:25618148
DOI:10.5267/j.ijdns.2020.9.003