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1860799665583161344
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INTELEK Repository
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Online Access
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https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2017-06-19 11:30:01
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| eventvenue |
University of Malaya Kuala Lumpur
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| format |
Restricted Document
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| id |
6910
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UniSZA
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1643-01-FH03-FESP-17-09162.jpg
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| person |
norman
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| recordtype |
oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6910
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| spelling |
6910 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=6910 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Conference Conference Paper image/jpeg inches 96 96 norman 1432 20 20 760 2017-06-19 11:30:01 1432x760 1643-01-FH03-FESP-17-09162.jpg UniSZA Private Access The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling Structural equation modeling (SEM) is the second generation statistical analysis technique developed for analyzing the inter-relationships among multiple variables in a model. Previous studies have shown that there seemed to be at least an implicit agreement about the factors that should drive the choice between covariance-based structural equation modeling (CB-SEM) and partial least square path modeling (PLS-PM). PLS-PM appears to be the preferred method by previous scholars because of its less stringent assumption and the need to avoid the perceived difficulties in CB-SEM. Along with this issue has been the increasing debate among researchers on the use of CB-SEM and PLS-PM in studies. The present study intends to assess the performance of CB-SEM and PLS-PM as a confirmatory study in which the findings will contribute to the body of knowledge of SEM. Maximum likelihood (ML) was chosen as the estimator for CB-SEM and was expected to be more powerful than PLS-PM. Based on the balanced experimental design, the multivariate normal data with specified population parameter and sample sizes were generated using Pro-Active Monte Carlo simulation, and the data were analyzed using AMOS for CB-SEM and SmartPLS for PLS-PM. Comparative Bias Index (CBI), construct relationship, average variance extracted (AVE), composite reliability (CR), and Fornell-Larcker criterion were used to study the consequence of each estimator. The findings conclude that CB-SEM performed notably better than PLS-PM in estimation for large sample size (100 and above), particularly in terms of estimations accuracy and consistency. 3rd ISM International Statistical Conference 2016: Bringing Professionalism and Prestige in Statistics, ISM 2016 University of Malaya Kuala Lumpur
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| spellingShingle |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
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| summary |
Structural equation modeling (SEM) is the second generation statistical analysis technique developed for analyzing the inter-relationships among multiple variables in a model. Previous studies have shown that there seemed to be at least an implicit agreement about the factors that should drive the choice between covariance-based structural equation modeling (CB-SEM) and partial least square path modeling (PLS-PM). PLS-PM appears to be the preferred method by previous scholars because of its less stringent assumption and the need to avoid the perceived difficulties in CB-SEM. Along with this issue has been the increasing debate among researchers on the use of CB-SEM and PLS-PM in studies. The present study intends to assess the performance of CB-SEM and PLS-PM as a confirmatory study in which the findings will contribute to the body of knowledge of SEM. Maximum likelihood (ML) was chosen as the estimator for CB-SEM and was expected to be more powerful than PLS-PM. Based on the balanced experimental design, the multivariate normal data with specified population parameter and sample sizes were generated using Pro-Active Monte Carlo simulation, and the data were analyzed using AMOS for CB-SEM and SmartPLS for PLS-PM. Comparative Bias Index (CBI), construct relationship, average variance extracted (AVE), composite reliability (CR), and Fornell-Larcker criterion were used to study the consequence of each estimator. The findings conclude that CB-SEM performed notably better than PLS-PM in estimation for large sample size (100 and above), particularly in terms of estimations accuracy and consistency.
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| title |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|
| title_full |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|
| title_fullStr |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|
| title_full_unstemmed |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|
| title_short |
The assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|
| title_sort |
assessment of the performance of covariance-based structural equation modeling and partial least square path modeling
|