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1860796990565122048
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INTELEK Repository
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to all authors
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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 |
2016-08-12 15:11:28
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Restricted Document
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10954
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UniSZA
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5102-01-FH02-FESP-17-09394.pdf
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Author
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10954
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10954 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=10954 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 11 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Author to all authors to all authors 2016-08-12 15:11:28 5102-01-FH02-FESP-17-09394.pdf UniSZA Private Access A comparative study between GSCA-SEM and PLS-SEM MJ Journal on Statistics and Probability Structural Equation Modeling (SEM) now becomes one of the prominent methods that can ascertain researchers today to analyze the causal effect between latent constructs with multiple variables simultaneously. To add, SEM have two catego-ries namely Covariance and Component or Composite based SEM in which each approaches have their own advantages and disadvantages. Component model have two approaches namely Generalized Structured Component Analysis (GSCA) and Partial Least Square in which both of these applications itself using least square estimator and bootstrap technique in providing of parameter estimate and hypothesis testing. However, there are only a few studies using GSCA-SEM to ana-lyze the relationship between latent construct. Therefore, this study interest to compare the performance of PLS and GSCA in terms of the consistency factor loading, parameter estimation, standard error, and statistical power test using different of sample size. In this study, we perform the medical tourism data as a research subject. The study reveals that the perfor-mance GSCA-SEM is better than PLS-SEM in terms of the consistency, standard error and parameter estimate. However, both of this application is really powerful to minimize the type II error. 1 1 63-72
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| spellingShingle |
A comparative study between GSCA-SEM and PLS-SEM
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| subject |
to all authors
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| summary |
Structural Equation Modeling (SEM) now becomes one of the prominent methods that can ascertain researchers today to analyze the causal effect between latent constructs with multiple variables simultaneously. To add, SEM have two catego-ries namely Covariance and Component or Composite based SEM in which each approaches have their own advantages and disadvantages. Component model have two approaches namely Generalized Structured Component Analysis (GSCA) and Partial Least Square in which both of these applications itself using least square estimator and bootstrap technique in providing of parameter estimate and hypothesis testing. However, there are only a few studies using GSCA-SEM to ana-lyze the relationship between latent construct. Therefore, this study interest to compare the performance of PLS and GSCA in terms of the consistency factor loading, parameter estimation, standard error, and statistical power test using different of sample size. In this study, we perform the medical tourism data as a research subject. The study reveals that the perfor-mance GSCA-SEM is better than PLS-SEM in terms of the consistency, standard error and parameter estimate. However, both of this application is really powerful to minimize the type II error.
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| title |
A comparative study between GSCA-SEM and PLS-SEM
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| title_full |
A comparative study between GSCA-SEM and PLS-SEM
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| title_fullStr |
A comparative study between GSCA-SEM and PLS-SEM
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| title_full_unstemmed |
A comparative study between GSCA-SEM and PLS-SEM
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| title_short |
A comparative study between GSCA-SEM and PLS-SEM
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| title_sort |
comparative study between gsca-sem and pls-sem
|