Gain more insight from common latent factor in structural equation modeling
There is a great deal of evidence that method bias is really sure influences item validities, measurement error, correlation and covariance between latent constructs and thus leading the researchers to erroneous conclusion due to inflation or deflation during hypothesis testing. To remedy this, the...
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| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2021
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| Subjects: | |
| Online Access: | http://eprints.um.edu.my/35673/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103080561&doi=10.1088%2f1742-6596%2f1793%2f1%2f012030&partnerID=40&md5=897b2002e1694b4355e1c5ba51cfa37d |
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| Summary: | There is a great deal of evidence that method bias is really sure influences item validities, measurement error, correlation and covariance between latent constructs and thus leading the researchers to erroneous conclusion due to inflation or deflation during hypothesis testing. To remedy this, the study provides a guideline to minimize the method bias in the context of structural equation modeling employing the covariance method (CB-SEM) using medical tourism model. A practical approach is illustrated for the identification of method bias based on the new construct namely common latent factor. Using this latent construct, we managed to identify which item has potential to permeate more variance from common latent factor. Nevertheless, we figure out that the method bias is do not exist in our developed model. Therefore, this measurement model is appropriate for structural model in order to achieve the research hypotheses. We hope that this discussion will help the researchers anticipate which items are likely exposed on method bias before proceed to advance modeling. © Published under licence by IOP Publishing Ltd. |
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