Hi everyone
For testing my model, I always studied about the PLS and how to test my model in the software. But now I am wondering if there is any preliminary test (such as detecting outliers?) I should do before importing data into smatpls. If you know any step by step methodology, please share.
With many thanks
What to do before doing PLS?
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MOHAMED-2016
- PLS Junior User
- Posts: 3
- Joined: Tue Aug 16, 2016 7:39 pm
- Real name and title: MOHAMED TAILAB-PhD STUDENT
Re: What to do before doing PLS?
This is what you should test after importing your data to the SmartPLS, and before reporting your results
First, you should assess your model in three steps:
(1) Evaluating Reflective Measurement Models: This can be done by consistency reliability, indicator reliability, discriminant validity try to use Cornell-Lacker and Cross-Loading; and Average Variance Extracted
(2)Evaluating Formative Measurement Models: This can be done by Multicollinearity.
(3)Evaluating PLS-SEM Structural Models. this can be done by R square, Q square, f square q, square, and path coefficient. Pay attention to R square due to your subject.
First, you should assess your model in three steps:
(1) Evaluating Reflective Measurement Models: This can be done by consistency reliability, indicator reliability, discriminant validity try to use Cornell-Lacker and Cross-Loading; and Average Variance Extracted
(2)Evaluating Formative Measurement Models: This can be done by Multicollinearity.
(3)Evaluating PLS-SEM Structural Models. this can be done by R square, Q square, f square q, square, and path coefficient. Pay attention to R square due to your subject.
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mnabi
- PLS User
- Posts: 12
- Joined: Sat Aug 20, 2016 3:09 pm
- Real name and title: Morteza - PhD student
Re: What to do before doing PLS?
Thank you very much.
I also found this:
"When empirical data are collected using questionnaires, typically data collection issues must be addressed after the data are collected. The primary issues that need to be examined include missing data, suspicious response patterns (straight lining or inconsistent answers), outliers, and data distribution" (Hair et al. 2014, 50).
I also found this:
"When empirical data are collected using questionnaires, typically data collection issues must be addressed after the data are collected. The primary issues that need to be examined include missing data, suspicious response patterns (straight lining or inconsistent answers), outliers, and data distribution" (Hair et al. 2014, 50).