Discriminant Validity Using SmartPLS

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Discriminant Validity

Validity Meaning

Validity is the extent to which a concept, conclusion or measurement is well-founded and corresponds accurately to the real world. In short it is to know whether it serves the purpose.

Example:

The purpose of grapes should serve its purpose by blocking the body’s production of vitamin K and making the blood thinner. when grapes fails to serve this purpose then it looses validity. Similarly, when the constructs of the questionnaire fail to measure what it has to measure, then the questionnaire looses validity. so this test is essential for a research at the initial level and this also gives the reliability of the construct.

Discriminant Validity Using SmartPLS

Discriminant validity is the degree to which any single construct is different from the other constructs in the model (Carmines and Zeller, 1979). Validity is measured with the following,

1. Content validity


2. Criterion validity


3. Construct validity – It has two, named as a) Convergent and b) Discriminant validity

Discriminant validity can also be measured with SPSS, AMOS etc. Now in this article we are going to be clear that Discriminant validity can be better measured with SmartPLS software. This company also shares its lower version at free of cost. The key is sent to us by email.

Output table of Discriminant Analysis

Discriminant Analysis: Table1

From table1, it is clear that the cross loading of the respective factor is greater than that factor’s AVE. For Example The cross loading of “Customization” is 0.837 which is greater than its respective AVE of 0.700. Since the cross loading  is greater for all the factors than the AVE of the respective construct, discriminant validity is confirmed.

More details about SmartPLS from the official website

When running the PLS and PLSc algorithm in SmartPLS, the results report includes discriminant validity assessment outcomes, in the section “Quality Criteria”. The following results are provided:

• the Fornell-Larcker criterion,


• cross-loadings, and


• the HTMT criterion results.

Recommend using the HTMT criterion to assess discriminant validity. If the HTMT value is below 0.90, discriminant validity has been established between two reflective constructs.

If you like to obtain the HTMT_Inference results, you need to run the bootstrapping routine. When starting the bootstrapping routine, it is important that you select the option “Complete Bootstrapping”. Then, in the bootstrapping results report, you find the bootstrapped HTMT criterion results in the section “Quality Criteria”.

The table of Discriminant Analysis

Please note: In SmartPLS 3.2.1 and later version, the HTMT criterion computation differs from the equation given by Henseler, Ringle and Sarstedt (2015). Instead of using the correlations between indicators, SmartPLS uses the absolute value of the correlation between indicators.

For example, when instead of using 0.1, 0.2 and -0.3, which results in an average correlation of 0 an causes problems in the original HTMT equation, SmartPLS uses 0.1, 0.2 and 0.3, which results in an average correlation of 0.2. In consequence, the HTMT criterion is normed between 0 and 1 in SmartPLS and no issues result from negative correlations.

References

Henseler, J., Ringle, C. M., and Sarstedt, M. 2015. A New Criterion for Assessing Discriminant Validity in Variance-based Structural Equation Modeling. Journal of the Academy of Marketing Science, 43(1): 115-135.

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. 2017. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd. Ed., Sage: Thousand Oaks.

https://www.smartpls.de/documentation/discriminant-validity-assessment

Visit Saru Publications

1. INTERNATIONAL JOURNAL OF SCIENCE & ENGINEERING RESEARCH – IJOSER


2. INTERNATIONAL JOURNAL OF COMMERCE, ECONOMICS & MANAGEMENT – IJOCEM


3. INTERNATIONAL JOURNAL OF SERVICE QUALITY – IJSQ


4. INTERNATIONAL JOURNAL OF WEB COMPONENTS, DATA & ANALYTICS – IJWCDA

 

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