Fornell-Larcker criterion
Fornell-Larcker criterion
Probably a really silly question but i would like to confirm that i am on the right path.
In order to assess the Fornell-Larcker criterion due i use the results presented in the table "Latent Variable Correlations" then square them and compare them to the AVE?
In order to assess the Fornell-Larcker criterion due i use the results presented in the table "Latent Variable Correlations" then square them and compare them to the AVE?
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Hey Christian,
Thank you very much for your quick reply.
In the meanwhile i had already compute based on your way.
Another question arises though. Just found out that in one case the AVE is smaller than a latent variable correlation by 0.05.
The two latents are significantly distant in terms of meaning.
What do you think that i should do?
thank you very much for all your help.
Thank you very much for your quick reply.
In the meanwhile i had already compute based on your way.
Another question arises though. Just found out that in one case the AVE is smaller than a latent variable correlation by 0.05.
The two latents are significantly distant in terms of meaning.
What do you think that i should do?
thank you very much for all your help.
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Hey Thomas,
As far as I know there is no rule, how to handle such problem.
But here comes my proposals :-)
The problem is that a latent variable explane more of an other latent variable as the direct measurements (items) of it.
Therefore the next step should be to check your measurements loadings. Are they high enough (>0,7)?
You should also check your "cross loadings". The correlation between the direct measurement of a latent variable and latent variable should always the highest one.
Should the one or other mesurement not fulfill this criteria I would delete it.
I hope this will help you.
Christian
As far as I know there is no rule, how to handle such problem.
But here comes my proposals :-)
The problem is that a latent variable explane more of an other latent variable as the direct measurements (items) of it.
Therefore the next step should be to check your measurements loadings. Are they high enough (>0,7)?
You should also check your "cross loadings". The correlation between the direct measurement of a latent variable and latent variable should always the highest one.
Should the one or other mesurement not fulfill this criteria I would delete it.
I hope this will help you.
Christian
Christian and Michel thank you very much.
I have checked cross-loadings and they are ok.
The loadings are also very good (above .80 for most of the cases and 0.75 for a couple of indicators).
So you would suggest that according to this there is discriminant validity although the Fornell-Larcker criterion is not met?
thanks
I have checked cross-loadings and they are ok.
The loadings are also very good (above .80 for most of the cases and 0.75 for a couple of indicators).
So you would suggest that according to this there is discriminant validity although the Fornell-Larcker criterion is not met?
thanks
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Re: Fornell-Larcker criterion
hey guys i wanna ask something,
is it alright if i only using the cross loading test for the discriminant validity test?
because my cross loading test values are ok/valid as required
Thank you in advance
is it alright if i only using the cross loading test for the discriminant validity test?
because my cross loading test values are ok/valid as required
Thank you in advance
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Re: Fornell-Larcker criterion
No it is usually not ok. The cross-loadings are only a weak test of discriminant validity. There can be severe problems with discriminant validity and the cross-loadings would not indicate it. Therefore, you should also use the Fornell-Larker and HTMT test.
Dr. Jan-Michael Becker, BI Norwegian Business School, SmartPLS Developer
Researchgate: https://www.researchgate.net/profile/Jan_Michael_Becker
GoogleScholar: http://scholar.google.de/citations?user ... AAAJ&hl=de
Researchgate: https://www.researchgate.net/profile/Jan_Michael_Becker
GoogleScholar: http://scholar.google.de/citations?user ... AAAJ&hl=de