Reporting single items measures in discriminant analysis for HTMT?
Posted: Thu May 07, 2020 11:48 am
Hello,
I have a doubt concerning whether I should report single items measures in the discriminant validity analysis. I know that for reliability and convergent validity that will not make sense as all the values of Crombach, Composite AVE will always be equal to 1.
Also with respect to discriminant validity I assume that reporting single items constructs and categorical variables like gender in the Fornell-Larcker criterion and in the Cross-loadings tables is purposeless as the item loading of the single item construct or of the categorical variable gender will always be 1.
However, I am not sure whether I should report the values of the ratio of the correlations of single items constructs and categorical variables like gender in the HTMT table and in the HTMT confidence intervals table. Since they are ratio of between/within correlations I am not sure if in any case I should report such correlations!
I look forward to hearing from you!
Thank you in advance for reading this!
Kind regards!
I have a doubt concerning whether I should report single items measures in the discriminant validity analysis. I know that for reliability and convergent validity that will not make sense as all the values of Crombach, Composite AVE will always be equal to 1.
Also with respect to discriminant validity I assume that reporting single items constructs and categorical variables like gender in the Fornell-Larcker criterion and in the Cross-loadings tables is purposeless as the item loading of the single item construct or of the categorical variable gender will always be 1.
However, I am not sure whether I should report the values of the ratio of the correlations of single items constructs and categorical variables like gender in the HTMT table and in the HTMT confidence intervals table. Since they are ratio of between/within correlations I am not sure if in any case I should report such correlations!
I look forward to hearing from you!
Thank you in advance for reading this!
Kind regards!