1 How can we improve on the MSV values in case subdimensions of the second order construct correlate highly ?
2. Is there any other method to establish discriminant validity of second order construct in CB SEM ?
Sarita,
Have you deleted problematic indicators in first order and second order models? If not then apply these and then check MSV.
1. First-order measurement items to be deleted:
i) delete non-significant loadings on the hypothesized sub-dimensions as such loadings indicate poor or lack of validity,
ii) delete standardized loadings that are less than .50;
iii) also delete large and significant measurement error co-variances with other measures (especially measures of other sub-dimensions) as such measurement error co-variances reflect multidimensionality. The covariance values can be checked using modification indices.
iv) also delete large and significant cross-loadings on non-hypothesized sub-dimensions as these indicate conceptual confounding. These values can also be checked using modification indices.
2. First-order sub-dimensions that have weak or non-significant loadings on the second-order construct can also be deleted with justification as sometimes this may result in lack of validity.
For establishing discriminant validity, see if AVE of a construct is greater than the squared correlation between the construct and conceptually similar construct. See the paper by Fornell and Larcker (1981).
Hope this will help you...
Hardip
Thank you madam for the great help. I Will go through the paper you have suggested.
Regards
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