How to Solve Discriminant Validity Issues?
Understanding and Solving Discriminant Validity Issues.
We have now assessed the discriminant validity. However, a number of time scholars face issues in establishing discriminant validity. The two session specifically focus on how to solve discriminant validity issues.
Considerations before Collecting the Data
- Make sure to have adequate No. of Items in each scale/construct.
- There should be at least 4-6 items, since, in SEM items are deleted if they fail to load or due to cross-loading.
- Make sure that the Items/Statements are easy to understand.
- Make sure there is no overlap in the statements of different constructs.
How to Solve Discriminant Validity Problems?
To solve the discriminant validity issues, following steps may be undertaken
- Make sure to clean the data.
- Check for Standard Deviation in Responses. Standard Deviation value of Less than 0.25 mean that there is probability of respondent misconduct and the responses for the questions are almost similar.
- Assess cross-loading, if an item is loading well onto another construct instead of its own construct, that particular items shall be removed. For example, an item has loading over 0.70 on two constructs
- Check for Cross-loading, if an item is cross-loading, and the difference is less than .10, REMOVE the item(s).
- Check for low loadings, remove items that have low loadings (start with < 0.40).
- If the problem still persists,try collecting more data
- Combine the constructs that have lack of discriminant validity as they are measuring the same concept (This is appropriate for dimensions of the same construct).
- If problems still persist, dropping one (or more) independent variables (i.e., collinear variables that demonstrate insufficient discriminant validity) from the model may also help.
Farrell, A. M. (2010). Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). Journal of Business Research, 63(3), 324-327.