Factor loading is more than 0.92 also ave and cr are more than 0.95? Is this acceptable? If it's not i tried to delete items but the more i delete the more factor loading is higher! how can i treat it?
factor loadings, AVE, and CR may initially seem desirable, but excessively high values can indicate potential issues with your measurement model.
Factor loadings above 0.92 suggest very high correlation between items and the factor. it may indicate that the items are too similar.
AVE and CR above 0.95 may indicate over-specification.
If items are too similar, consider revising or rephrasing them to increase diversity in content.
If high factor loadings persist after item deletion, you may need to try alternative models, such as exploratory factor analysis.
In SmartPLS 4, factor loadings indicate the strength of relationships between observed variables (indicators) and their corresponding latent constructs (factors). Higher factor loadings suggest stronger relationships, indicating good convergent validity when all factor loadings are significant and reasonably high (typically above 0.5). This validates that the observed variables adequately measure their respective latent constructs.
@Chuck_A_Arize thank you for your answer sir. I know that factor loading is good when it's more than 0.5 but when it's 0.92 and more this means there is Multicollinearity. I want to fix it
Hi. I agree with Heba Ramadan. It seems your observed variables (indicators) are too similar and will not provide enough variability to measure the latent variables.
It is weird to say, but such a level of AVE and CR (above 0.95) is bad news. Usually when we delete items the situation is upside down: we have several observable items with low factor loading and this pull down AVE and CR.
So, we eliminate the indicators with the lowest factor loadings to improve AVE and CR.
I think that in your case, if you do so, you put AVE and CR even higher, what is bad.
Sorry to say, but I agree with Heba Ramadan and I also think you must consider revising or rephrasing the indicators to increase diversity in content.
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