Hi,
my title already points it out. For a construct item that is composed of three measurement items I got the following information using SmartPLS 3.0:
Cronbach's Alpha: 0.865
rho_A: -4.502
Composite Reliabiolity: n/a
Average Variance Extracted (AVE): n/a
What could be the reasons that I got such an output?
That's how I modeled it in SmartPLS 3.0.
Thanks.
Composite Reliability and AVE are n/a despite high Cronbach's alpha
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- SmartPLS Developer
- Posts: 1284
- Joined: Tue Mar 28, 2006 11:09 am
- Real name and title: Dr. Jan-Michael Becker
Re: Composite Reliability and AVE are n/a despite high Cronbach's alpha
You have a very strange (i.e., negative) estimate for rho_A and it seems that you are using PLSc.
With such a strange reliability estimate (similar to Haywood cases in CB-SEM where you have negative variances) you will get inadmissible solutions for some of your estimates such as path coefficients and loadings and subsequent estimates such as composite reliability and AVE. In contrast, Cronbach’s Alpha is not based on any PLS results, but only on correlations between the indicators. Hence, it is not affected by the strange PLSc estimates.
Solution: Estimate your model with normal PLS or define the problematic construct as composite (formative) in PLSc. Indicators pointing inwards.
The reasons is that PLSc makes very strict assumptions about the data structure and requires common factor model data. If this is not fulfilled, it gives strange estimates. Usually, this is a sign that your construct is simply not a common factor (reflective) and should be estimated differently.
With such a strange reliability estimate (similar to Haywood cases in CB-SEM where you have negative variances) you will get inadmissible solutions for some of your estimates such as path coefficients and loadings and subsequent estimates such as composite reliability and AVE. In contrast, Cronbach’s Alpha is not based on any PLS results, but only on correlations between the indicators. Hence, it is not affected by the strange PLSc estimates.
Solution: Estimate your model with normal PLS or define the problematic construct as composite (formative) in PLSc. Indicators pointing inwards.
The reasons is that PLSc makes very strict assumptions about the data structure and requires common factor model data. If this is not fulfilled, it gives strange estimates. Usually, this is a sign that your construct is simply not a common factor (reflective) and should be estimated differently.
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