AVE & composite reliability with SPSS I have some issues with an exploratory factor analysis.
Can anybody please tell me how to calculate the Average Variance Extracted (AVE) and the Composite Reliability from two factors, each with three items using SPSS? If not with SPSS, Stata might help too.
 A: The following is shamelessly extracted from the following link.

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Finally, the "average variance extracted" measures the amount of variance that is captured by the construct in relation to the amount of variance due to measurement error and can be calculated using the following formula: (summation of squared factor loadings)/(summation of squared factor loadings) (summation of error variances) (Fornell & Larcker). If the average variance extracted is less than .50, then the variance due to measurement error is greater than the variance due to the construct. In this case, the convergent validity of the construct is questionable. 
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I haven't used SPSS in some time, and I don't remember seeing an option to perform these calculations, but you can certainly do it using the syntax. 
These two links give you an introduction to SPSS syntax. What I would do from here is examine the syntax SPSS gives you when you perform an FA, and use the variables named to compute the average variance explained.
Sorry for the lack of a definitive answer, but I felt that some response was definitely better than none. 
A: In case richiemorrisroe's response doesn't give you quite enough, I suggest...


*

*right-clicking your SPSS factor
analysis output and choosing Results
Coach to clarify the contents of the Variance
Explained table 

*searching the Help    files or
Tutorial for Reliability    Analysis.
I'm thinking that by    "composite
reliability" you mean    internal
consistency reliability
(Cronbach's alpha).

A: The average variance extracted (AVE) calculated as follows:
total of the squared multiple correlations plus the total sum of each variable, then divides it by the number of factors in that variable.
In order to get square multiple correlation of each item, you need to find square of each item Standardized Regression Weight / Estimate.
AVE- average variance extracted (AVE) should not be less than .05, this is to show that more than half of the variances is observed (Janssens, et. Al). 
AVE can be calculated by using auto design by James Gaskin by visiting this website, and click on Excel StatTools on the left hand menu, it is an excel file with calculator for calculating AVE, reliability and validity test. 
You can also view the video on YouTube through this link.
