Re:

Posted by Art Kendall on Nov 16, 2011; 12:57pm
URL: http://spssx-discussion.165.s1.nabble.com/no-subject-tp4997278p4997664.html

What did you use as a stopping rule?
Why did you use promax?  Were you not interested in divergent validity?

That a set of variables be close to uncorrelated it a desirable property when you are going to use them as predictors in a GLM or clustering?



Art Kendall
Social Research Consultants

On 11/16/2011 5:02 AM, Eins Bernardo wrote:

Dear All,

I used Principal Axis Factoring using promax method in conducting EFA for the 81 items that utilized six-point ordinal scale.  The sample was n=381.  There is no indication of severe skewness on the data (skewness <3, kurtusis <10 and mardia coefficients >1000).  I used commonalities and factor loadings as criteria of dropping items.  Items with commonalities of <.40 were dropped.  Items with factor loadings of <.32 were also dropped. Crossloadings items were also dropped.  Finally, 35 items were left which loaded to six interpretable correlated factors.   The factors have the following number of items: 10, 7, 8, 4, 3 and 3.  After the factor analysis, the reliability coefficients were computed for each factor.  The Cronbach alpha are quite high.

After the EFA, a CFA was conducted using a separate sample of n=500 using amos.  Unfortunately, the chiquare has zero pvalue and no one of the fit indices were acceptable. I tried to improve the model  (guided by the modification indices).  I found out that the fit (at least the fit indices such as RMSEA, SRMR, cmin/df) of the model improved when I correlated the residuals/error terms.  Question:Is it appropriate to correlate the error terms?

Thank you in advance for your comments.

Eins

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