Re:

Posted by Art Kendall on Nov 18, 2011; 4:56pm
URL: http://spssx-discussion.165.s1.nabble.com/no-subject-tp4997278p5004930.html

You use of promax specifically allows items to be cross loaded.  but the you dropped cross loaded items.  This is contradictory to the purpose of promax.

See what happens if you go back and 1) use varimax rotation and 2) use parallel analysis to ball park the number of factors to retain.
In my experience the number I have finally retained is at least 1 "variable's worth" more than that obtained from purely random data (or random permutations).


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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