Eins,
Don’t you actually mean that the chi –square value is very large/the p value is ver small. Yes, you can add residual covariances. But, how do you decide which ones to add and which ones not to add? Factor analysis allows every item to load on every factor. Conventional CFA allows each item to load on only one factor. The question is where does the item-factor covariance go for the non- dominant factors? Some items may need to load on multiple factors.
Gene Maguin
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Eins Bernardo
Sent: Wednesday, November 16, 2011 5:03 AM
To: [hidden email]
Subject:
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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