CFA is a much more restricted model than EFA so often an EFA solution indicate lack of fit in a CFA. With respect to correlated residuals, this indicates that the correlations among the indicators is either higher than expected given the solution (positive residual correlations) or lower (negative residual correlations). This indicates that the factor structure is more complex than proposed. Most SEM analysts do not like correlated residuals because they are a sort of hand-waving approach to dealing with the problem. Of more importance is to understand the data structure, not just to get a non-significant chi-square.
Dr. Paul R. Swank,
Children's Learning Institute
Professor, Department of Pediatrics, Medical School
Adjunct Professor, School of Public Health
University of Texas Health Science Center-Houston
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Eins Bernardo
Sent: Wednesday, November 16, 2011 4:03 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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