You are using Promax, which produces correlated factors. Your sample
size is not necessarily large enough for a good construction from 81 items.
That suggests you would get a lot of cross-loaded items. Varimax is
the most common tool, for various reasons, including the clearer delineation
of loadings.
You use Promax, and then you eliminate items that are cross-loaded? That
sounds like it might be a formula for getting rid of the best items. When you
read the items that were dropped for low communalities, I assume you can
infer (somewhat) why these are inferior to the rest -- unclear items or not
on-topic. I expect that is not the case for cross-loaded items.
Poor replication/confirmation could owe to a poor choice of items from the
original set.
--
Rich Ulrich
Date: Wed, 16 Nov 2011 18:02:43 +0800
From:
[hidden email]To:
[hidden email]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