Posted by
Hector Maletta on
Jan 27, 2012; 5:09pm
URL: http://spssx-discussion.165.s1.nabble.com/about-to-exploratory-factor-analysis-tp5435719p5436073.html
The explained variance proportion is an attribute of the underlying
components or factors. Loadings on each factor is an attribute of the
observed variables, and can be changed by rotation of the factors.
Discarding variables with lower loadings is not an analytical requirement
but a heuristic decision, dictated by the desire to have a more frugal model
with the minimum possible number of variables. If some variable appears to
have low loadings, especially in the first few factors, and this is not
greatly changed via rotation, eliminating those variables may be a smart
move.
A general warning, however, is that even variables that have low loadings in
the first factor extracted may have a heavier weight in some other factor.
Sometimes the problem is not defined by several observed variables mostly
explained by the first factors (as in the classical case of several
inter-correlated cognitive tests explained by the first factor or "general
intelligence"). Sometimes, indeed, observed variables are better explained
by several of the underlying factors, and it may also be the case that the
applicable theory justifies this multi-factor analysis. Thus a variable with
low loading on Factor 1 may have a larger loading on Factor 2 or 3.
Besides, in some cases several variables, having load loadings individually,
may make, all together, a substantial contribution. If these variables (or
some of them) somehow can be interpreted as belonging in the same underlying
dimension (e.g. by being all correlated to the same factor, even if that
factor is not the first) or if they reflect variables with important
theoretical functions, then retaining them may be wise. For instance,
suppose in an instance of the same classical analysis of cognitive ability
you have some variable (or variables) reflecting the nervousness of the
subjects in the test situation: even if that variable (or variables) have a
relatively low loading on the first factor, they may be important to retain.
Finally, it may be the case that through rotation of factors you may get a
better (and more easily interpretable) picture of the importance of those
variables.
Hector
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De: SPSSX(r) Discussion [mailto:
[hidden email]] En nombre de
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Enviado el: Friday, January 27, 2012 09:33
Para:
[hidden email]
Asunto: about to exploratory factor analysis
Hi all,
I have a question. Namely, running exploratory factor analysis (EFA) in
spss, it gives explained variance proportion for each eiganvalue. Besides,
the variables that have loading less than .30 can be dropped out from data
set according to this analysis. Herein, reporting the total explained
variance in articles, is it necessary or appropriate to repeat the EFA with
reduced data set and report the total explained variance obtained from
second analysis?
thanks in advance
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