> To:
[hidden email];
[hidden email]> Subject: RE: about to exploratory factor analysis
> Date: Fri, 27 Jan 2012 14:09:38 -0300
>
> 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
>
> -----Mensaje original-----
> De: SPSSX(r) Discussion [
[hidden email]] En nombre de
>
[hidden email]> 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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