Do I have to eliminate variables that are highly correlated before doing an exploratory factor analysis, like it has been discussed for PCA already here?

To specify, some items of my data are highly correlated r = 0.8, some items stem from a similar/partially same test [Example: Persons had to remember 20 words, they had to repeat them directly after (one item) and many minutes after (second item).] Even though this should capture different cognitive dimensions (working memory and short term memory), they are of course highly correlated. Can I use both such highly correlated items as an exploratory factor analysis? (and yes, they do load highly on the same factor). Is there a cutoff for a correlation between items that is ok?

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    $\begingroup$ Good question. As I've just mentioned here, whether to allow "excessively" correlated items in FA has no straightforward answer. Possibly, varied advice could be made. It hugely depends on the field of your study and the purpose of your EFA. Sure, if items are stimuli for humans then one wouldn't want to include apparent duplicates, in their eyes; in psychology, factor-developed scales are usually comprised of items similar but not too similar. Yet, again to say, it depends. $\endgroup$ – ttnphns Aug 8 '16 at 11:30
  • $\begingroup$ (cont.) Try both include and exclude and compare factor structures. You might include at EFA, but exclude afterwards from a scale. Don't forget to check KMO to see if partial correlations are not strong. $\endgroup$ – ttnphns Aug 8 '16 at 11:31
  • $\begingroup$ Also, you might want to turn to Alpha factor analysis to explore how well your collection of items covers the hypothesized latent trait "field". $\endgroup$ – ttnphns Aug 8 '16 at 11:37

The purpose of factor analysis is evaluating the relationship between observed variables.

Exploratory factor analysis is usually used to find the underline structure of the observed variables and identifying the latent structs.

PCA is mathematical tool used for finding such underline structure between variables. PCA is subjected to scaling and actual relation between variables(such as correlation).

I wouldnt linger on filtering variables pre factor analysis, mainly because the math behind most such process(PCA specifically) handles it well.


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