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?
Best Answer
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.