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What should be the minimum value of correlation in EFA option?
While performing EFA using Principal Axis Factoring with Promax rotation, Osborne, Costello, & Kellow (2008) suggests the communalities above 0.4 is acceptable. This is also suggested by James Gaskin on http://statwiki.kolobkreations.com .
What is the minimum correlation?
Correlation coefficient values below 0.3 are considered to be weak; 0.3-0.7 are moderate; >0.7 are strong. You also have to compute the statistical significance of the correlation.
Why do we need EFA?
In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.
Do you have to eliminate correlated variables in PCA?
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?
You do not want to remove all correlated variables. It is only when the correlation is so strong that they do not convey extra information. This is both a function of the strength of correlation, how much data you have and whether any small difference between correlated variables tell you something about the outcome, after all.
When to remove correlated variables from data science stack?
If you retrain the model, the opposite could have happened. You should consider checking VIF (Variance Inflation Factor). Try removing features with higher VIF. Generally, it is preferred that VIF is below 10.
Initially, with two almost uncorrelated variables, the contributions are almost equal (upper left corner). After adding one variable correlated with the second–exactly as in the geometric illustration–there are still just two major components, one now twice the size of the other.