I am performing a factor analysis with 60 attributes. Each one were
rated in a importance scale - 0 for Not Important to 10 for Very
Important. As there are so many attributes each respondent rated only a
random subset of 39 attributes. That means I have 60 - 39 = 21 missing
values for each case. I want to run a factor analysis with this data.
If I select the listwise method of missing values deletion, of
course I end up with no case left.
If I select the pairwise method of missing values deletion, I
get na error message - "The matrix is not positive definite. This may be
due to pairwise deletion of missing values.".
If I select the "replace with means" method of missing values
substitution, then it sorks well.
My questions are:
1) Why does the pairwise method does not work? Even when I select a
subsample it does not work. I know what is a 'not positive definete
matrix', but why this happens? Is there a way to handle this?
Before the enterview had been done, I made a simulation using another
study. I deleted randomly some values for every case so that every cases
had some missing values, then I ran a factor analysis with pairwise
deletion. It worked pretty well, in fact the final factor were almost
the same these one got with the complete data. So I didn't hope this
problem could happen.
2) In such a case, would the factor analysis done with missing values
replaced with means be reliable?
Thanks in advance,