Hi all,

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,

Marcos