Hey, Marcos,

It might be helpful to know how many cases you have in your data.

Just a hunch, but the pairwise deletion/not positive definite problem may lie in the large number of missing values per case and/or the exact way the attributes were distributed (i.e. randomized) among respondents. You can read more on this (so I'm told) in Arbuckle, J. L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 243-78). Mahwah, NJ: Lawrence Erlbaum.

You may have no choice but to replace (with care) the missing values before running your analysis. As far as reliability is concerned, there is no way to tell if you would get exactly the same factors and similar factor scores if you had no missing data.

King Douglas

>>> Marcos Sanches <marcos.sanches@IPSOS.COM.BR> 02/03/05 04:49AM >>> 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