Nilesh,

1. In UCINET, you can JOIN all the matrices together into a
single file containing 500 2-way matrices: in effect a 15x15x500 matrix. Then
you can run SIMILARITIES on that file, specifying MATRICES as the dimension to
correlate (at least in fairly recent versions of ucinet). The result is a 500 by
500 matrix of similarity coefficients. You can then submit this correlation
matrix to any clustering routine.

2. In SPSS or SAS, it is not difficult to cluster your 500
matrices. Just string out each one as a row of 225 numbers (call these
variables). So you have a new matrix that is 500 rows by 225 columns. Then
submit to clustering routine (specifying cases as the dimension to cluster).

3. I'm not sure what you are trying to do so is difficult to
comment on the larger issues. But I think you flirt with the Dark Side whenever
you manipulate the data expressly to "get more
significance".

Steve.

----- Original Message -----From:[log in to unmask] href="mailto:[log in to unmask]">Saraf, NileshSent:Monday, June 11, 2001 8:41 PMSubject:Multiple matricesHello again everyone,A colleague of mine has overwhelmed me with some 500 matrices.Each matrix is a 15X15 cognitive map. The expected hypothesis use QAP and are not so strongly supported.Therefore, we are exploring how to cluster these 500 matrices into groups of matrices depending on certain characteristic of each matrix such as centralities of nodes, etc. The end result could be a few clusters where matrices in these clusters are similar in certain respects.With this clustering, we plan to add dummy variables for these matrices in a QAP regression and hope to get more significance.1. Is this a statistically valid thing to do?2. Which software package facilitates clustering of matrices (unlike clustering nodes in a single matrix) ?3. Running a QAP correlation on a hundred pairs of matrices is troublesome if I use the user interface in UCINET. Is there a way one can write a simple small program to automate this once the matrices are stored in certain known files? (e.g., like a SAS procedure)Thank you.Regards.Nilesh Saraf