Name of file containing matrix to be analyzed. Data type: Graph

The name of an UCINET dataset that contains a partition of the actors into two groups. To partition the data matrix into groups specify a vector by giving the dataset name, a dimension (either row or column) and an integer value. For example, to use the second row of a dataset called ATTRIB, enter "ATTRIB ROW 2". The program will then read the second row of ATTRIB and use that information to define the groups. All actors with identical values on the criterion vector (i.e. the second row of attrib) will be placed in the same group.

The number of random permutations required in the test.

If Yes, the values along the main diagonals of each matrix are included in the computations. Otherwise, they are treated as missing.

The random number seed sets off the random permutations. UCINET generates a different random number as default each time it is run. This number should be changed if the user wishes to repeat an analysis. The range is 1 to 32000.

A table which gives the observed and expected counts for the data. The first row gives the counts within group 1, the second is the counts between the groups and the third is the counts within group 2. The expected simply gives the values that would be expected if the ones were randomly distributed within and between the groups. The observed gives the counts of the data and the difference subtracts the expected from the observed. The P>=Diff and P<=Diff give the relative frequency that a randomly permuted matrix gets a difference as large or larger and as small or smaller than the observed. These columns are used to test the significance of the observed data.