Contents - Index


PURPOSE Perform randomization test of autocorrelation for a symmetric adjacency matrix which is partitioned into two groups.

DESCRIPTION Relates a dyadic binary variable (an actor-by-actor adjacency matrix) to a monadic variable (a vector representing an attribute of each actor). For example, if the dyadic variable consists of who is friends with whom, and the categorical variable is gender, the procedure tests whether friendship is patterned by gender (e.g., do boys prefer boys and girls prefer girls?). The routine is limited to two groups and is based upon counting the entries within and between the groups and comparing them with a randomized model.

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

Partition Vector:
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.

No. of Permutations: (Default = 10000)
The number of random permutations required in the test.

Treat diagonals as valid? (Default = No)
If Yes, the values along the main diagonals of each matrix are included in the computations.  Otherwise, they are treated as missing.

Random number seed:
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.

LOG FILE The actor attributes are recoded to 1 and 2 these are reported. 
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.


REFERENCES Cliff, A D and Ord, J K  1973 Spatial Autocorrelation. Pion, London.