Contents - Index


PURPOSE Perform a randomization test of autocorrelation with an interval or ratio level attribute variable.

DESCRIPTION Relates a dyadic variable (an actor-by-actor matrix) to a monadic variable (a vector representing an interval-scaled attribute of each actor). For example, if the dyadic variable is who is friends with whom, and the monadic variable is height, the procedure tests whether friendship is patterned by height (e.g., children prefer to be friends with children who are the same height as themselves). 

Network or Proximity Matrix
Name of file containing matrix to be analyzed. Data type: Matrix.

Actor Attribute(s)
Name of file containing actor attributes.

Model (Default = Geary)
Choices are:

Geary.  Geary's C statistic (larger negative values indicate greater positive autocorrelation).

Moran. Moran's I statistics (larger positive values indicate greater positive autocorrelation).

Number of random perms: (Default=1000)
Number of autocorrelations to compute between the data matrix and the randomly permuted structure matrix.  The larger the number of permutations, the better the estimates of standard error and "significance", but the longer the computation time.

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.

Output dataset (Default= 'AUTOSIM')

LOG FILE The value of the autocorrelation followed by the autocorrelation averaged over all the permutations together with the standard error. The proportion of random values which are as large for Geary or small for Moran as the actual autocorrelation gives the significance of the calculated value and this is reported.



REFERENCES See Cliff and Ord's classic 1973 book 'Spatial autocorrelation' London: Pion.