Networks -> Ego Networks -> Composition -> Categorical alter attributes

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NETWORK > EGO NETWORKS > COMPOSITION > CATEGORICAL ALTER ATTRIBUTES

PURPOSE To provide descriptive statistics about the values of a categorical variable for ego's alters.

DESCRIPTION This procedure generates a frequency table and measure of heterogenity for the categorical variable of ego's alters, egos category is not included in the calculation.

PARAMETERS Input Network Dataset
This parameter specifies the network dataset indicating the presence and absence of ties. Datatype: directed graph.

Input Attribute Dataset
This parameter specifies the dataset containing attribute data for the network specified above. Once the filename is entered, UCINET will read the labels. You can specify that the attributes appear on the column (the default) or the row Dimension, and then select the attribute's label from the drop down list under Value.

Output Measures Dataset
Specifies the name of the dataset created to store the resulting measures. By default it is the same as the input dataset name with "-EgoHomophily" appended.

Definition of Ego Network
Allows you to specify which nodes connected to ego are included in the ego network calculations.

LOG FILE The log file contain a table with the frequencies for each categorical value and a measure of heterogeneity for the values of a categorical variable among each ego's alters. The first n columns in the table are the distinct values for that categorical value, with labels across the top and frequencies down the columns. The final two columns in the output table is Blau's measure of heterogeneity. Basically, Blau's measure of heterogeneity is 1 minus the sum of the squares of the proportions of each value of the categorical variable in ego's network. For example, a person connected to equal numbers of men and women will have a Heterogeneity measure of 0.5, calculated as 1 - ( (1/2)^2 + (1/2)^2) ). The final column headed IQV (Index of Qualitative Variation) is a normalized version of this index and is equal to the previous column divided by 1-1/n.