I received two different results using first QAP (matrix x matrix
and then Autocorrelation (matrix x vector -- interval/ratio data --
correlation). The same data was used for both tests. Please read on:
I used the normalized degree centrality score (vector data), with a
I performed an autocorrelation on this data using Geary's C statistic
as the default model,
I got the following results:
P as Large : 0.992
P as Small: 0.008
According to my understanding of the Geary statistic (just based on the
this means I don't have a significant correlation by a long shot.
However, when I took that same normalized degree score, transformed it
into a square matrix
through the UCINET command Data --> Attribute (chose absolute
difference as method), and
then ran a QAP with the same square matrix data, I got significant
Pearson Correlation: r = 0.058 p = 0.008
Notice that P as Small for Geary is the same p value for Pearson.
However, my understanding
re: the Geary is that one should read the P as Large value.
So what's up? Thanks in advance, Christina