Dear SocNet,
I received two different results using first QAP (matrix x matrix
correlation)
and then Autocorrelation (matrix x vector -- interval/ratio data --
correlation). The same data was used for both tests. Please read on:
1. Autocorrelation:
I used the normalized degree centrality score (vector data), with a
square matrix.
I performed an autocorrelation on this data using Geary's C statistic
as the default model,
I got the following results:
Autocorrelation: 0.629
P as Large : 0.992
P as Small: 0.008
According to my understanding of the Geary statistic (just based on the
UCINET help),
this means I don't have a significant correlation by a long shot.
2. QAP:
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
results:
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
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