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