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