162013-03-08 10:17:26localhostconverted to 1.6 markup152008-05-29 10:57:49PeterWatson142008-05-29 10:50:47PeterWatson132008-05-29 10:50:36PeterWatson122008-05-07 09:04:47PeterWatson112008-05-06 14:59:43PeterWatson102008-05-06 14:48:01PeterWatson92008-05-06 14:41:07PeterWatson82008-04-21 15:28:29PeterWatson72008-04-21 15:28:15PeterWatson62008-04-21 13:36:07PeterWatson52008-04-21 10:59:08PeterWatson42008-04-21 10:58:08PeterWatson32008-04-21 10:56:41PeterWatson22008-04-21 10:54:18PeterWatson12008-04-21 10:52:49PeterWatson

The following syntax produces individual group correlation matrices involving variables, x, saves the output to a SPSS data file and then compares two of the groups using Fisher's transformation (details and exmaples, for example in, Howell, (2002)). In brief, we can transform a correlation, r, to ,r', where $$\mbox{r' = 0.5} log_text{e} abs [ \frac{1+r}[1-r} } ]$$. Now, for a sample of size, N, the transformed correlation, r', follows a normal distribution with a mean of zero and a variance equal to 1/(N-3). This means we can compare two correlations by comparing their transformed counterparts and so form a z score. $$z = \frac{r'_text{1} - r'_text{2}}{\sqrt{1/(N_text{1}-3) + 1/(N_text{2}-3)}}$$ The macro below firstly uses the output management system (OMS) to obtain the correlations for two independent groups and then carries out the transformation and finally computes a z score which we can convert to p-values using other functions in SPSS. Any p-value less than 0.05 suggests there is evidence a pair of group correlations differ from one another. Reference Howell DC (2002) Statistical methods for psychologists. Duxbury Press:Pacific Grove, CA