Group wise Correlation Analysis - Compare Correlation between Groups

Group-wise correlation analysis lets researchers test whether the relationship between two variables changes across groups—such as whether servant leadership (SL) relates to self-efficacy (SE) differently for male versus female respondents. The core workflow starts by splitting the dataset by the grouping variable (here, gender), running correlation separately within each subgroup, and then statistically checking whether the two correlation coefficients differ beyond what chance would produce. This matters because “different-looking” correlations (e.g., significant in one group but not the other) are not automatically evidence of a real group difference.

After splitting the file by gender, the analysis runs a two-tailed Pearson correlation with flags for significance and significant correlation. The results show a meaningful contrast: in male respondents, the SL–SE correlation is significant and moderately positive, while in female respondents the correlation is very weak and not significant. That pattern already suggests the relationship may differ by gender, but it still doesn’t answer whether the difference between the two correlation coefficients is statistically significant.

SPSS does not directly perform the significance test for the difference between two independent correlation coefficients across groups, so the method shifts to a manual approach. The key step is converting each subgroup’s correlation coefficient (r) into a Fisher r-to-Z score using Fisher’s r-to-Z transformation. In the example, the male group has r = 0.608 with N = 166, and the female group has r = 0.104 with N = 55. Using Fisher’s transformation in Excel yields Z values of about 0.705 for males and about 0.163 for females.

With those Z scores in hand, the observed Z statistic for the difference is computed using the formula Z_observed = (Z1 − Z2) / sqrt( (1/(N1−3)) + (1/(N2−3)) ). Plugging in N1 = 166 and N2 = 55 produces an observed Z of 3.83. Interpretation follows a standard decision rule: if the Z statistic falls between −1.96 and +1.96, the difference would be treated as not significant (p > 0.05), meaning the null hypothesis of equal correlations would not be rejected. Here, 3.83 exceeds +1.96, so the null hypothesis is rejected.

The conclusion is straightforward: the correlation between servant leadership and self-efficacy is significantly different between male and female respondents. Practically, this approach turns subgroup correlation results into a formal test of whether the strength of association truly varies across groups, not just whether each subgroup’s correlation happens to cross a significance threshold.