I have done a canonical correlation analysis using the American Community Survey Dataset. The analysis is done between Ancestry
and Educational Attainment
variables. The values for canonical correlations are: {0.8140, 0.5716, 0.4708, 0.3946, 0.1465, 0.1365, 0.0409}.
The values for multivariate tests of significance for the first canonical function:
Statistic Value F-Value df1 df2 P-Value
Wilk's Lambda 0.143 35.373 98 19754 0.000
Pillai's Trace 1.408 54.358 98 21896 0.000
Hotelling Trace 2.961 22.779 98 21842 0.000
Roy's Root 0.662
The redundancy analysis for the first canonical function:
For Educational Attainment
variables
Average Squared Loading Canonical R^2 Redundancy Index
2.119 0.666 1.411
For Ancestry
variables
Average Squared Loading Canonical R^2 Redundancy Index
2.1514 0.666 1.432
Please help me interpret these results. I understand the p-values are good (as they are very low); F-values seems good as well (as they are high), but I'm not sure how to interpret the values of Wilk's Lambda, Pillai's Trace, the Hotelling–Lawley Trace, and Roy's Largest Root.
According to my understanding of the analysis, the multivariate tests are statistically significant, but not practically significant according to redundancy analysis. Any help (a little explanation of the analysis) would be appreciated.