I am running cluster analysis (using
R) and then looking to see whether various known data groupings (based on metadata) reveals associations between clusters and metadata groups. For each cluster/groupings table I am using the chi squared test, and since there are multiple ways to group the data I am simply using a Bonferroni correction on the resulting p-values.
I've had a hard time finding good references for how to interpret the individual associations, but my understanding currently is that I can determine the significance of a given association by converting the standardized residual of each cell to a p-value using the normal distribution (
p = 2*pnorm(-abs(stdres))). Is this correct, and further is it proper to not adjust these values based on the size of the given table? I've seen nothing to suggest I should perform such a correction, so I'm assuming it is unnecessary.
If so, then given the fact that I am testing multiple tables, should I be simply multiplying each cell's p-value by the number of tables to achieve the right multiple testing correction?