Cross tabulation of two categorical variables: recommended techniques I'm aware that this one is far from yes or no question, but I'd like to know which techniques do you prefer in categorical data analysis - i.e. cross tabulation with two categorical variables.
I've come up with: 


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*χ2 test - well, this is quite self-explanatory

*

*Fisher's exact test - when n < 40,

*Yates' continuity correction - when n > 40,


*Cramer's V - measure of association for tables which have more than 2 x 2 cells,

*Φ coefficient - measure of association for 2 x 2 tables,

*contingency coefficient (C) - measure of association for n x n tables,

*odds ratio - independence of two categorical variables,

*McNemar marginal homogeniety test,


And my question here is: Which statistical techniques for cross-tabulated data (two categorical variables) do you consider relevant (and why)?
 A: I think you need to rework this question.   It all depends on the problem/data which has generated the cross-tab.   
A: I would use Fisher's Exact Test, even for large N. I wouldn't know why not. Any performance argument predates today's fast computers.
A: I must agree.. there is no single best analysis!
not just in cross tabulations or analysis of categorical data but in any data analysis... and thank god for that!
if there was just a single best way to address these analyses well many of us would not have a job to start with... not to mention the loss of the thrill of the hunt!
the joy of analysis is the unknown and the search for answers and evidence and how one question leads to another... that is what i love about statistics!
So back to the categorical data analysis... it really depends on what your doing. Are you looking to find how different variables affect each other as in drug tests for example we may look at treatment vs placebo crossed with disease and no disease... the question here is does treatment reduce disease.... chi square usually does well here (given a good sample size).
Another context ihad today was looking at missing value trends... i was looking to find if missing values in one categorical variable relate to another... in some cases i knew the result should be missing and yet there were observations that had values... a completely different context to the drug test!
