Effect size for log linear models My understanding is Cramer's V is a measure of the correlation between two discrete variables can be used as an effect size measure for a chi-squared test for observations in a two-way contingency table. 
Following the answer given to this question, when trying to carry out such a test for a three-way contingency table, I used a log linear model. Is there a measure of the correlation between three discrete variables to be used alongside log linear models similar to Cramer's V for a chi-squared test?
 A: Don't know about a measure of association between 3 disrete variables, but you could use R^2 or something like that from a regression model.  There is an alternative measure for pairs of binary variables $(x,y)$ called Tanimoto distance (Jaccard distance for discrete vars), which is
\begin{equation}
d(x,y)=\frac{n(x \cap y)}{n(x) + n(y) - n(x \cap y)},
\end{equation}
where $n(x \cap y)$ is the number of records with ones in both vectors, and $n(x)$ and $n(y)$ are the total number of ones in each vector.  This is a similarity coefficient with range $0 \leq d(x,y) \leq 1$.   While the above is not for hypothesis testing, you should probably be aware of it. 
Log-linear regression has not been developed in all computer packages as much as one would assume.  Statistica (STATSOFT, DELL) has a quite strong package, and SAS has CATMOD, etc.  There is a family of categorical regression models known as Grizzle-Starmer-Koch (GSK) which is quite nice for count data which mostly came out of UNC-Chapel Hill, and CATMOD can tackle some of these.  I never liked log-linear, and always ran GSK, or "linear categorical regression" -- since you can perform logistic, log-linear, survival analysis, any multiway contingency table analysis problem using GSK.  GSK is like a SWAK (Swiss Army knife).       
