Row wise significance testing? Chi squared on individual rows? I have a table of counts with 4 columns and 25 rows. What statistical test should be used to asses significance for values within each row? In other words I want to perform significance testing on individual rows to see if there is homogeneity.
I was thinking of something similar to rowwise ANOVA, except I have counts instead of means.
Can chi squared be performed on individual rows?
I am using R to perform this analysis.
Edit: Mainly, I want to do a significance test of each row, so 25 tests. However, I'm not sure whether a chi-squared test for just 4 values would make statistical sense.

Assuming the counts are large enough, a chisquare test for just 4 values makes statistical sense. You must just be prepared for the multiplicity problems of making many tests, so the p-values cannot be taken at face value, some multiple testing correction must be done.
With simulated data, 25 rows so 25 pvalues, we cam make a qqplot against the uniform distribution:

which seems consistent with the null hypothesis, which is true in this simulation.  The R code used:
set.seed(7*11*13)  # My public seed
tab <- matrix(as.integer(NA), nrow=25, ncol=4) 
for (i in 1:25) tab[i, ] <- 
   table(factor(sample(1:4, 100, replace=TRUE, prob=rep(1/4, 4)),
                levels=1:4)) 

pvals <- rep(0, 25)
for (i in 1:25) pvals[i] <- chisq.test(tab[i, ])$p.value 

qqplot(ppoints(25), pvals) 
qqline(pvals, distribution=qunif, col="red") 

You could of course equally well have done a qqplot of the 25 observed chisquare test statistics against the chisquared distribution with 1 df.
