Kruskal-Wallis test data considerations I'm about to apply Kruskal-Wallis test (non-parametric ANOVA) on three groups of unequal length. I was taught/advised to apply Krukal-Wallis only if:


*

*dependent variable is at least at ordinal level of measurement

*group's $ n > 5 $ (otherwise H statistic is not $ \chi ^2 $ distributed, so exact p-value cannot be calculated, etc.)

*Wikipedia page says that it requires an identically-shaped and scaled distribution for each group
Apart from that, what are general considerations when applying Kruskal-Wallis test? Should I check homoscedascity with Fligner-Killeen's test, and what should I do in case of small $ n $, or unequal group sizes?
There are exactRankTests::wilcox.exact and PASWR::wilcoxE.test functions in R, but I can't seem to find an analogous one for Kruskal-Wallis...
Advice, anyone?
 A: With small, and possibly unequal group sizes, I'd go with chl's and onestop's suggestion and do a Monte-Carlo permutation test. For the permutation test to be valid, you need exchangeability under $H_{0}$. If all distributions have the same shape (and are therefore identical under $H_{0}$), this is true.
Here's a first try at looking at the case of 3 groups and no ties. First, let's compare the asymptotic $\chi^{2}$ distribution function against a MC-permutation one for given group sizes (this implementation will break for larger group sizes).
P  <- 3                     # number of groups
Nj <- c(4, 8, 6)            # group sizes
N  <- sum(Nj)               # total number of subjects
IV <- factor(rep(1:P, Nj))  # grouping factor
alpha <- 0.05               # alpha-level

# there are N! permutations of ranks within the total sample, but we only want 5000
nPerms <- min(factorial(N), 5000)

# random sample of all N! permutations
# sample(1:factorial(N), nPerms) doesn't work for N! >= .Machine$integer.max
permIdx <- unique(round(runif(nPerms) * (factorial(N)-1)))
nPerms  <- length(permIdx)
H       <- numeric(nPerms)  # vector to later contain the test statistics

# function to calculate test statistic from a given rank permutation
getH <- function(ranks) {
    Rj <- tapply(ranks, IV, sum)
    (12 / (N*(N+1))) * sum((1/Nj) * (Rj-(Nj*(N+1) / 2))^2)
}

# all test statistics for the random sample of rank permutations (breaks for larger N)
# numperm() internally orders all N! permutations and returns the one with a desired index
library(sna)                # for numperm()
for(i in seq(along=permIdx)) { H[i] <- getH(numperm(N, permIdx[i]-1)) }

# cumulative relative frequencies of test statistic from random permutations
pKWH   <- cumsum(table(round(H, 4)) / nPerms)
qPerm  <- quantile(H, probs=1-alpha)  # critical value for level alpha from permutations
qAsymp <- qchisq(1-alpha, P-1)        # critical value for level alpha from chi^2

# illustration of cumRelFreq vs. chi^2 distribution function and resp. critical values
plot(names(pKWH), pKWH, main="Kruskal-Wallis: permutation vs. asymptotic",
     type="n", xlab="h", ylab="P(H <= h)", cex.lab=1.4)
points(names(pKWH), pKWH, pch=16, col="red")
curve(pchisq(x, P-1), lwd=2, n=200, add=TRUE)
abline(h=0.95, col="blue")                         # level alpha
abline(v=c(qPerm, qAsymp), col=c("red", "black"))  # critical values
legend(x="bottomright", legend=c("permutation", "asymptotic"),
       pch=c(16, NA), col=c("red", "black"), lty=c(NA, 1), lwd=c(NA, 2))


Now for an actual MC-permutation test. This compares the asymptotic $\chi^{2}$-derived p-value with the result from coin's oneway_test() and the cumulative relative frequency distribution from the MC-permutation sample above.
> DV1 <- round(rnorm(Nj[1], 100, 15), 2)  # data group 1
> DV2 <- round(rnorm(Nj[2], 110, 15), 2)  # data group 2
> DV3 <- round(rnorm(Nj[3], 120, 15), 2)  # data group 3
> DV  <- c(DV1, DV2, DV3)                 # all data
> kruskal.test(DV ~ IV)                   # asymptotic p-value
Kruskal-Wallis rank sum test
data:  DV by IV 
Kruskal-Wallis chi-squared = 7.6506, df = 2, p-value = 0.02181

> library(coin)                           # for oneway_test()
> oneway_test(DV ~ IV, distribution=approximate(B=9999))
Approximative K-Sample Permutation Test
data:  DV by IV (1, 2, 3) 
maxT = 2.5463, p-value = 0.0191

> Hobs <- getH(rank(DV))                  # observed test statistic

# proportion of test statistics at least as extreme as observed one (+1)
> (pPerm <- (sum(H >= Hobs) + 1) / (length(H) + 1))
[1] 0.0139972

A: *

*You need not check homoscedasticity. Kruskal and Wallis stated in their original paper that the “test may be fairly insensitive to differences in variability”.

*If there is no exact test available, you can use bootstrap.

