Thanks to Peter Dalgaard for the correct answer. I misinterpreted what
R was returning.
On Mar 11, 2009, at 7:32 AM, David Winsemius wrote:
On Mar 11, 2009, at 6:36 AM, soeren.vo...@eawag.ch wrote:
A Likert scale may have produced counts of answers per category.
According to theory I may expect equality over the categories. A
statistical test shall reveal the actual equality in my sample.
When applying a chi square test with increasing number of
repetitions (simulate.p.value) over a fixed sample, the p-value
decreases dramatically (looks as if converge to zero).
(1) Why?
With low numbers of repetitions the test has low power, i.e, it may
give you the wrong answer to the question: are those two vectors
from the same distribution? As you increase in number, the simulated
value approaches the "truth".
(2) (If this test is wrong), then which test can check what I want
to check, that is: are the two distributions of frequencies
(observed and expected) in principle the same?
"In principle" they are not the same. Do you want a test that tells
you they are?
(3) By the way, how to deal with low frequency cells?
r <- c(10, 100, 500, 1000, 2000, 5000)
v <- c(35, 40, 45, 45, 40, 35)
sapply(list(r), function (x) { chisq.test(v, p=c(rep.int(40, 6)),
rescale.p=T, simulate.p.value=T, B=x)$p.value })
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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