Rajesh Tharyan wrote (excerpted):
. . . The post was referring to tests
where you could carry out multiple comparision tests using nonparametric
tests the same way as the oneway with the scheffe option would do (report
the significance of the pairwise difference in means between the various
groups).
My problem was this.
I have a variable class which represents 4 classes of people who buy shares.
They are male exec directors, female execdirectors, male non execdirectors,
female non execdirectors. The corresponding var is class.
I also have a variable caar. Which is the sum of returns they have made by
trading shares over some no of days.
I want to see which group on average does better.
The stats are as follows
[redacted for brevity]
So clearly, the variance is not homogenous across groups. . . .
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A couple of questions:
1. How do you plan to interpret the results of a nonparametric multiple
comparison test in terms of a location shift, given that you've concluded
a priori that the variance is heterogeneous?
2. You seem to have two factors: sex and executive status. Is there a
particular reason why you're using a one-way layout with all pairwise
comparisons, which includes, for example, comparing returns made by male
executive directors to those of female nonexecutive directors? (Also,
Scheffe's method is for contrasts and is not especially recommended for all
pairwise comparisons.)
A couple of comments:
1. The ratio of the largest variance to the smallest variance in your
sample is 2.46. This doesn't seem extraordinary.
2. Perhaps this sample of directors and how much money they make on the
stock market is an exhaustive accounting of the population, based upon
compulsory reporting by directors of publicly traded firms. Perhaps not.
The greatest concerns about the ability to make defensible inferences would
seem to lie here.
Joseph Coveney
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