Hi
On Mon, 12 Mar 2001, Irving Scheffe wrote:
> Jim:
> For example, suppose you had a department
> in which the citation data were
>
> Males Females
> 12220 1298
> 2297 1102
When I said outlier, I had in mind hypothetical data of the
following sort (it doesn't matter to me whether it is the
salaries or the citation rates):
Males Females
17000 1000
1000 1000
1000 1000
1000 1000
Avg 5000 1000
vs.
Males Females
5000 1000
5000 1000
5000 1000
5000 1000
Avg 5000 1000
I would view the latter somewhat differently than the former with
respect to differences between these samples of males and
females, and with respect to the kinds of explanations I would
seek (e.g., somewhat general to males, something specific to
male 1).
> The male with 12220 is, let's imagine, a Nobel Prize
> winner. The salaries for the 4 people are
>
> Males Females
> 156,880 121,176
> 112,120 114,324
Of course if the salaries were:
Males Females
112,120 121,176
156,880 114,324
You probably might want not to promote the hypothesis of
productivity differences explaining the gender differences. That
was the point of one of my later comments.
> As Radford Neal has pointed out succinctly, the argument about
> outliers is irrelevant, and I want to emphasize with this example
> that it is irrelevant on numerous levels. First of all,
> it is not necessarily clear whether, and in which of several
> senses, our Nobel Prize winner is an outlier in his group.
> Second, even if he is -- so what? Surely you would not argue
> that this means he didn't deserve his salary!
Assuming a correlation between productivity and salary (or
winning of Nobel prizes).
> In fact, careful examination of the salary data [never
> made public by the administration] together with the
> performance data might well have led to the conclusion
> that it is the male faculty who are underpaid.
I'm in perfect agreement with this, although I still think that
statistics would play a positive role in identifying the
determinants of salary.
> Although, as Dr. Neal pointed out, it is not logically
> relevant to the issue, I would like to
> explore your notion, echoed without
> justification by Rich Ulrich, that the
> huge difference in citation performance between
> MIT senior men and women might be due
> to "one or two outliers."
I don't remember making any such attribution. I asked a question
about whether detractors of statistical testing would view
equivalently differences due to some outliers and more consistent
results, in the sense I showed above. I'm not sure it is any
more palatable to have one's motives misconstrued by people
arguing against gender-related bias than to have them
misconstrued by people arguing for gender-related bias.
> Take a look at the data again, and tell me
> which male data you consider to be outliers
> within the male group, and why. For example,
> are the men with 2133 and
> 893 "outliers," or those with 12830 and 11313?
Not having taken any position on it, I am not too sure I feel any
compulsion to answer your question. I guess I would turn it
around and say, would you interpret your results exactly the same
as the modified results that I have presented below?
> The data for the senior men and women:
> 12 year citation counts:
> Males Females
> ----------------------
> 12830 2719
> 11313 1690
> 10628 1301
> 4396 1051
> 2133 935
> 893
> -----------------------
Average 7032 1539
Modified (Hypothetical ... for pedagogical purposes only ... no
hidden agenda results ...)
Males Females
34500 1500
1500 1500
1500 1500
1500 1500
1500 1500
1500
Avg 7000 1500
To me, these data are much less suggestive of general differences
in productivity between males and females, would not be an
adequate account of widespread (i.e., consistent or uniform
across individuals) differences in salaries, and so on. Am I
correct to assume that for you the consistency of the differences
between the groups (which is what a statistical test measures) is
completely irrelevant? Or are you implicitly engaging in
inferential-like thinking when you examine the actual
distributions?
> As for the notion of exploring the relationship between
> salary, gender, and performance -- I'd be more than happy
> to examine any data that MIT would make available. They
> will, of course, not make such data available. It is too
> private, they say.
But were the data made available to you, would you use any
statistical procedures in the examination? Would you care
whether the differences in salary were significant? The
differences in productivity? The differences in any number of
potential confounding variables? What about the significance and
strength of the relationships between predictors and
salary? What about whether the gender difference was significant
after productivity was entered as a covariate?
Best wishes
Jim
============================================================================
James M. Clark (204) 786-9757
Department of Psychology (204) 774-4134 Fax
University of Winnipeg 4L05D
Winnipeg, Manitoba R3B 2E9 [EMAIL PROTECTED]
CANADA http://www.uwinnipeg.ca/~clark
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