Thanks again for the clarification, Jim. I think we
are in essential agreement.
To reply succinctly to your message:
1. Certainly, as a general rule
one should *always* look at distributional
shape as well as summary statistics. Feminists seldom
do, by the way, in advancing arguments about
discrimination. Indeed, as those of us who've
heard the "women make 73 cents on the dollar"
mantra for years know all too well, they'll
repeat the most inane statistic if it sounds
good.
2. In the MIT data, Mr. Ulrich seemed to be implying
that the mean differences *favoring* the men might be
due to one or two outliers. However, there is a serious
question whether the men in the 10000 range should actually
be considered outliers. If you don't want to address that,
fine. It seemed like you were agreeing with his position.
It now seems you were not. Sorry if I misread.
3. In the modern academic environment, I think that Nobel
Prize Winners generally make above average salaries, and
tend to be highly productive people as well. I may be wrong,
but some data I've seen suggest otherwise.
4. I probably would not be inclined to use formal inferential
procedures with the MIT data, even if it were provided. Keep in
mind that, in a perfectly fair society, there is a "balance of
unfairness." What I'd probably do is a regression analysis,
and try to decide, on the basis of some fairly extensive consultation,
when a residual is large enough to merit recompense.
There is a real problem with some of the recommendations
recently agreed to at MIT. Salaries have a natural error variance,
if you take two groups of "equally" performing people,
they will almost certainly have differences both in pay and
in performance. The way it now stands, feminists planning
to use MIT as a template want the right to demand a pay increase
anytime they can identify a salary decrement, regardless of
(a) whether any performance figures have been taken into account,
and (b) whether "natural variation" has been examined. Similar
venues are not open to men. So, in the future, we may find
rapid "fixing" of even minor, well-deserved differences when
women find themselves on the short end, but no such "fixes"
when men find themselves on the short end. This merely perpetuates
more unfairness, and will almost certainly result in a backlash
some time in the future.
BTW, I would like to rebut any notion that I am, in general, against
salary equity procedures. It is a matter of record that, in 1988,
when serving as a member of the salary negotiation team at UBC,
I pointed out that an across-the-board raise of $2700, requested
for all women, would unfairly benefit those who had started
working recently, and not make up the balance for those who had
been working there a long time. As a result, a regression based
procedure was adopted that more equitably distributed the money.
I supported this, as did most of the other members of the team,
among them several women.
What I am against is poorly designed, unfair procedures that reward
people solely on the basis of their race or gender and their
willingness to gripe.
Best regards,
Jim Steiger
--------------
James H. Steiger, Professor
Dept. of Psychology
University of British Columbia
Vancouver, B.C., V6T 1Z4
-------------
Note: I urge all members of this list to read
the following and inform themselves carefully
of the truth about the MIT Report on the Status
of Women Faculty.
Patricia Hausman and James Steiger Article,
"Confession Without Guilt?" :
http://www.iwf.org/news/mitfinal.pdf
Judith Kleinfeld's Article Critiquing the MIT Report:
http://www.uaf.edu/northern/mitstudy/#note9back
Original MIT Report on the Status of Women Faculty:
http://mindit.netmind.com/proxy/http://web.mit.edu/fnl/
On Mon, 12 Mar 2001 13:10:47 -0600, jim clark <[EMAIL PROTECTED]>
wrote:
>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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