HDTenEightyP/NetNews huggingface dataset:
From: rossini@biosun4.harvard.edu (Anthony Rossini)
Newsgroups: bit.listserv.edstat-l
Subject: Re: Applied statistical work
Date: 6 Oct 92 21:27:13 GMT
Organization: Biostat Dept, HSPH, Boston MA, USA.
In-reply-to: efst03@castle.ed.ac.uk's message of 6 Oct 92 13:10:20 GMT

In article <26594@castle.ed.ac.uk> efst03@castle.ed.ac.uk (D I Sales) writes:

   In article <01GPIWEUV6SO8Y56ZL@Citadel.edu> Steve Comer <COMERS@CITADEL.BITNET> writes:

   This system leads to strong selective pressure for individuals to shun any
   inter-disciplinary work and concentrate on "pure" research.
   Thus those with
   tenure tend to be those least suited to teaching service courses for other
   departments.  A corollary of this is that journals that start out as
   "applied" tend to evolve towards the "pure" (e.g. Biometrika).

I'm not sure.  At least in my department, it seems that the people who get
the best teaching reviews tend to be tenured, and quite active in applied
statistical problems (well, one of them is a bit more theoretical than most
of the department, but he's been trying to cure himself for years).

It might be true in general, but those statements fail in some
particulars...

-tony
--
Anthony Rossini       -       rossini@biostat.harvard.edu
Department of Biostatistics, Harvard School of Public Health
677 Huntington Ave, Boston MA 02115  617-432-1056
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 6 Oct 1992 11:04:55 EDT
Reply-To:     Steven R Brown <SBROWN@KENTVM.BITNET>
From:         Steven R Brown <SBROWN@KENTVM.BITNET>
Subject:      Re: factor analysis

+---------------------------------------------------------------+
|  From Steven R. Brown, Kent State University, SBrown@KentVM   |
+---------------------------------------------------------------+

On Tue, 6 Oct 92 10:16:54 -0400 Paul Thompson said:
>
>You should get a bit more current.  Most methods used today are
>confirmatory, in which the analyst hypothesizes an overall structure (at
>least indicates what is NOT loaded on a factor), and lets the program solve
>for the parameters for the non-fixed values.  A significance test can be done
>and much discussion in the literature about the appropriate approaches

What Stephenson advocated has nothing to do with so-called "current"
procedures (LISREL et al.).  These are all in the hypothetico-deductive
framework, not in the abductory.  Stephenson also advocated centroid
factor analysis, which I suspect you would regard as even more behind
the times, but it turns out that centroid analysis, unlike the other
more determinant methods, is more compatible (precisely because of the
indeterminancy involved) with quantum theory.  With Stephenson, there
was no necessary "overall structure," but the use of factor rotation
to probe about in factor space.  That is why Thompson noted that Ste-
phenson's ideas (unlike Burt's, Eysenck's, Cattell's, and those of
others) could never be replaced by the methods which you listed since
theoretical rotation unfolds during the process of inquiry and cannot
be specified in advance.  It is inherently judgmental.  Everything is
unfixed.  There are no parameters to solve for.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 29 Sep 1992 15:19:44 -0400
Reply-To:     "George W. Cobb" <gcobb@MHC.MTHOLYOKE.EDU>
From:         "George W. Cobb" <gcobb@MHC.MTHOLYOKE.EDU>
Subject:      factor analysis

Factor analysis came up a while back as part of the discussion of who
should teach statistics.  To anyone who teaches or uses factor analysis,
whether that person be statistician, psychologist, or thespian, I
recommend an old article from The Amercian Statistician (Dec. 1967,
pp. 17-21), by J. Scott Armstrong:

Derivation of Theory by Means of Factor Analysis
                       or
Tom Swift and His Electric Factor Analysis Machine.


George Cobb
Math/Stat/CS
Mount Holyoke College
So. Hadley, MA  01075
gcobb@mhc.mtholyoke.edu
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 1 Oct 1992 12:37:00 PDT
Reply-To:     Albyn Jones <jones@REED.EDU>
From:         Albyn Jones <jones@REED.EDU>
Subject:      CI's, envelopes

Walt Pirie <WLTPIRIE%VTVM1.BITNET@ncsuvm.cc.ncsu.edu> says:

"That's not at all like the Confidence Interval problem."

on the contrary, that's exactly the "Confidence Interval problem".
Your desire to make probability statements here is exactly the same
desire that motivates students to make probability statements about
individual CI's, conditional on the location of the interval.
Frequentists can't, subjectivists use their prior to supply the
missing piece...

"Without some additional information  on the amounts involved,
your data provides no information relevant to the original probabilities"

sounds like a mad-dog bayesian attacking a frequentist here...(%^)

albyn jones
From: ECO_GXC@SHSU.BITNET
Newsgroups: bit.listserv.edstat-l
Subject: gender and math
Date: 5 Oct 92 14:27:56 GMT
Reply-To: ECO_GXC@SHSU.BITNET
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I don't recall where I saw it but I have seen some pscy research that
found that young girls outperform boys in math -- sometime around the
4th or 5th grade or so the rankings reverse and then boys stay ahead
of girls.  The implication is, of course, that some sort of socialization
process occurrs about the time of grades 1-3 which causes girls to
perform poorly in math  ----- gary carson
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: edstat-l@ncsuvm.bitnet
X-VMS-To: IN%"edstat-l@ncsuvm.bitnet"
MIME-version: 1.0
Content-type: TEXT/PLAIN; CHARSET=US-ASCII
Content-transfer-encoding: 7BIT
Newsgroups: bit.listserv.edstat-l
Date:         Sat, 26 Sep 1992 13:40:51 -0700
Reply-To:     BERGERD@CGSVAX.CLAREMONT.EDU
From:         BERGERD@CGSVAX.CLAREMONT.EDU
Subject:      The Science of Polling

All right, who taught statistics to the Science editors of Newsweek?
In the Science section of Newsweek, Sept 28, 1992, pp 38-39 is an
article "The Science of Polling."  It has a nice discussion of some
of the real-world factors that make polls less accurate than many
statisticians would like to believe - context effects, people who
lie, sampling problems, etc.  You might find the article useful
to stimulate class discussion of sampling.

Yet, the article includes a gross error.  I quote:

"The margin of error, calculated according to a textbook statistical
formula, varies inversely with the sample size.  In general, about
500 responses gives a possible error of 5 percent either way;
2500 responses decreases it to 1 percent."

I plan to have my students critique this article after we have
covered sampling.  It should help convince students that they need
to THINK when they interpret statistical presentations.

Dale Berger, Psychology, The Claremont Graduate School, CA
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Mon, 28 Sep 1992 09:10:35 EDT
Reply-To:     Walt Pirie <WLTPIRIE@VTVM1.BITNET>
From:         Walt Pirie <WLTPIRIE@VTVM1.BITNET>
Subject:      Who....

The following is from William Hart:


Harvey raises some interesting points.  I have had several excellent
teachers of statistics who were statisticians as well as some who
were clearly bored to tears teaching us applied types.  I wish
that my initial exposure to the field had included an approach
similar to that used in Miller's book "Beyond ANOVA".  I want
to know when ML methods should be used and not used; I do not want
to be involved in testing the robustness of such.  I also find that
my graduate students have not been exposed to sensitivity and
specificity, predictive value, etc in their stats courses.  These
are critical issues in my area but do not seem to be covered
in intro stats courses.  I guess I do not care (in a Hegelian sense)
who teaches stats to my students but I do care that they understand
 - and can apply - what they were taught.  I want them to be able
to read research articles and look for flaws or weak points in the
design.  My students, and I, are not going to design the next
LISREL but we need to be able to design decent experiments.  Stats
classes should help us to read the literature, conceptualize
how to test research models and to know when to yell for help from
'real statisticians'.  As I said above, I don't care who teaches
as long as this is what is taught.  (IMHO, FYI, MMNM, :-))


William D. Hart,  Saint Louis University
Sure, every subject matter discipline can point to a favorite topic that
is not covered in an intro Stat course when taught by someone not in
that discipline.  And Specificity and Sensitivity are a good example.
These techniques are for the most part used in the behavioral sciences
and not of much interest elsewhere. And there are techniques for
which the opposite is true. But that's true of any subject taught as a
service course to a general audience. And then the subject matter
discipline teaches follow-on courses using the material in ways most
common in that discipline and overlaying it with preferred terminology
and ideas most used by them. Why is it that almost exclusively in
Statistics the model of a general foundation followed by subject-matter
applications is rejected? Someone on the net last week claimed that it
was mostly a matter of "turf." I think there's some truth to that.
Walt Pirie
Va Tech, Statistics.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:          UK.AC.LE;  6 OCT 92 12:20:07 BST
Site:         University of Leicester, Leicester, U.K.
X-Name:       David D Stretch
Phone:        +44 533 522522 ext 3251, or +44 533 523251 (direct line)
Fax:          +44 533 523293
Organisation: Organisation??? Who?? Me??? Don't make me laugh!
X-Address:    Lecturer in Psychology, Dept. of Psychiatry,
X-Address:    Clinical Sciences Building, Leicester Royal Infirmary,
X-Address:    PO Box 65, Leicester, LE2 7LX, United Kingdom.
X-Mailer:     ELM [version 2.3 PL0 (LUT)]
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 6 Oct 1992 12:19:07 BST
Reply-To:     dds@leicester.ac.uk
From:         David Stretch <dds@LE.AC.UK>
Subject:      Factor Analysis, etc.

I would just like to add my voice to the people who have, quite rightly,
pointed out that Factor Analysis can be misused, and has, in my opinion,
been overused in the past. However, I would like to make the point that
the fact that Factor Analysis *has* been misused does not imply that it
can never be used correctly.

This point may seem obvious to many, but I feel it is worthwhile making
it, in case some people immediately overgeneralize and start thinking
that *anything* which could be done and which used Factor Analysis is
necessarily fundamentally flawed - This would be another inappropriate
dogma, like the one I believe underlies the nominal/ordinal/interval/ratio
scale prescriptions that take no account of the purpose or meaning of the
observations to be analyzed.

David Stretch

  +-----------------------+---------------------------------------------+
  |  Dr. David D Stretch  | I was gratified to be able to answer        |
  |  Dept. of Psychiatry  | promptly, and I did. I said I don't know.   |
  |  dds@leicester.ac.uk  |                                  Mark Twain |
  +-----------------------+---------------------------------------------+
From: jerry@hnrc.tufts.edu (Jerry Dallal)
Newsgroups: bit.listserv.edstat-l
Subject: Re: Envelope questions
Date: 3 Oct 92 19:06:13 GMT
Organization: USDA HNRC at Tufts University

In article <199210021318.AA01810@world.std.com>, mikeh@WORLD.STD.COM (Mike M Harde) writes:
> Re: "probabilities"
> 
> Suppose the envelopes are marked "A" and "B".  Then by Jerry Dallal's
> definition of probability, is the probability that the higher amount is in
> A  either 1 or 0?  We just don't know which?  Has it been determined since
> there is no physical process that can change it?  If so, is there no
> random event left?


Let Ai = envelope A contains higher amount in experiment i.
Let there be a series of experiments in which someone places two different
amounts of money in two envelopes at random.  (Always the same two amounts.)
Then P(A) = 0.5.  Half the time envelope A *will* have the higher amount.

But in the envelope paradox, we *know* the *amount* in our envelope.  Ai is
now "other envelope contains $200 given that A has $100 dollars".  From a 
*frequentist* standpoint, P(envelope B contains $200|A contains $100) = 0 or 1.
Why?  Because in a sequence of experiments in which
Ai contains $100, *all* envelope B's will contain $50 or 
*all* will contain $200.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 6 Oct 1992 10:16:54 -0400
Reply-To:     "Paul A. Thompson" <pat@po.CWRU.Edu>
From:         "Paul A. Thompson" <pat@PO.CWRU.EDU>
Subject:      Re: factor analysis

>But Armstrong's stance is only slightly less passive than the factor-
>analytic strategy against which he argues since his hero Tom Swift could
>conceivably stand next to the computer and wait 'til the cows come home
>for it to produce results that comport with his theory.  A more active
>tack is to rotate the factors theoretically -- remember that old skill,
>from back in the '40s? -- to see if the factors as extracted will sup-
>port one's theory.  This is an active rather than passive stance vis-a-
>vis factor analysis, and the only prominent factor analyst of which I am
>aware who consistently advocated this was the late William Stephenson,

Also John B. Carroll, U N Carolina, uses this method

>who tied judgmental rotation to Charles Peirce's theory of abductory
>logic (in contrast to Armstrong's ad hoc inductive logic).  One of the
>best defenses of Stephenson's view was in an article by John W.
>Thompson, "Meaningful and Unmeaningful Rotation of Factors," _Psycholog-
>ical Bulletin_, 1962, 59, 211-223.  Armstrong's position is certainly
>better than the wait-and-see head-in-the-sand position of most factor
>analyses (even today),

You should get a bit more current.  Most methods used today are
confirmatory, in which the analyst hypothesizes an overall structure (at
least indicates what is NOT loaded on a factor), and lets the program solve
for the parameters for the non-fixed values.  A significance test can be done
and much discussion in the literature about the appropriate approaches

The following programs do confirmatory analysis:
    EQS - Bentler
    LISREL - Joreskog and Sorbom
    CALIS  - SAS (Hartman)
    RAM    - McDomald and McArdle
    COSAN  - maybe same
    there is a program by Steiger that I can't remember


--
Paul Thompson, Ph.D.  |  Department of Psychiatry  |  (216) 844-7463
Case Western Reserve University  | Cleveland, OH  44106
------------------------------------------------------------------------------
When I hear "family values," I reach for my revolver. - para. Hans Jonst
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Mailer: ELM [version 2.3 PL11]
Newsgroups: bit.listserv.edstat-l
Date:         Sat, 3 Oct 1992 08:16:15 EDT
Reply-To:     Jon Vaughan <jvaughan@ITSMAIL1.HAMILTON.EDU>
From:         Jon Vaughan <jvaughan@ITSMAIL1.HAMILTON.EDU>
Subject:      Re: men, women, and stats

John Wendell recently wrote, in part:
> Mathematical reasoning is one where men tend to do
> better than women and at the high end of the distribution the ratio of
> males to females is 13:1.

He wrote in the context of the Barbie Doll controversy and recent
studies of gender differences in brain structures (see his post for the
full context).

Like many statistics, the appropriate interpretation of this ratio
depends greatly on how it was measured, and the definition of 'high
end' of performance on standardized tests, which I won't get into here.

There's another issue, here, though, which is what is the appropriate
measure (if any) for comparisons of performance across genders.
Paradoxically, other measures of mathematical performance show female
advantage.  If we used classroom performance as our touchstone, we would
conclude that women are better at math than men, because they get better
grades in math courses.  (see an article by Meredith Kimball about 2
years ago in the American Psychologist for a fuller discussion of the
grade/test paradox). Presumably if we emphasized classroom performance we
would be inclined to interpret the differences found between male and female
brains as the indicators of female math superiority.

One can only speculate that one component of the poorer standardized
test performance of girls (why they might do less well than their grades
would predict) is the incremental anxiety they might feel in
the testing situation as a result of having repeatedly heard, from Barbie
and other cultural icons, that math is hard for girls.
--
Jonathan Vaughan       Psychology, Hamilton College        jvaughan@hamilton.edu
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
From: ST9PH@PRIMEA.SHEFFIELD.AC.UK (P.Holmes)
Newsgroups: bit.listserv.edstat-l
Subject: Teaching Statistics
Date: 6 Oct 92 09:45:56 GMT
Reply-To: "P.Holmes" <ST9PH@PRIMEA.SHEFFIELD.AC.UK>
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:      UK.AC.SHEF.PA;  6 OCT 92  9:07:15 BST

Since my name has occurred twice recently in connection in connection with
the journal 'Teaching Statistics' I am writing to make the position clear.

The Editor is David Green at the University of Loughborough , email address
is D.R.GREEN@LUT.AC.UK. Any correspondence about the journals content,
including articles for possible publication should be sent to him.

I was the editor until about 5 year ago. I am now the circulation manager
and responsible for most of the administration of the journal. Any requests
for information about subscriptions, back issues etc should be sent to me.
My email is st9ph@pa.shef.ac.uk and snail mail is Centre for Statistical
Education, University of Sheffield, Sheffield S3 7RH, UK.

Peter Holmes
From: johan.bring@statistik.uu.se (Johan Bring)
Newsgroups: bit.listserv.edstat-l
Subject: Re: The envelope problem
Date: 30 Sep 92 15:32:07 GMT
Organization: Dept. of Statistics, Uppsala University

To answer the paradox about the envelopes I would like to repeat the 
paradox once more.

I randomly choose an a amount oof dollars X and put them in one envelope.
I then put 2X in another envelope. Without knowing the amounts in any of the
envelopes you get to pick one. After opening that envelope you get the 
opportunity to switch. And the paradox is that independently of how much
money you find in your envelope it is always rational to switch. Assume 
that you find Y dollars in your envelope. Then it is either 2Y or 0.5Y in
the other. Both possibilities are equally likely. So the expected reward if
you switch is +*2Y++*0.5Y=1.25Y which is more then you have. So you don't
even have to open your envelope you can switch without seeing how much
money there is in the envelope.
The answer to the paradox is in the first sentence "randomly choose". How did I 
randomly choose? To make the problem into an paradox we have to assume to all
amounts are equally likely. WE could almost say that I randomly pick an
integer between 1 and infinity and put that amount in the first envelope. 
But it is impossible to randomly pick an integer between 1 and infinity
if all integer shall have the same probability of being choosen. Because if 
they all have an probability larger then zero the sum of all probabilities
will add to more then one. And if they all have zero probability it is
impossible to choose an integer. If I dont use a uniform probability
distribution over the possible amounts to put in the first envelope or
if I put an upper limit of possible amounts then the paradox disappears.
I thik this problem is extensively analysed in "Bayesian Statistics,
proceedings of the third Valencia international meeting." edited by
Bernardo.

Johan Bring
Dep. of Statistics
Uppsala, Sweden
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: edstat-l@ncsuvm.BITNET
X-VMS-To: STAT
MIME-version: 1.0
Content-transfer-encoding: 7BIT
Newsgroups: bit.listserv.edstat-l
Date:         Sat, 3 Oct 1992 21:57:02 -0500
Reply-To:     Arnie Kahn <FAC_ASKAHN@JMUVAX.BITNET>
From:         Arnie Kahn <FAC_ASKAHN@JMUVAX.BITNET>
Subject:      gender & math performance

>I too am curious what the social scientists think about the observed
>differences between men and women in mathematical ability.  It
>appears to me that the experiments one would like to do, involving
>random assignment of babies (possibly at the point of conception)
>to environments will simply never be done.  Hence it will be
>difficult to make any statements about the biological basis of
>ability, or other behavioral traits.
>
[much deleted]
>albyn jones
>jones@reed.edu

Not only that, you would have to hold constant all gender stereotypes.  They
could never watch TV or movies, read popular magazines or fiction, etc.  The
fact is that we live in a gendered world--we even have different hair coloring
products for women and men.  The general message that is conveyed from the time
of birth is that men are competent and women are not...women need a man.

In class I often use this analogy:  You have 2 seeds.  You plant seed A in soil
P and use fertilizer X.  You plant seed B in soil Q and use fertilizer Y.  The
seeds grow into two somewhat different looking plants.  Were the two seeds
different to begin with?

Arnie
********************************************************************************
Arnie Kahn, Psychology, JMU, Harrisonburg, VA 22807     (703) 568-3963 - day
fac_askahn@vax1.acs.jmu.edu (preferred)                 (703) 434-0225 - night
fac_askahn@jmuvax                                       (703) 568-3322 - fax
********************************************************************************
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: edstat-l@ncsuvm.bitnet
X-VMS-To: IN%"edstat-l@ncsuvm.bitnet"
MIME-version: 1.0
Content-type: TEXT/PLAIN; CHARSET=US-ASCII
Content-transfer-encoding: 7BIT
Newsgroups: bit.listserv.edstat-l
Date:         Sat, 3 Oct 1992 20:37:07 FULL
Reply-To:     Steve Comer <COMERS@CITADEL.BITNET>
From:         Steve Comer <COMERS@CITADEL.BITNET>
Subject:      Applied statistical work

How does an applied statistician document "scholarly" work?
For tenure or promotion most departments and colleges look for evidence of
scholarship.  In the case of an applied statistician located in non-statistic
departments (eg., mathematics) what type of evidence of scholarly work is
normally acceptable?  I'm asking about items other than papers in refereed
journals.  Is it normal to cite papers by others who acknowledge help with
statistical calculations?  Does one include consulting work for companies
where the results and data are confidential and not subject to peer review?
What is "scholarship" for an applied statistician?
        S. D. Comer
        The Citadel,  Charleston, SC 29409
        Internet:  comers@citadel.edu
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU