HDTenEightyP/NetNews huggingface dataset:
Newsgroups: bit.listserv.stat-l
Date:         Mon, 27 Jul 1992 11:53:16 -0400
Reply-To:     pgill@kean.ucs.mun.ca
From:         pgill@KEAN.UCS.MUN.CA
Subject:      Is it good agreement ?

   A researcher has developed a questionaire containing 25 questions.
Each question refers to an action in a given situation.  The
respondent would answer whether the action is appropriate (1)
or not (0).  Before sending the questionaire to the field, the
researcher wanted to test the questionaire on a panel of 5 experts
including herself.  The following are the responses of the experts,
expert 1 being our researcher :

Expert \        R E S P O N S E S  TO  Q U E S T I O N S
_____________________________________________________________________

  1        0 0 1 0 1  1 0 1 1 0  1 1 0 0 1  1 0 1 0 0  0 1 1 0 1

  2        0 0 1 0 1  1 1 1 1 0  0 1 0 0 1  1 0 1 0 0  0 1 1 0 1

  3        0 0 1 1 1  1 0 1 1 0  1 1 0 0 0  1 1 1 0 0  0 1 1 0 1

  4        0 0 1 0 1  1 0 0 1 0  1 1 0 0 1  1 0 1 0 0  0 1 1 0 1

  5        0 0 1 0 1  1 0 1 1 0  1 1 0 0 1  1 0 1 0 0  0 1 1 0 1
_____________________________________________________________________

   The following table the number of times (out of max 25) a pair of
experts agree on the appropriateness of the action :

  EXPERT          1       2      3      4     5
  --------------------------------------------------------
    1             -      23     22     24     25
    2                     -     20     21     23
    3                            -     21     22
    4                                   -     24
  --------------------------------------------------------

   It is reasonable to assume
   Prob{ two given experts agree on a given question by chance} = 0.5.
   The Binomial probablity of more than or equal to 20 (pure chance)
   agreements between two given experts is (approximatley) 0.0026.

  QUESTION 1. Does there seem to be a good agreement among the five
  experts ?  Moreover, all five experts agree 19 out of 25 times.
  The Binomial probablity that all five experts agree by chance on
  a given question is (.5)**5 = 0.03125.  Therefore, the probablity
  that all five experts agree (by pure chance) 19 or more times out
  of 25 times is almost 0.

  QUESTION 2.  The researcher tried to use Cohen's kappa as an
  indicator of agreement among the experts and found it to be
  negative.  Is Cohen's kappa approprite for binary agreement ?

 Any comments are welcome.

P. S. Gill
Dept of Maths & Stats
Memorial Univ of Newfoundland
St. John's   CANADA
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 31 Jul 1992 13:41:45 +1200
From:         brobinso@LEVEN.APPCOMP.UTAS.EDU.AU
Subject:      tetrachoric correlation
X-To:         STAT-L@VM1.MCGILL.CA

Can anyone please supply a good reference to tetrachoric and/or polychoric
correlation coefficients? I have a colleague who has consulted 20
statistics texts without success.

Thanks!

Barrie.


--
Barrie Robinson,                  |email:
brobinso@leven.appcomp.utas.edu.au
University of Tasmania at Launceston.  |phone:  (61)(003)260211
From: den0@ns1.cc.lehigh.edu (DEAN E. NELSON)
Newsgroups: bit.listserv.stat-l
Subject: Re: Bonferroni adjustments for annual results...
Date: 22 Jul 92 20:47:29 GMT
Organization: Lehigh University

In article <STAT-L%92071910460529@VM1.MCGILL.CA>, ECOOCH@PENNSAS.UPENN.EDU (Evan
 Cooch) writes:
>
>  So, I end up with a large table, one row for each year, with the SS, F and P
>values noted. Say I have 30 years. In your opinion, do the individual year
>significances need to be adjusted for the number of years presented?
>
>I have my opinion on this (and I won't tip my hand yet). One approach is to
>ignore the issue and present both within year probabilities, and the
>Bonferroni significances of same. Obviously, this begs the issue of whter you
>need to adjust in the first palce or not.

The Bonferroni adjustment comes from an inequality, and thus ensures an
overall error rate will be below some specified alpha, but it could be a lot
lower, and hence it is notoriously conservative (unless you are doing safety
studies, in which case it is liberal).

A confounding factor in your case is whether the relation of X and Y from years
near each other are correlated, which I believe makes the Bonferroni even
more conservative.

We once did some simulations comparing the bonferroni with other real and
heuristic methods and found it to be highly conservative under the range
of alternatives (in the specific experimental design we were looking at)
we tried.

So, even if philosophically you buy the multiple comparisions argument,
experience would seem to point you to another form of adjustment.

-- 
Dean E. Nelson  den0@lehigh.edu  (Internet)
(215)-758-4988  (That is d-e-n-zero, not the letter O)
 
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Date:         Tue, 21 Jul 1992 07:15:54 EDT
Comments:     Resent-From: Linda Atkinson <ATKINSON@ERS>
Comments:     Originally-From: William Greene <wgreene@option.stern.nyu.edu>
From:         "Linda Atkinson" <ATKINSON@ERS.BITNET>
Subject:      LIMDEP

I was asked by the vendor of LIMDEP to send this out for the benefit
of those who are having difficulty installing the new mainframe version.

+----------------------------------------------------------------------+
| Linda Atkinson, Statistician     | BITNET:  ATKINSON@ERS             |
| U.S. Department of Agriculture   | Tel:     (202) 219-0505           |
| Economic Research Service        | FAX:     (202) 219-1292           |
| Washington, D.C.                 |          (ATTN: RM 240)           |
+----------------------------------------------------------------------+

----------------------------Original message----------------------------
Linda:  (Listen carefully and see if you can pick up my frustration.)
	The problem you are having with LIMDEP, as are several other
IBM system users, is due to a quirk in IBM's Fortran.  I won't call it
a bug, but it is unique to IBM.  The problem is this:  I am trying to
read your data as a string of characters, so that LIMDEP can interpret
it and free you from having to provide a format -- i.e., an unformatted
read.  Since I do not know how many characters there are on a line, or
even if there will be the same number of characters on each line, I
try to read 255 characters.  If the line contains less than 255 characters,
here is what happens:
	IBM mainframes:  Program crashes, as you saw
	All other types of mainframes, and all PCs:  Program reads as
		many characters are as present, and fills remainder
		with blanks.
I suppose I will have to program around this.  If I am successful, I will
let you know.
	Perhaps you could send the previous paragraph out on your network.
(I do not access it.)  In any event, it does not have to do with being
unfamiliar with IBM systems.  I know more about them than I wish to  right
now.
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Newsgroups: bit.listserv.stat-l
Date:         Wed, 22 Jul 1992 10:15:20 PDT
Comments:     Warning -- original Sender: tag was NETNEWS@UVVM.UVIC.CA
From:         Melvin Klassen <KLASSEN@UVVM.BITNET>
Subject:      Re: LIMDEP

Linda Atkinson <ATKINSON@ERS> writes:
>This is a second message I received from Dr. Greene about LIMDEP, with
>more detailed information on work-arounds for the mainframe version.
>----------------------------Original message----------------------------
>        The problem you are having with LIMDEP is now very familiar to me,
>and is arising from two sources:  (1) An incompatibility between IBM's Fortran
>and every other Fortran with which I am familiar, which, as you can
>imagine, is a lot and (2) A bug in the IBM fortran compiler which, as far
>as I know, has always been present, and shows no sign of being eradicated.
                                         ?????????????????????????????????
> ...
>        IBM fortran (MVS, CMS, all of them): Program crashes, as you saw.
>        All other fortrans:  Unread characters are filled with blanks.
>Now, notice that the statement contains an ERR=992.  This is designed to
>prevent programs which encounter data errors from crashing. It is not working.
>That is the bug.  There is no ambiguity about it as far as I can see.
>The odd thing about this, once again, unique to IBM, is that this
>whole problem disappears a) if the 255 above is changed to an 80, whether
>or not the line read is actually 80 characters long or not and b) if
>the line really is 255 characters long, for obvious reasons.
> ...
>Once again, it is a mystery to me why IBM has locked itself into this 80
>character format, but apparently, it is the bottleneck.  Keep in mind,
>again, for unformatted data sets, you are limited to 80 character lines.

All these problems with FORTRAN and LIMDEP can easily be solved if the user
of LIMDEP is using the current release of IBM's VS FORTRAN Program Product.
In 1990, IBM fixed the problem in Release 3 of Version 2 of their product.
Note that both Release 3 and Release 4 have now been obsoleted
by Release 5 of their product.

The solution is just to specify the "CNVIOERR" run-time option
(as documented on page 92 of "VS FORTRAN Version 2, Programming Guide
for CMS and MVS, Release 5").  Then, FORTRAN error 212 (end-of-record)
**will** be intercepted by the 'ERR=' option.

As an alternate solution, the user can turn to page 96,
specify the "RECPAD" run-time option, and FORTRAN will perform blank-padding.
Newsgroups: bit.listserv.stat-l
From: H.Edwards@massey.ac.nz (H. Edwards)
Subject: Positions in stochastic OR and Statistics
Organization: Massey University, Palmerston North, New Zealand
X-Reader: NETNEWS/PC Version 2c
Date: Tue, 28 Jul 92 04:36:09 GMT

Massey University, Palmerston North, NewZealand.
Department of Statistics

Lectureship in Operations Research, and Lectureship/Senior Lectureship in
Statistics.

The Department of Statistics has two newly created positions in Operations
Research and Statistics.  Applicants should have a PhD and/or teaching,
research or consulting experience in one of these areas.  The Department is
particularly interested in seeking candidates with expertise in one or more
of the following: Stochastic Operations Research (applied probability,
decision theory), Business Statistics (statistical process control, total
quality improvement methods), and Applied Statistics (including statistical
consulting).  The Department teaches undergraduate postgraduate and service
courses, and supervises research degrees to the PhD level.  Courses are also
taught by correspondence throughout New Zealand.  The Department is interested
in expanding its consulting activities, and the position in Statistics may be
offered at Senior Lecturer level to a suitably qualified and experienced
applicant.

Further information on teaching and research in the Department may be obtained
from the Head of Department, Professor Jeffrey J Hunter (Phone: 64 6 350-5082,
Fax: 64 6 350-5611, E-mail: J.Hunter@massey.ac.nz).
Reference number 51 (O.R.) or 52 (Statistics) must be quoted.

Closing date:  15 September 1992.

Further details of these positions together with Conditions of Appointment
are obtainable from Mrs V B Bretherton, Personnel Section, to whom
applications, including a full curriculum vitae and the names, addresses and
fax numbers of three referees, should be sent.
-- 


Howard Edwards				Ph: (64)(6)3569099 ext 8331
Dept of Statistics			Fax: (64)(6)3505611
Massey University			email: H.Edwards@massey.ac.nz
Palmerston North
NEW ZEALAND
From: jerry@nutmeg.hnrc.tufts.edu (Jerry Dallal)
Newsgroups: bit.listserv.stat-l
Subject: Re: Multiple comparisons w/ repeated measures or split plotstructures
Date: 22 Jul 92 17:04:16 GMT
Organization: USDA HNRC at Tufts University

In article <920722083615.202116e5@UNHH.UNH.EDU>, W_URBAN@UNHH.UNH.EDU writes:
> 
>     n.b.  With unequal numbers of subjects per treatment, no two
>     methods/packages that I have used have given the same F ratios.
> 
>     Willard Urban
>     Biometrics
>     University of New Hampshire
> 
>     will@unh.edu


They should.  You should post a SMALL data set and some sample command files.
This would make a nice project for the newsgroup--to run the data set on a
variety of packages to see which programs disagree and try to learn why.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Mon, 27 Jul 1992 18:05:37 U
From:         Paul Velleman <paul_velleman@QMRELAY.MAIL.CORNELL.EDU>
Subject:      Fixed regressions

 Subject:
           Fixed regressions
John J. Wiorkowski says of Lotus that "They have obviously fixed the bug in
the algorithm which existed in Ver. 2.1." based on one small trial.
First: I don't believe that there really was a bug, just a poor choice of
algorithm. (For those of you who have been around from the start of the
spreadsheet debates, this is just one more example of how tricky it can be to
get the algorithm right.)
Second: If they have changed the algorithm, it might still not be stable
against other challenges, or against the same challenge carried just a bit
further. Have they documented their algorithm? Do we know what it will do when
(not if) it breaks down? (*All* algorithms for regression break down somewhere.
The trick is knowing when and failing gracefully.) Is the behavior of the
algorithm dependent on the floating point chip?
Finally: Just one more plug. If you want to do accounting, by all means use a
spreadsheet. If you want to do statistics, you will be *much* better off
getting a statistics package designed and written by professional
statisticians. There is much science and a bit of art behind the choice and
implementation of algorithms and a wide enough array of packages to choose from
to please almost anyone.
  -- Paul Velleman
(Claimer: As the developer of the Data Desk exploratory data analysis package
for the Mac, I am biased in favor of professionally developed software.)
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X-Mailer: ELM [version 2.2 PL11]
Newsgroups: bit.listserv.stat-l
Date:         Fri, 24 Jul 1992 08:14:47 EST
From:         "D. Glen McPherson" <mcphers@HILBERT.MATHS.UTAS.EDU.AU>
Subject:      Re: "s-mode" and "t-mode" in principal component analysis (fwd)
X-To:         "stat-l list" <stat-l@vm1.mcgill.ca>

Forwarded message:

From HARVEYRJ@VTVM1.CC.VT.EDU Thu Jul 23 21:19:12 1992
Message-Id: <9207231119.AA15315@hilbert.maths.utas.edu.au>
Date:         Thu, 23 Jul 92 07:15:24 EDT
From: rj <HARVEYRJ@VTVM1.CC.VT.EDU>
Subject:      Re: "s-mode" and "t-mode" in principal component analysis
To: "D. Glen McPherson" <mcphers@hilbert.maths.utas.edu.au>

hi again:
  it just struck me that a more general treatment on the r v. q and
s v. t kinds of factors (components) can be found in Tucker's work on
three-mode factor analysis; the main paper is Tucker, L. R., (1966, I
think), _psychometrika_, title three-mode factor analysis.  as I recall,
at the end of that paper he discusses issues involved in analyzing and
interpreting the factors of a "time mode" of data, which is the basic
problem addressed by s and t-mode analysis.  hope this helps!

r. j. harvey
virginia tech, psychology
blacksburg, VA 24061-0436


--
 ---------------------------------------------------------------------------
|  Glen McPherson            |                                   |  _--_|\  |
|  Department of Mathematics |-----------------------------------| /      \ |
|  University of Tasmania    |               E-Mail:             | \_.--._/ |
|  Australia.                | mcphers@hilbert.maths.utas.edu.au |       *  |
 ---------------------------------------------------------------------------
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Acknowledge-to:    XIC@NIHCU.BITNET
X-Acknowledge-to:  XIC@NIHCU.BITNET
Newsgroups: bit.listserv.stat-l
Date:         Mon, 27 Jul 1992 10:00:34 EDT
From:         XIC@NIHCU.BITNET
Subject:      Judgmental sampling - reply

This note is in response to the query of John Wendell on
judgmental vs. probability sampling.

The best discussion that I know of is in section 1.6 of the
book by Raymond J. Jessen, Statistical Survey Techniques (John
Wiley and Sons, 1978).

Jessen describes an experiment in which participants were
asked to select stones from a population of 126 stones based
on judgment.  They did better (in terms of mean weight, I
believe) than a probability sample only when the sample size
was less than 7.

from Michael P. Cohen, Mathematical Statistician
Statistical Standards and Methodology Division
National Center for Education Statistics
Washington, DC 20208-5654
Bitnet:  XIC@NIHCU
Internet:  XIC@CU.NIH.GOV
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Date:         Thu, 30 Jul 1992 14:38:00 EDT
From:         Rick Troiano <XETJ@CORNELLA.BITNET>
Subject: Re: interaction effects in regression models
X-To:         N o s t a l g i a <matsuda@LINC.CIS.UPENN.EDU>

On Thu, 30 Jul 1992 13:06:56 -0400 Nostalgia (matsuda@LINC.CIS.UPEN.EDU) said:
>Supposing that there are some grounds in putting the non-significant variable
>which turned out to interact with some other variable(s) in the model together
>with the interaction term, how would you deal with the multicollinearity that
>would surely ensue between that non-significant variable and the interaction
>term?

One way to avoid (or greatly decrease) the multicollinearity when
interactions or polynomial terms are involved in a model is to
center the main effect variable at its mean, or at a meaningful
value close to the center of the distribution of values in the
sample.

For a discussion of controversy surrounding this approach, see
The American Statistician, May 1984, 38(2) 73-93.

Also pertinent to this discussion is the Hierarchy Principle
which states that given any variable in a model, all lower
order components of the variable must also be contained in the
model.  The rationale for this principle has been noted
in other responses without mention of the principle itself.
>
>Kenjiro Matsuda
>Dept. of Linguistics
>Univ. of Pennsylvania
>matsuda@linc.cis.upenn.edu

Rick Troiano
Division of Nutritional Sciences
Cornell University                  Bitnet:   XETJ@CORNELLA.BITNET
Ithaca, New York 14853              Internet: XETJ@CORNELLA.CIT.CORNELL.EDU
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Newsgroups: bit.listserv.stat-l
Date:         Thu, 30 Jul 1992 13:06:48 EDT
From:         MSTRATIL@PEMBVAX1.BITNET
Subject: Re. Kaufman's "Finding Groups in Data"

I am currently reading L. Kaufman's "Finding Groups in Data" (Wiley) and would
very much like to obtain a copy of the source code for the programs (e.g. PAM)
that he describes.  So far I have not been able to make contact with
Professor Kaufman directly.  If anyone has a copy of these, I would heartily
appreciate your sending a copy to:

     mstratil@pembvax1.pembroke.edu

To avoid redundant efforts, I will send a notice to list subscribers
after receiving the first copy.  Thanks.

Mike Stratil
Department of Psychology
Pembroke State University
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X-Mailer: Mail User's Shell (7.2.3 5/22/91)
Newsgroups: bit.listserv.stat-l
Date:         Tue, 28 Jul 1992 10:46:22 -0400
From:         "Timothy F. Tolson" <tft8g@HOLMES.ACC.VIRGINIA.EDU>
Subject:      Test, please ignore
X-To:         stat-l%mcgill1.BITNET@uvaarpa.Virginia.EDU

This is a test, do not attempt to eat your pencil
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Date:         Mon, 27 Jul 1992 12:41:32 -0500
From:         Kernon Gibes <gibes@SWIRL.MONSANTO.COM>
Subject:      RE: Is it good agreement ?
X-To:         stat-l@vm1.mcgill.ca@tin.monsanto.com
X-cc:         GIBES@tin.monsanto.com

        Regarding P. S. Gill's "good agreement" question, I have some
        non-statistical questions:

        As stated, the point of submitting the questionnaire to 5
        experts was to "test" the questionnaire.  But, I'm wondering,
        *IF* the objective of the questionnaire is attitude assessment,
        what could be inferred from agreement OR disagreement among N=5?
        These 5 people may very well tend to have the same assessments
        as to appropriateness of the actions presented insofar as they
        are, at best, a (non-random?) sample of the population of
        experts.  Alternatively, if they disagree, does this imply that
        the questionnaire somehow fails to pass this "test" or merely
        that the questionnaire is very good at presenting situations for
        which no wide consensus exists?

        Kernon Gibes
        Internet: gibes@swirl.monsanto.com
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Date:         Thu, 30 Jul 1992 11:27:26 EDT
From:         Marshall Feldman <MARSH@URIACC.BITNET>
Subject:      Re: interaction effects in regression models

Three comments on the question of including insignificant variables
that become significant in interactions:

  1) I don't think one can make hard and fast rules, such as always include
     or never include the variable.  The choice has to depend on the
     underlying theory (e.g., race by itself may be a significant
     predictor of income, but when race interacts with class and
     education, race by itself may become insignificant; this would
     be an important finding).  So there is no hard and fast rule.

  2) Generally, however, including both the variable and it interaction
     demonstrates that the interaction is not acting as a proxy for the
     variable.

  3) Reviewers usually feel they have to say something intelligent or they
     are not doing their job.  I am certain that many refereed articles
     are needlessly revised because of some reviewers' urge to seem
     to contribute to the process.  In short, the best strategy for you
     might be to have a table with the results of three regressions
     (no interaction, interaction only, and both interaction and plain
     variable).  Give the referee what s/he wants, but interpret the model
     you feel is most meaningful based on your understanding of the
     problem.

Marsh Feldman
Community Planning                      Phone: 401/792-2248
204 Rodman Hall                           FAX: 401/792-4395
University of Rhode Island           Internet: marsh@uriacc.uri.edu
Kingston, RI 02881-0815
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Date:         Thu, 30 Jul 1992 09:49:00 PDT
From:         Albyn Jones <jones@REED.EDU>
Subject: Re: interaction effects in regression models
X-To:         A716HOX%HASARA11.bitnet@VM1.MCGILL.CA, STAT-L@MCGILL1.BITNET
X-cc:         A716HOX@HASARA11.BITNET

the reviewer is full of hooie (a technical term i learned growing up
in an agricultural region).  "not significantly different from 0"
is not the same thing  as "surely equal to 0"; if you fit a model
leaving out terms which should be there (eg. main effects which
are not statistically significant, but still not zero) then the
estimates of the remaining coefficients that aren't orthogonal to
those factors are biased, and it may be hard to tell how much the
bias is.  a related issue: if your design involves a blocking factor,
should you include the main effect for the blocking factor in the
model even if its non-significant?  i'd say yes, because you get an
inflated estimate of the error variance if you omit the block
effects from the model:  the model should reflect the design!

albyn jones
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Date:         Wed, 29 Jul 1992 15:30:02 EDT
From:         George McCarthy <mccarthy@LEVY.BARD.EDU>
Subject:      community
X-To:         stat-l@vm1.mcgill.ca

Greetings,

I have an unusual request.  A colleague asked me if I had any cites for what
he called 'a statistical definition of community.'  Knowing that I did
statistics, he assumed I would know immediately what this means.  My first
impulse was to see if anyone had defined community by clustering or
networking, but I'm unfamiliar with this literature.  Has anyone got any
ideas about this?

Thanks.
--Mac. <mccarthy@levy.bard.edu>
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