HDTenEightyP/NetNews huggingface dataset:
Newsgroups: bit.listserv.advanc-l
Date:         Wed, 11 Nov 1992 13:52:14 PST
              <ADVANC-L@IDBSU.BITNET>
From:         Debbie Hackleman <hacklemd@CCMAIL.ORST.EDU>
Subject:      Re: Cataloging module

          If Fresno and Matt Goldner have figured out how to put MARC
          subfields for a tag all on one line in the MARC display, I
          would like them to post the method to the list.  I know this
          is possible on the Detailed Display, but I wasn't aware that
          the MARC display itself could be adjusted to appear more
          like OCLC.

          Debbie Hackleman
          Oregon State University
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Mailer: ELM [version 2.3 PL11]
Newsgroups: bit.listserv.edstat-l
Date:         Fri, 6 Nov 1992 21:20:15 EST
Reply-To:     Bob Hayden <hayden@OZ.PLYMOUTH.EDU>
From:         Bob Hayden <hayden@OZ.PLYMOUTH.EDU>
Subject:      no subject (file transmission)

There was a discussion a while back about degrees of freedom.  Here's
a completely different approach to explaining why we calculate
variances with a divisor of n-1, along with comments on teaching
sampling distributions and the lies that textbooks tell us.

Sampling distributions are very abstract and very important.  Students
have a hard time getting a handle on what a sampling distribution is.
In most textbooks, they never SEE one.  To let the cat (Thomas?) out
of the bag, I do an in-class exercise with my students.  I put a tiny
population consisting of 3,6,6,9,12,15 on the board.  I ask everyone
to take a random sample from this population, and calculate every
imaginable summary statistic for their sample.  Then I give them a
sheet containing all 20 possible samples, and we find all possible
sample means, and the probability of each.  Now they have a sampling
distribution right before their eyes.  We find its mean and compare it
to the population mean.  We discuss bias in an estimator.  We check
the median, range, variance, etc. for bias.  We find that if we define
"variance" with a divisor of n-1 (for BOTH sample and population), we
get an unbiased measure of variability.  If we use a divisor of n, we
do not.  So maybe we should divide by n-1.

Some books say that using a divisor of n-1 for the sample will give
you an unbiased estimator of what you'd get for the population using a
divisor of N, and talk about a "sample variance (or s.d.)" and a
population "variance (or s.d.)".  That might be true if you sample
with replacement -- I have not checked it and I won't because I never
sample with replacement so who cares.  It is not true for sampling
without replacement from a finite population.  (If you don't have a
finite population, then both formulae under consideration for the
population are undefined anyway.)
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Mon, 9 Nov 1992 13:26:38 CST
Reply-To:     April Milliken <BIOM030@UNLVM.UNL.EDU>
From:         April Milliken <BIOM030@UNLVM.UNL.EDU>
Subject:      Estimability?!?!?!?!

I have a question that needs cleared up based on a student's question (I
teach a SAS lab for an intro course for grads).  I think this is more
along the lines of linear models and I don't even know that I can phrase
my question correctly.

When deciding whether or not a hypothesis is estimable, say Ho: k`b=m ,
is it usually the case that differences between parameter estimates will
be estimable, but often times nothing else is (unbalanced case, maybe with
missing data)?  When working with generalized linear models, is it due to
a non-unique generalized inverse...is this where the answer to estimability
lies?  That is, the solution vector is not unique, but the differences are;
what else is unique (what else is estimable, and is it always true?)?

I guess what I am looking for is guidelines for estimability.  This all came
about when my student made an error in writing his program, but then came up
with "NON-ESTIMABLE" statements....extremely frightening to my novice
programmers!  If anyone can give me some guidelines or even ways to best
explain this issue, I would be grateful!

Thanks,

April Milliken

"I may be in Nebraska, but my heart is still at Kansas State University!"
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
MIME-version: 1.0
Content-type: TEXT/REPORT
Content-transfer-encoding: 7BIT
From-warning: From: header was inserted by: mrgate.uwyo.edu
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 5 Nov 1992 20:35:02 -0700
Reply-To:     Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Comments:     <Parser> E: "From:"/"Sender:" field is missing.
From:         Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Subject:      Service Message

Report on your message to: /R=AM/U=SSAVOR/@mrgate.uwyo.edu
Reason: Failed to transfer; communications failure (0)
Arrival-date: Thu, 5 Nov 1992 20:35:02 MST
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 5 Nov 1992 17:02:45 GMT
Reply-To:     Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
From:         Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
Subject:      rules about keeping research data

CROSS-POSTED STAT-L AND EDSTAT-L - PF it if you've seen in before!

I've had a query from a colleague. She works in a therapy unit.
They collect data on each person they see. Recently they added
to the database a number of items as part of a research project.
There items, together with a machine-assigned subject number,
are exported to the person who is analysing the data.
Questions:
1. The ID number is useful for tracking down missing or dud
information in the datafiles exported to the researcher, and
it isn't linkable to the original record unless you have
access to the original database system. Does this adequately
protect the confidentiality of the clients?

2. Does retaining the information gathered only for research
purposes on the original, administrative database, contravene
any known code of confidentiality or use of research data. The
admin people are anxious to maintain the data there until the
end of the project, in case anything happens to the research
database, or in case a larger research dataset has to be created.
If the data items gathered purely for research are deleted as
soon as a full, cleaned version of the research database is
created, does this cover them against the requirement not to
keep unnecessary data?

3. If an administrative database system like this is set up
to aid a research project, is there any way that you can argue
that ALL the information on it is for research and should be
erased as soon as it has been exported to the researcher?

I would be grateful for any references to published guidelines
addressing these issues, as some strong opinions are being aired
on the subject over here. Much as I would also like the opinions
of my e-friends out there, what I most need is references!

with many thanks

Ronan Conroy                      EMAIL RCONROY@IRLEARN.UCD.IE
Lecturer in biostatistics         Royal College of Surgeons in Ireland
and computing and stuff           Mercer Building, Dublin 2.
                                  (353)-(1)-780200 [fax:780934]
    ---------------------------------
   "Sutor ne ultra crepidam judicaret"
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
MIME-version: 1.0
Content-type: TEXT/REPORT
Content-transfer-encoding: 7BIT
From-warning: From: header was inserted by: mrgate.uwyo.edu
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 5 Nov 1992 16:22:33 -0700
Reply-To:     Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Comments:     <Parser> E: "From:"/"Sender:" field is missing.
From:         Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Subject:      Service Message

Report on your message to: /R=AM/U=SSAVOR/@mrgate.uwyo.edu
Reason: Failed to transfer; communications failure (0)
Arrival-date: Thu, 5 Nov 1992 16:22:33 MST
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: edstat-l@ncsuvm.bitnet
X-VMS-To: IN%"edstat-l@ncsuvm.bitnet"
X-VMS-Cc: JZEITLER
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 12 Nov 1992 17:04:00 EDT
Reply-To:     "James A. Zeitler" <JZEITLER@BENTLEY.BITNET>
From:         JZEITLER@BENTLEY.BITNET
Subject:      re.: Source for Gauss

Special thanks to Andy Taylor, Beth Schreiber, and R.L. Brown for help in
locating GAUSS.  The current address and phone number I have are:

Aptech Systems, Inc.
23804 South East Kent-Kangley Road
Maple Valley, WA  98038
(206) 432-7855


james zeitler
jzeitler@bentley.bitnet
X-Envelope-to: edstat-l@ncsuvm.bitnet
X-VMS-To: IN%"edstat-l@ncsuvm.bitnet"
X-VMS-Cc: JZEITLER
Newsgroups: bit.listserv.edstat-l
Date:         Mon, 9 Nov 1992 09:51:00 EDT
Reply-To:     "James A. Zeitler" <JZEITLER@BENTLEY.BITNET>
From:         JZEITLER@BENTLEY.BITNET
Subject:      Aggregating a 7-Point Scale

We have a survey which employs a 7-point scale: Very Satisfied, Satisfied,
Somewhat Satisfied, Neutral, Somewhat Dissatisfied, Dissatisfied, Very
Dissatisfied.

When discussing the responses to individual questions we want to aggregate the
scale a bit, for example,

Very Satisfied and Satisfied                       - "Satisfied"
Dissatisfied and Very Dissatisfied                 - "Dissatisfied"
Somewhat Satisfied, Neutral, Somewhat Dissatisfied - "Lukewarm"

Can anybody offer guidance on which is the better aggregation scheme to follow,
that given above, or that given below:

Very Satisfied, Satisfied, and Somewhat Satisfied           - "Satisfied"
Dissatisfied, Very Dissatisfied, and Somewhat Dissatisfied" - "Dissatisfied"
Neutral                                                     - "Neutral"

Thank you in advance for your assistance.

james zeitler
jzeitler@bentley.bitnet
Newsgroups: bit.listserv.edstat-l
Date:         Wed, 11 Nov 1992 17:58:18 CST
Reply-To:     Larry Ries <C459006@MIZZOU1.BITNET>
From:         Larry Ries <C459006@MIZZOU1.BITNET>
Subject:      robustness and correlation

     I'm a little confused about the discussion of robustness concerning
correlation coefficient inferences.  As I understand robustness, it refers
to, essentially, how well a test does when the assumptions about the
population are violated.

     For example, to do a one sample t-test we must assume the data comes from
a normal population.  As I understand it, the t-test works pretty well
even if the data isn't from a normal population, so we consider it a
fairly robust test.

     On the other hand, if we are even talking about a correlation, we
basically MUST ASSUME we are talking about a bivariate normal distribution.
(How many distributions have rho as a parameter?  The bivariate normal does,
and I can't think of any others off hand.)

     So my training suggests that you shouldn't look at a corrlation unless
you have a bi-variate normal.  Thus robustness can't be an issue.

     On the other hand, we do nonparametric correlations all the time.
ranks aren't bivariate normal.  Hmmmm.....

     My point is that basically a correlation has no meaning without the
assumption of bivariate normality, so maybe people look at correlation
a whole heck of a lot more than they should.

     Any discussion?


Larry Ries
Dept. of Statistics
University of Missouri
Columbia
From: gskanes@KEAN.UCS.MUN.CA (GRAHAM SKANES, GENERAL STUDIES)
Newsgroups: bit.listserv.edstat-l
Subject: Humour (or amusement)
Date: 4 Nov 92 19:09:17 GMT
Reply-To: gskanes@kean.ucs.mun.ca
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I was reminded of the following by J.E.H. Shaw telling of an experience he
had in Foyle's bookstore in London. Years ago one of the prized possessions
of senior graduate students and junior faculty was Siegel's "Non-parametric
Statistics". I had a copy and it went missing from my office in St. John's
Newfoundland one fall. The following spring we were on holiday in London and
staying with some friends in Enfield. I did virtually nothing but go
around to bookstores both new and used, so I was over-dosed on the activity.
On the day before we were to return to Canada my friend said he was going
to go to Dillon's bookstore (near the University of London) and asked me if
I would join him. I practically regurgitated at the thought but he pressed
me and I went along. So I dutifully trudged up to the used section on
one of the upper floors and browsed. Almost the first thing that struck my
eye was a copy of Siegel's "Non-parametric Statistics". I said to myself,
"I must buy that to replace the one that somebody stole from me." When I
picked it up, sure enough it was my own copy. I showed it to the manager
along with the signaure on my passport and after a moment of embarassment
they gave it back to me. The location of the find told me, by the way, who
had stolen it. The sequel, after being on my shelf for maybe a year or so
after that, the book disappeared again. I haven't stumbled on it yet. Has
anybody seen it? Australia perhaps?
Graham Skanes
Memorial University of Newfoundland
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: EDSTAT-L@NCSUVM.CC.NCSU.EDU
X-VMS-To: @EDSTAT
MIME-version: 1.0
Content-transfer-encoding: 7BIT
Newsgroups: bit.listserv.edstat-l
Date:         Mon, 9 Nov 1992 23:27:06 -0700
Reply-To:     BERGERD@CGSVAX.CLAREMONT.EDU
From:         BERGERD@CGSVAX.CLAREMONT.EDU
Subject:      Confidence intervals for r vs. B

Here is a question that may have a simple answer, but so far
I haven't found it.

When the population correlation is different from zero the
sampling distribution for correlations is skewed.  A confidence
interval for the population correlation is not symmetric
around the sample correlation.

When the regression weight B for a single predictor is nonzero
the usual procedure is to calculate a symmetric confidence
interval around the sample B (e.g., Cohen & Cohen, p. 62).
Is the sampling distribution for B really symmetric?
Note that if we use standardized scores, B = r.

I would welcome an explanation for this apparent contradiction.
Thanks.   Dale Berger
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
MIME-version: 1.0
Content-type: TEXT/REPORT
Content-transfer-encoding: 7BIT
From-warning: From: header was inserted by: mrgate.uwyo.edu
Newsgroups: bit.listserv.edstat-l
Date:         Thu, 5 Nov 1992 18:03:33 -0700
Reply-To:     Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Comments:     <Parser> E: "From:"/"Sender:" field is missing.
From:         Undetermined origin c/o Postmaster <POSTMASTER@NCSUVM.BITNET>
Subject:      Service Message

Report on your message to: /R=AM/U=SSAVOR/@mrgate.uwyo.edu
Reason: Failed to transfer; communications failure (0)
Arrival-date: Thu, 5 Nov 1992 18:03:33 MST
From: P.Jackson@LSE.AC.UK (P Jackson)
Newsgroups: bit.listserv.edstat-l
Subject: Re: robustness and correlation
Date: 12 Nov 92 16:01:06 GMT
Reply-To: P Jackson <P.Jackson@LSE.AC.UK>
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:        UK.AC.LSE; 12 NOV 92 16:01:33 GMT


          Surley it is when considering whether or not a correlation
          coefficient is significantly different from  value (usually
          zero) that  distributional assumptions are important.
          The coefficient itself does carry information regarding the
          linear component of the association between two variables,
          whatever the distribution, does it not ?

          Paul Jackson
          Dept Social Psychology
          LSE
          London
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Original_To:  EDSTATL
Newsgroups: bit.listserv.edstat-l
Date:         Tue, 10 Nov 1992 09:15:00 EST
Reply-To:     SMCNULTY@IUBACS.BITNET
From:         SMCNULTY@IUBACS.BITNET
Subject:      Averaging beta weights

I posted this question to the list a week or so ago, but some hardware
problems on this end lead me to believe it never made it.  At any rate,
the recent questions about the Fisher r to z transformation and confidence
intervals for B's suggest now would be a good time to ask again:

Is any transformation (analogous to the Fisher r to z) necessary before
averaging standardized regression weights?  I have a dataset in which
we've computed a within-subjects regression weight for each case
(regressing self-report's of several traits on ratings by another person
and an earlier set of self-ratings) and we'd like to be able to say
something about the central tendency of the resulting distribution of
beta weights.  Can we simply compute the mean?  If a transformation is
necessary, what form would it take?  Thanks in advance for your advice.

Shawn E. McNulty

bitnet:  smcnulty@iubacs
internet:  smcnulty@ucs.indiana.edu
From: ED563C20@CANISIUS.BITNET
Newsgroups: bit.listserv.edstat-l
Subject: Mastery Learning
Date: 4 Nov 92 19:48:00 GMT
Reply-To: ED563C20@CANISIUS.BITNET
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: edstat-l@ncsuvm
X-VMS-To: IN%"edstat-l@ncsuvm"

Hi!   I am currently in a Psychology class and I am required to do some
research on Mastery Learning for a presentation on Nov. 23.
Does anyone know how effective Mastery Learning is in the earlier grades
like  K-2?  Is this Mastery Learning beinng used in these classrooms and
does it work?  Please send in any info on Mastery Learning!!!!

Thanx

Anastasia Hassos
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Wed, 11 Nov 1992 13:17:55 -0800
Reply-To:     "Kevin J. Keen" <kkeen@TYR.ROYALROADS.CA>
From:         "Kevin J. Keen" <kkeen@TYR.ROYALROADS.CA>
Subject:      Re: fisher's z transformation and robustness
              11 Nov 1992 14:44:21 -0800 .
              <9211112040.AA02848@tyr.RoyalRoads.ca>

1. Fisher's z transformation is a variance stabilizing transformation.
   That is,

             var(r)=(1-rho**2)**2/(n-1) + O(1/n**2)
    while
             var(z)=1/(n-1) + O(1/n**2) .

    Note that the z transform does not depend on the unknown value
    of the correlation coefficient parameter, rho, at least to
    order n**(-2).  So it can therefore be concluded on an
    asymptotic basis that the normal theory for the z transform
    is more robust to departures from the null hypothesis of
    Ho: rho=0 than the normal theory for Pearson's product
    moment correlation, r.

2. Note that for small rho, a better approximation for the variance
   of z is

            var(z)=1/(n-3).

3. Nevertheless, I have found the normal theory for the correlation
   coefficient to function adequately for a sample size as small as
   50.  However, for smaller sample sizes I would use Fisher's z.

4. I hope this helps.


Aye yours,

Kevin J. Keen
Dept. of Maths
Royal Roads Military College
Canadian Military University
Victoria, BC V0S 1B0  CANADA

email: kkeen@tyr.royalroads.ca
fax:   604/363-4913
phone: 604/363-4590
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Mon, 9 Nov 1992 22:53:00 PST
Reply-To:     "Peter A. Lachenbruch" <IARSPAL@UCLAMVS.BITNET>
From:         "Peter A. Lachenbruch" <IARSPAL@UCLAMVS.BITNET>
Subject:      fisher's z and r

A recent comment on the Fisher z transformation reraised a question in my mind.
It has long been known that the distribution of the correlation coefficient is
very NONROBUST (shout from rooftops). An article by E. Pearson and another by
C. Kowalski (roughly in the early 1970s) showed this. My question is "how
robust are analyses which are based on the Fisher z transformation?" I've never
 seen anything on it, but maybe the literature I read doesn't give it much play
 - does anybody on the network have any information?

Tony Lachenbruch
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.edstat-l
Date:         Wed, 11 Nov 1992 14:44:21 -0800
Reply-To:     Tor Neilands <tbn@UTXVM.CC.UTEXAS.EDU>
From:         Tor Neilands <tbn@UTXVM.CC.UTEXAS.EDU>
Subject:      Re: fisher's z and r

Tony Lachenbruch writes:

>A recent comment on the Fisher z transformation reraised a question in my mind.
>It has long been known that the distribution of the correlation coefficient is
>very NONROBUST (shout from rooftops). An article by E. Pearson and another by
>C. Kowalski (roughly in the early 1970s) showed this. My question is "how
>robust are analyses which are based on the Fisher z transformation?" I've never
> seen anything on it, but maybe the literature I read doesn't give it much play
> - does anybody on the network have any information?
>
>Tony Lachenbruch

I am not aware of any literature pertaining to the robustness of Fisher's r
to z transformation and would definitely be interested in hearing/reading
about any information others have on the subject. It seems to me (unless
I'm missing something here) that if the correlation coefficient on which
the transformed z-value is based is nonrobust, wouldn't the resulting
z-value have to be at least as non-robust as the original correlation, if
not even less robust to potential losses of robustness in using the
transformation procedure itself? If this is not so, could someone explain
how the transformed z-value could in fact be more robust than the original
correlation on which the transformed z-value was based?

thanks,

TBN


Tor Neilands
Systems Analyst
Statistical Services Group
Computation Center
University of Texas at Austin
Austin, TX  78712

Phone: (512)-471-3241, Ext. 263

Internet:  TBN@Utxvm.cc.utexas.edu
___________________________________________________

"Life is too short to self-verify"   --- Shawn McNulty

"Two roads diverged in a yellow wood and I, I took
 the road less traveled by, and that has made all the
 difference".                                 --- Robert Frost
___________________________________________________

Disclaimer:  All of the views expressed above are solely
those of the author. Any relationship between my own
views and those of the University of Texas and/or The
Computation Center at the University of Texas are
due merely to regression to the mean.
____________________________________________________
From: dobler@LUNASEA.GAC.EDU (Carolyn Dobler)
Newsgroups: bit.listserv.edstat-l
Subject: Statistics concentration in a Math major
Date: 12 Nov 92 16:53:48 GMT
Reply-To: Carolyn Dobler <dobler@LUNASEA.GAC.EDU>
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I am the statistician within a math dept at a small college (2200 students).
 The dept is
interested in developing a statistics concentration within our math major.

I have read Gaudard and Hahn's "An Undergraduate Concentration in Applied
 Statistics
for Mathematics Majors" in the May 1991 American Statistician, and am interested
 in
hearing from other small colleges who have successfully implemented such a
concentration.  Some question I have are:  What are the required courses?  What
 texts are
you using for the stat courses?  How many statisticians are available to teach
 the
courses?  How many students pursue such a program?


Thanks for your help (in advance).

Carolyn Pillers Dobler
EMAIL:   dobler@lunasea.gac.edu
Department of Math and Comp Sci
Gustavus Adolphus College
St. Peter MN  56082
From: johan.bring@statistik.uu.se (Johan Bring)
Newsgroups: bit.listserv.edstat-l
Subject: Re: Confidence intervals for r vs. B
Date: 11 Nov 92 10:35:31 GMT
Organization: Dept. of Statistics, Uppsala University

In article <01GQYRJOOXCI8WYHWC@CGSVAX.CLAREMONT.EDU> BERGERD@CGSVAX.CLAREMONT.EDU writes:

>Here is a question that may have a simple answer, but so far
>I haven't found it.

>When the population correlation is different from zero the
>sampling distribution for correlations is skewed.  A confidence
>interval for the population correlation is not symmetric
>around the sample correlation.

>When the regression weight B for a single predictor is nonzero
>the usual procedure is to calculate a symmetric confidence
>interval around the sample B (e.g., Cohen & Cohen, p. 62).
>Is the sampling distribution for B really symmetric?
>Note that if we use standardized scores, B = r.

>I would welcome an explanation for this apparent contradiction.
>Thanks.   Dale Berger

The confidence interval for B is wrong.
An asumption in the regression analysis is that the x-values are fix. But 
when you standardize the variables you divide with the variables standard 
deviation which is a random variable. 
The significance test for B is also wrong.

Johan Bring
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU