HDTenEightyP/NetNews huggingface dataset:
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Date:         Tue, 13 Oct 1992 07:02:00 EST
From:         mmz1@CCDDCD1.EM.CDC.GOV
Subject:      "proving" no difference

Some work has been done on this with respect to testing bioequivalence for
the availability in the blood stream of oral formulations of generic
pharmaceuticals compared to that of a proprietary pharmaceutical.


                                    Matthew Zack
From: MGRANAAS@CHARLIE.USD.EDU
Newsgroups: bit.listserv.stat-l
Subject: data snooping
Date: 14 Oct 92 18:59:45 GMT
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ON 14 Oct 1992 Dale Hample writes that he believes that data snooping is
a good idea, and then provides rational.  I would like to let Dale know
that he is not alone in the world.  For a variety of reasons I allow/
encourage data snooping for the graduate students that I advise/train.
One of my reasons is similar to Dale's--most of our students design their
studies in such a way that they collect much more information than they
really need for their research question.  My efforts to change this
behavior by encouraging faculty to force their students to design better
studies has largely fallen on deaf ears.  So I have taken the position that
data snooping is o.k., but *only if it is clearly marked as such*.  I prefer
this to retro-fitting hypothesis to data.  I also believe that it allows
the reader to evaluate the worth of the results more accurately than if
interesting results are presented as if the researcher had planned to
do the analysis all along.]

And, since I cannot get my colleagues and students to clean up their act
and gather only the data they need for the research question at hand, it
seems reasonable to explore the rest of the stuff they get so that the
subject's time isn't wasted by providing data that is never used.

I would also like to see some discussion of this topic to get a feel for
how others view this issue.

Mike Granaas
mgranaas@charlie.usd.edu
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Date:         Thu, 15 Oct 1992 17:21:00 EST
From:         POSTOFFICE@ROUTER.EM.CDC.GOV
Subject:      RETURNED BY ADMINISTRATOR

I have the impression that quite a bit of the recent discussion on
data snooping has been off-base and mis-represents what the phrase
``data snooping'' actually means.

Exploratory data analysis --- looking for patterns and making
discoveries --- is not data snooping.  Data snooping consists in
formulating and formally testing hypotheses based upon one's
exploration of the data rather than upon a priori conjectures.  As
such it renders probability statements associated with the results of
such hypothesis tests essentially meaningless.

Someone in the discussion has suggested reporting the p-values of
such post facto hypothesis tests as a form of descriptive
statistics.  There is clearly no intrinsic harm in so doing, as long
as these p-values are clearly distinguished as being ``descriptive''
rather than having a genuinely probabilistic meaning.  However, the
general devotion to p-values and 0.05 significance levels being what
it is, it seems to me that such a practice runs a serious risk of
being misinterpreted.  (The warning that these p-values are not
***really*** p-values will be treated as ``fine print'' and ignored.)


                         cheers,

                              Rolf

Rolf Turner
Dep't. of Maths and Stats
U.N.B., Fredericton, N.B. Canada E3B 5A3
rolf@math.unb.ca

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Newsgroups: bit.listserv.stat-l
From: mfdjh@uxa.ecn.bgu.edu (Dale Hample)
Subject: Re: Power analysis
Organization: Educational Computing Network
Date: Fri, 16 Oct 1992 15:39:48 GMT

I'd also be interested in knowing how to compute power for a canonical
correlation analysis.
-- 
Dale Hample                            Dept. Communication A&S
mfdjh@ecnuxa.bitnet                    Western Illinois University
mfdjh@uxa.ecn.bgu.edu                  309-298-1492
        The beatings will continue until morale improves.
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Date:         Thu, 15 Oct 1992 16:59:00 EST
From:         POSTOFFICE@ROUTER.EM.CDC.GOV
Subject:      RETURNED BY ADMINISTRATOR

   I wrote something pretty stupid a few minutes ago in my last
message.

   In order to get a random sample from a multivariate normal population
of known variances and covariances, one needs as many normal random vectors
of the size of the number of variables, and not as many normal random
vectors of the size of the sample as there are variables.

   It makes no sense to sample separate variables.

   My apologies to those who helped me and may have thought their
explanations were not clear enough. It's just that the student had left
his mind somewhere else for a while.


Benoit Laplante
Departement de psychologie
Universite de Montreal
Laplante@ERE.UMontreal.CA
From: maj@waikato.ac.nz
Newsgroups: bit.listserv.stat-l
Subject: F tests when scores are responses
Date: 9 Oct 92 13:24:28 +1300
Organization: University of Waikato, Hamilton, New Zealand

Dear Stat-L,

I have a data set taking the form of a large, unbalanced ANOVA
situation. 

    It's large : around 5000 observations


    There are five factors having 19,5,5,18,4 levels


I want to analyse this using regression programs by using
groups of indicator variables for the factors, in fact this
has already been done. There may be interactions but I have
restricted attention to two-factor interactions not involving
the many-levelled factors.

The problem is this: the response variable is not continuous,
but is a subjective score on a 3-point scale.


My request is: does anyone have references to the behaviour of
General Linear tests with this type of data?

[Cochran,W.G. (1950) 'The comparison of percentages in
matched samples' Biometrika 37,256-266

did some simulations with binary responses which seemed to
suggest that the usual F critical values were not too misleading,
but there must be some more recent references.]
-- 
Murray A. Jorgensen [ maj@waikato.ac.nz ]    University of Waikato
Department of Mathematics and Statistics     Hamilton, New Zealand      
__________________________________________________________________
'Tis the song of the Jubjub! the proof is complete,
if only I've stated it thrice.'
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Acknowledge-to:    XIC@NIHCU.BITNET
X-Acknowledge-to:  XIC@NIHCU.BITNET
Newsgroups: bit.listserv.stat-l
Date:         Sat, 10 Oct 1992 16:52:32 EDT
From:         XIC@NIHCU.BITNET
Subject:      Imputation Information

This note is in response to Philip Gallagher's request for
references on imputation.  As noted by Matthew Zack, the book
by Don Rubin, "Multiple Imputation for Nonresponse in
Surveys," (John Wiley, 1987) is very advanced.  His book with
Rod Little, "Statistical Analysis with Missing Data," (John
Wiley, 1987) is not as advanced but not elementary.

My favorite source is the 3 volume "Incomplete Data in Sample
Surveys," edited by Madow, Olkin, and Rubin (Academic Press,
1983).  For the hot deck procedure, the paper by Barry Ford
"An Overview of Hot-Deck Procedures" in Volume 2 is
recommended.  The three volumes contain many well-written
papers at a variety of levels.

The monograph by Graham Kalton on missing data, published by
the University of Michigan, is very good.  I do not have the
exact title because I loaned my copy to someone.

I hope this helps.  From
Michael P. Cohen
National Center for Education Statistics
Washington, DC 20208-5654
Bitnet: XIC@NIHCU
Internet:  XIC@CU.NIH.GOV
From: ACMARSHA@ECUVM1.BITNET (Marsha Shepherd)
Newsgroups: bit.listserv.stat-l
Subject: Data snooping
Date: 15 Oct 92 13:13:48 GMT
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Three cheers for data snooping!!  I'm a research consultant.  I cannot
begin to tell you how many times my habitual data snooping has turned
a research project on its ears.  Why do I data snoop?  Because I'm
usually investigating the experimental design to be sure what the
client tells me holds true.  I'm also trying to familiarize myself
with a subject matter that I've not had full training in.  Unfortunately,
I usually discover serious design flaws and other data problems in
the experiment.  Most problems we can work around and many of my
"discoveries" about the data are of valuable interest to the researcher,
but s/he would not have thought about looking for them.

I get very dismayed when I see people blindly analyze data according to
some canned technique without even bothering to see what they actually
discovered.  The excitement generated from my "data snooping" is a joy
to observe and I believe is a necessary part of research, which I
define as "exploration".
From: potthoff@JAGUAR.SPH.UMN.EDU (Sandra Potthoff)
Newsgroups: bit.listserv.stat-l
Subject: nonindependence of observations
Date: 14 Oct 92 19:36:44 GMT
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I would appreciate advice on the following problem:

The research question being addressed is:  Do school based clinics
decrease birth rates?

The data we have are: The total number of births and the total number
of female students by year for 16 years from 5 high schools.  In all
schools, there were a number years in which there wasn't a school
clinic followed by the remainder of years in which there was a school
clinic, although the different schools got their clinics in different
years.  Thus, for 5 schools we have annual birth rates pre and post
clinic.

The problem is that from year n to (n+1) approximately 2/3 of the
student sample will be the same, since the seniors graduated and the
new sophomore class came in.    All we have are the aggregate
numbers, not student specific data.

Thus, data are not independent.  This creates a problem for doing
t-tests.  I talked to a statistician who said that the upper bound on
the standard error would be square root of (3p(1-p)/N), (the 3
because the school has 3 grades), although he said that because we
have the whole population in this case the standard error is 0 and so
one can only do descriptive statistics.  If one takes the view  that
these schools represent a sample of all schools in the U.S. that have
school based clinics, then the standard error is not 0.  Any comments
on how standard error was calculated, and alternatives to it, would
be appreciated.

Second, we would like to conduct logistic regression, except we have
this problem of nonindependence because of  2/3 repeated measures
from year to year at the school level.  Again, all data are at the
school level, and we would be trying to model 16 years x 5 schools =
90 birth rates using school variables.  As another twist here, one of
the schools had 9-12 graders, resulting in 75% repeat from year n to
(n+1).  Any suggestions on what we can do?  I've been following the
discussion on logistic regression for repeated measures, but I think
this is something different, since all we know is that we have 2/3
repeats, but because it's data aggregated at the school level by
year, we don't know who the repeats are.
From: TCD@CORNELLC.BITNET (Tim Dorcey)
Newsgroups: bit.listserv.stat-l
Subject: Re: data snooping and PCA
Date: 14 Oct 92 20:49:13 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
              14 Oct 1992 15:37:26 GMT from <mfdjh@UXA.ECN.BGU.EDU>

On Wed, 14 Oct 1992 15:37:26 GMT Dale Hample said:
>autobiographical flaws.  Just because I'm too imperceptive to realize
>that some other relationship might appear within my data, why should the
>scientific community be deprived of that information?  I understand that

Because the scientific community already has more "information" than
they know what to do with.

>one needs different data for discovery and verification, but the

My feelings exactly.  It's my impression that the Journals in many fields
contain mainly 2 kinds of results:  convincingly documented trivialities
("I believe it, but who cares?"), and interesting results without adequate
substantiation ("That's neat, but I could have found neat stuff in a
30 x 30 correlation matrix based on 10 observations too...").  The first
kind represents the "verification" without "discovery", while the latter
represents the opposite.  Neither does much to advance science.

>argument against data snooping seems to me to censor lots of interesging
>discovery.  If I find something intriguing in a huge correlation matrix,
>something that I hadn't anticipated, I don't think that I ought to
>swallow it, or do the write-up so that it looks like I knew it would be
>there all the time.

Of course not.  Do the right experiment that will convince everyone else
that your discovery is genuine.  Imagine what a well-trained investigator
might come up with if they spent the first 20 years of their career
snooping for something interesting...scientific proof, be damned...and
the next 5 confirming its existence through rigorous data collection and
analysis.  Certainly not tenure, but maybe a paper worth reading...

Tim Dorcey                        BITNET:   TCD@CORNELLC
Statistical Software Consultant   Internet: TCD@CORNELLC.CIT.CORNELL.EDU
Cornell Information Technologies  Phone:    (607) 255-5715
Cornell University
Ithaca, NY  14853
From: NARAYANA@UCS.INDIANA.EDU (A. NARAYANAN DECISION & INFO. SYSTEMS--855-2665)
Newsgroups: bit.listserv.stat-l
Subject: Artificial data
Date: 14 Oct 92 17:04:11 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

>    I need to generate several random samples of size 200 to 400 from a
> normal multivariate theoretical population of which the variance-covariance
> matrix is known. The only software that would do this that I know of is
> GENRAW, a utility program distributed with Lisrel. Unfortunately, it is
 limited
> to 10 variables while the problems I am working on involve 11 and 14
 variables.
>
>    So does anyone on the list know of a software, program or big macro that
> would generate artificial random samples for up to at least 14 variables?

There has been some discussion on this topic in this list sometime ago. All
you need to do is Cholesky factorization. Almost all the statistical
packages have routines to do this. One I like is the IMSL routine RNMVN. I
don't believe there any restrictions on the number of variables.

A. Narayanan
School of Business
Indiana University
From: ILFC594@VAX2.CONCORDIA.CA (GEOFF SELIG - CONCORDIA U. COMPUTING SERVICES)
Newsgroups: bit.listserv.stat-l
Subject: Esoterica....
Date: 15 Oct 92 18:08:00 GMT
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Albyn Jones writes
 > Surely no harm can come from misunderstanding esoterica like this!
 > Who cares if students understand the logic of inference, so long as they
 > can churn out CI's and p-values?

Hopefully, this is said with tongue in cheek.

Geoff Selig        Phone (514)-848-7666 |        _,  /|     The Cat's Meow
Academic Services    FAX (514)-848-7622 |        \ o.O'
Department of Computing Services        |        =(___)=
Concordia University   (H928)           |            U
Montreal, Quebec   H3G 1M8              |  ilfc594@vax2.concordia.ca
                                        |  ilfc594@conu2.BITNET
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Via:         UK.AC.HULL.SEQ; 13 OCT 92 10:55:11 BST
X-Mailer:    ELM [version 2.3 PL11]
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Newsgroups: bit.listserv.stat-l
Date:         Tue, 13 Oct 1992 06:03:47 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Need help with PCA
              "Meredith_Warshaw@com.FRANKSTON" at Oct 12, 92 10:25 pm

> concerns about "data snooping".  On the other hand, with only 15 animals,
> some of which have
> missing data,

W.E.Deming makes a good point in "Sampling for Business Studies" (1960)
[query the title but it's something like that] that very small random
samples are inherently unreliable, so that for small studies it will
often be preferable to choose a "judgemental sample" in which case
probabilities are not applicable for inference.
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Date:         Tue, 6 Oct 1992 20:48:28 EDT
From:         "Karl L. Wuensch" <PSWUENSC@ECUVM1.BITNET>
Subject:      Re: point biserial correlation

     Is it just me, or does the question "How do I do a point biserial
correlation on such and such a stat pack" seem to come up just about every
week on the various stat lists?  Has any other question been so frequently
asked on these lists?  Would someone please ask the SAS and SPSS folks to put
"point biserial correlation" in the index of their manuals along with the page
number for their Pearson r procedure?

    Karl L. Wuensch, Dept. of Psychology, East Carolina Univ.
    Greenville, NC  27858-4353, phone 919-757-6800
    Bitnet Address: PSWUENSC@ECUVM1
    Internet Address:  PSWUENSC@ECUVM.CIS.ECU.EDU
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Date:         Tue, 13 Oct 1992 17:16:58 +1000
From:         "(Michael Smithson)" <bhmjs@MARLIN.JCU.EDU.AU>
Subject:      Proving No Difference

Andy Taylor asks:
>How best can we use statistical inference to "prove" (give strong evidence for)
>the _absence_ of a difference or effect?
>
>As Meredith says, clients/authors often want to use a nonsignificant test (or
>better, a P-value >> 0.05) as evidence two groups don't differ (or the
 regressio
>slope is 0, or whatever). What can/should they do instead?

Perhaps I'm missing something, but isn't this what beta is supposed to help
us do? If beta is the probability of incorrectly failing to reject the null
hypothesis (e.g., of 0 slope), then we ought to be able to argue that we're
justified in saying the null hypothesis is supportable when beta is low.
The catch is that beta requires us to specify how small a deviation from the
null hypothesis value we'd like our study to detect. So, my understanding
is that in order to provide evidence of no difference, we have to make a
statement that includes (1) the power (or beta) being used and (2) the
effect size associated with it.

For instance, "This study could detect a 2.5% voter swing against the
Republican Party with power of .90. We failed to reject the null hypothesis
of no swing (alpha = .05). With a beta of .10 we are unable to detect a
swing of 2.5% or more."-- Well, in better prose than that, but that's the
main idea.

Mike Smithson
Behavioural Sciences
James Cook University
Queensland Australia
From: WDAVIS@UNCVM1.BITNET (Walter Davis)
Newsgroups: bit.listserv.stat-l
Subject: Re: Independence of observations in logistic regression
Date: 9 Oct 92 19:38:47 GMT
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On Fri, 9 Oct 1992 15:34:00 EDT Dennis Roberts said:
>Usually, in multiple regression, the situation is that you have numerous
>variates on the SAME objects; how else will you get the data?  Thus, there
>is no problem in looking at the multiple regressiion of several predictors
>on some criterion.  Data are naturally correlated in this situation; ie,
>they are not independent.  True, there would be a higher multiple R if the
>predictors correlated 0 but, all that means is that combining correlated
>variables does not gain you as much as combining uncorrelated variables.  In
>either case, R is appropriate.

hi all,

I believe Dennis has misunderstood the original posting.  Dennis here is
talking about whether or not the variables are correlated - i.e. how
multicollinear are the variables.  The original question deals with
non-independence among the observations (among students in the same
schools).  The problem is that the disturbance term in the equation
is likely to be correlated across subjects (cov(ui,uj)^=0), thereby
violating one of the assumptions of regression (including logistic).

The problem is that whatever factors are not accounted for by
the regression are lumped into the disturbance term.  If some
of these factors are school-related then the disturbances of
students in the same school are likely to be correlated.

The consequences in linear regression are that the standard error
estimates are biased, leading to incorrect tests of significance.
I'm not sure if anyone has looked at the consequences of auto-
correlation for logistic models (hopefully I just have a lousy
memory), but the consequences will be at least this bad.  Whether
it may cause additional estimation problems, especially in small
samples, is the question.

For the data similar to those the original poster has, some form of
multi-level model may be most appropriate although I'm not sure
that software is available for multi-level logistic regression.

One first step may be to include a set of dummy variables for the
different schools.  If entered only as dummy variables, then these
would control for mean differences in the probabilities.  If inter-
action terms are also included then these would control for slope
shifts among schools.  Other possibilties are to enter some
school-level characteristics in the equations and to possibly
interact them with the individual-level characteristics as
well.

hope this helps,

Walter Davis <WDAVIS@UNCVM1>
Department of Sociology
Institute for Research in Social Science
UNC - Chapel Hill
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