HDTenEightyP/NetNews huggingface dataset:
From: ZUMBO@ACADVM1.UOTTAWA.CA (Bruno D. Zumbo)
Newsgroups: bit.listserv.stat-l
Subject: guttmann scales
Date: 6 Nov 92 22:12:49 GMT
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A colleague of mine writes the following message.  I am currently
pondering it but I thought I would pass it onto the list for
comments.

Bruno D. Zumbo
========================================================================

     We have developed a clinical questionnaire for head injured
patients on their social and adaptive functioning.  The
questionnaire consists of 6 or 7 major scales, each represented by
8 to 12 questions.  Each scale, e.g. "taking intiative" or "being
organized", is Guttman scaled more or less so that there is some
sort of threshold for problem behaviour depending on how impaired
the patient is.  But instead of YES/NO responses, we supply a
Likert scale.  We then adjust the polarity appropriately and simply
add up the scores on the 1 to 5 ratings.  We have found the results
useful and have externally validity in appropriate places.  Now, I
have an honours student doing a project on checking some
psychometric features (something new for me), such as doing a
proper item analysis, seeing whether the Guttman scaling is as we
hoped, factor analysis, etc.  The reason I am writing is that I
have not been able to find any relevant discussant in psychometry
books on such a type of test with Likert ratings on Guttman scales.
Do you know of any? and is there anything I should be aware of?
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Date:         Wed, 4 Nov 1992 15:41:38 -0500
From:         John Wendell <WENDELL@UHCCVX.UHCC.HAWAII.EDU>
Subject:      differences between correlation coeficients

A colleague asks:
  How do you test for statistical significance for different
correlation coefficients?  He referred me to the following table in an
article:

     Table of correlation coefficients, n is in parenthesis.

Variable      Y1           Y2           T-value
   X1        .63 (436)    .32 (436)       8.08
   X2        .43 (438)    .63 (443)       5.33
   X3       -.40 (441)   -.22 (411)      -3.90
   X4       -.29 (427)   -.36 (427)      -1.52

The variables X1-X4 and Y1-Y2 were measured for each subject in the
experiment. The differences in n are due to missing values for some
measurements. Several questions: (1) Can these T-values be derived
from the correlation coefficients and the sample size alone (and if so
how, and if not how would they be derived from the raw data)? (2) If
there was also a Y3 in the above table how would a test of an over-all
difference in correlation coefficients be done (an F-test perhaps)?

Mahalo in advance, - John WENDELL@UHCCVX.UHCC.HAWAII.EDU.internet or
                          WENDELL@UHCCVX.bitnet

-------
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Date:         Fri, 13 Nov 1992 09:42:31 WET
From:         "R.A.Reese" <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Outliers and correlations.

I have in my collection of graphics a scatterplot where a biologist
recorded data for days 1,2,3,4,5,6 and 107 - then computed the
correlation. In this context I'm not sure whether it is the data or
the scientist that is way out.

--
(R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
Head of Applications    Direct voice:   +44 482 465296
Computer Centre         Voice messages: +44 482 465685
Hull University         Fax:            +44 482 466441
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From: HARVEYRJ@VTVM1.BITNET (rj)
Newsgroups: bit.listserv.stat-l
Subject: standard error of r in small samples
Date: 5 Nov 92 22:55:27 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I know this is a pretty simple question, but I have not found a simple answer
yet!  the question is, I need a formula for estimating the standard error of
a correlation in a small sample (n = 20).  I'd like to know what it would
be both for the typical rho=0 case as well as for nonzero true rho.  I've
seen a bunch of approximations that work over N=50, but nothing as small
as 20.  the hard part is that there's very good reason to suspect that the
observations on which the r is calculated are non-independent (i.e., a person
can participate in rating 2-4 of the 20 "observations").  I'd be happy to
just get an answer to the "pure" case w/o worrying about the complications
caused by raters appearing more than once.


r. j. harvey
psychology
virginia tech
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 9 Nov 1992 09:37:00 EST
From:         sis1@NIOBBS1.EM.CDC.GOV
Subject:      Bootstrap

Gianna Faccioli writes:

>a) Is there any package (or software) which draws equiprobabilistic
>ellipses?
>b) the female  sub-sample has a basic  problem: one of
>the female  is very tall and  another very short......
>Bootstrapping on the measures  he obtains results even
>more  disappointing  than  just using  the  sample  in
>itself.

(a) I don't have the manuals in front of me, but I think SYSTAT will draw
equiprobability ellipses.

(b)  This may not be quite what you wanted, but if you have outliers that
are distorting your fit, you might consider a resistant method.  This would
involve recasting your problem in terms of a regression model.  If you take
this approach here are some references that might help.

Siegel, Andrew F. and Benson, Richard H. (1979) "Resistant Fitting as a Basis
for Estimating Allometric Change in Animal Morphology" Technical Report #552,
Department of Statistics, University of Wisconsin, Madison.

Siegel, Andrew F. (1979) "The Repeated Median Regression Algorithm" (Again a
technical report, though I have no details.  I think it was published a stat
journal in 1981 or so?).

Rousseeuw, Peter J. (1984) "Least Median of Squares Regression" JASA
79:871-880. (The bibliography of this paper may have more details on the
Siegel references).

I hope this helps.  If you do use this approach, let me know.

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Date:         Fri, 6 Nov 1992 23:19:41 EST
From:         Walt pirie <WLTPIRIE@VTVM1.BITNET>
Subject:      Chi-square

Following the discussion below: I had decided not to join the
original discussion on this, and in the meantime, several notes
came in giving various ways of computing "exact" probabilities
for the values given in the first note. ( I don't have those
any more) giving probability values of the order of e-7, e-8,
etc.
Phil Spector's note is much more relevant than those so-called
probabilities from the approximating chi-squares. In fact I think
even from his note, one could be mislead into thinking those
probability values could be used to comment on which of the
original statistics were more significant than others. With
differing degrees of freedom, even an ordering of the pro-
babilities would be completely unreliable that far out in the
tails.
Walt.

In response to the following query:

>   I have used the PROBCHI function in SAS
>   (probability = PROBCHI (value, df)) to estimate the probability
>   of each chi square value.  I've run into the problem that the
>   function cannot produce a probability value precise enough to
>   distinguish between the significance of chi square values.

Please realize that values of chi-square probabilities as small as the
ones you are talking about don't really have any quantitative value.
The behavior of a statistic which is claimed to have a chi-square
distribution (which in all likelihood is obtained only asymptotically),
cannot be so delicately measured, especially in the very far tails,
where the values which were reported lie.

This is *not* a flame, just a gentle reminder about the way that
probability values, especially ones corresponding to the tails of
a distribution, should be used.

                                       - Phil Spector
                                         Statistical Computing Facility
                                         Department of Statistics
                                         UC Berkeley
                                         spector@stat.berkeley.edu

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Date:         Thu, 12 Nov 1992 17:33:00 MST
From:         Haiyi Xie <SOCHX@FREUD.SBS.UTAH.EDU>
Subject:      LISREL, EQS, LISCOM and LVPLS

     If you compare LISREL, EQS, LISCOM and LVPLS, what are the strengths and
limitations of each?  I have no experience with LISCOM and LVPLS at all.  I
am strongly interested in one which is distribution free and good for small
sample size (I hope) or robust at least.

Thanks in advance

H.Y. Xie
SOCHX@EDU-UTAH-SBS-FREUD
(801) 581-7922
From: JAMES@UCRAC1.UCR.EDU
Newsgroups: bit.listserv.stat-l
Subject: confidentiality in medical records
Date: 6 Nov 92 22:34:04 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Vmsmail-To: STATL

Some one recently asked for info regarding confidentiality of medical records
on this network. I am sending this to the list because I do not recall who it
was. Apologies to the uninterested and/or disinterested.

These sorts of issues are addressed in the journal Review of Public Data Use
(now J. Econ. & Soc. Measurement). E.g. see  R.H Mugge, issues in protecting
confidentiality in national health statistics, Duncan, disclosure & confide-
ntiality issues in federal statistical environments; Kincannon - same title as
above--  all in Vol 12 #14 (1984).

james.
(even when you win the rat race, you are still a rat.)
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Newsgroups: bit.listserv.stat-l
Date:         Mon, 9 Nov 1992 09:15:03 -0700
From:         "(Byron Davis)" <byron@OSIRIS.USI.UTAH.EDU>
Subject: Re: Lisrel analysis (software)

Carmen,

LISREL analysis, often called Structural Equation Modeling, is done by the
program LISREL, available on most platforms from Scientific Software, Inc. and
for some platforms as a procedure through SPSS.

Additionally, Peter Bentler has written a SEM program named EQS (pronounced X)
which is marketed through BMDP software in California.

I believe that SAS also has a proc to do SEM though I have not personally used
this program, I have the other two.

Byron L. Davis
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Date:         Mon, 9 Nov 1992 09:23:02 GMT
From:         C Evans <sgju101@SGHMS.LON.AC.UK>
Subject:      software for LISREL analyses

Carmen Orozco asked about software to do LISREL analyses.  Well there is
LISREL itself, now on version VII for the PC I think.  I somewhat prefer
EQS which is marketed by BMDP but the friendliest and cheapest package is
AMOS which was $60 or therabouts from James Arbuckle v5113e@bitnet.templevm
Strongly recommended,


Chris Evans
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Newsgroups: bit.listserv.stat-l
Date:         Thu, 5 Nov 1992 14:33:38 WET
From:         "R.A.Reese" <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: differences between correlation coeficients

> measurements. Several questions: (1) Can these T-values be derived
> from the correlation coefficients and the sample size alone (and if so
> how, and if not how would they be derived from the raw data)? (2) If
> there was also a Y3 in the above table how would a test of an over-all
> difference in correlation coefficients be done (an F-test perhaps)?

Aloha!

First question: dunno, but I don't like to make any inferences based
only on the quoted correlations.  People that quote correlations and
p-values in isolation in my experience haven't looked at plots and
don't appreciate the limitations of testing a strictly linear effect.

How would I tackle it? Given the raw data, I'd use GLIM or similar
software to fit a regression to the combined data then add in a parameter
for the group. This gives an overall test for the improvement in fit
by allowing a different slope for each group (or a different intercept)
and each coefficient can be tested by a t-test. In the case of two groups,
these tests are equivalent; if more than two groups you have the standard
problem of interpreting multiple t's in the context of the overall test.
If you normalize the data and suppress the intercept, this is equivalent
to comparing the two correlations.

This is a good example of how the Generalized approach to data modelling
and hypothesis testing is a better m.o. than the cookbook approach of
looking for a specific test to meet each situation.  A good example paper
is "Statistical Modelling of a pharmacological experiment" by M.A.Adena,
The Statistician Vol 31 No 4 Dec 1982 pp279-292.

Sayonara ... to get me home via the pretty route.

--
(R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
Head of Applications    Direct voice:   +44 482 465296
Computer Centre         Voice messages: +44 482 465685
Hull University         Fax:            +44 482 466441
Hull HU6 7RX, U.K.
Newsgroups: bit.listserv.stat-l
From: sjones@alfred.carleton.ca (Stan Jones)
Subject: Re: guttmann scales
Organization: Carleton University
Date: Sat, 7 Nov 1992 14:53:15 GMT

>In <STAT-L%92110617193393@VM1.MCGILL.CA> ZUMBO@ACADVM1.UOTTAWA.CA (Bruno D. Zumbo) writes:

>     We have developed a clinical questionnaire for head injured
>patients on their social and adaptive functioning.  The
>questionnaire consists of 6 or 7 major scales, each represented by
>8 to 12 questions.  Each scale, e.g. "taking intiative" or "being
>organized", is Guttman scaled more or less so that there is some
>sort of threshold for problem behaviour depending on how impaired
>the patient is.  But instead of YES/NO responses, we supply a
>Likert scale.  We then adjust the polarity appropriately and simply
>add up the scores on the 1 to 5 ratings.  We have found the results
>useful and have externally validity in appropriate places.  Now, I
>have an honours student doing a project on checking some
>psychometric features (something new for me), such as doing a
>proper item analysis, seeing whether the Guttman scaling is as we
>hoped, factor analysis, etc.  The reason I am writing is that I
>have not been able to find any relevant discussant in psychometry
>books on such a type of test with Likert ratings on Guttman scales.
>Do you know of any? and is there anything I should be aware of?

Your student would be best off exploring the Item Response Theory
literature.  The techniques there have largely superseded (because
they build on) Guttman's work.  Wright and Masters _Rating Scale
Analysis: Rasch Techniques_ , MESA Press is the only work that
directly address the question (but it does directly address it) your
student is struggling with.  However, it advocates what I (and others,
I think) regard as the least powerful of the IRT models.  
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 13 Nov 1992 12:55:27 CST
From:         April Milliken <BIOM030@UNLVM.BITNET>
Subject:      Back to Factor Analysis.....

(also to EDSTAT-L...sorry for the duplication)

Okay, back to a question that I had a long time ago regarding principal
components analysis...but this time I am more prepared AND I have added
to the problem (is that good or bad)?

 I am working with a physiology
problem.  Fifteen dogs were placed on a protein treatment (3 trmts, 5
dogs/trmt).  After a period of time, 14 biochemical composition
variables were collected on the colon of the animal (example: sodium,
chloride, oxygen, pH, bicarbonate, etc.).  The question to be answered
is regarding the stool characteristics (total fecal material) pertaining
to each treatment...what variables play a role in distiguishing the
treatments (is one better than the other for the dog?).

I initially
used principal factor analysis to find the loading of each variable and
was able to "see" the biochemical breakdown occurring for each
treatment.

However, now a difficulty arises.  My initially analysis was
based on only 1 of 12 possible time periods.  The data were collected
every ten minutes for two hours (plus, there is a control factor...for a
total of 13 measurements per variables per dog).  My goal is to see if
there is an overall picture here...overall, what is the stool
characteristic of each treatment, over all time periods.  Is this where
I start to combine my factor scores with regression techniques?

Possibilities that I see are as follows:

1.  Forget the time factor for the moment, use all data in a factor analysis
    and then, once the factor scores are computed, break apart
    the time intervals from before and run a repeated measures analysis or use
    multiple regression.

2.  Is it possible to find factor loadings for each time
    period, calculate the    factor scores for each time, and some how put
    them all together?

3.  HELP!

This is a bit overwhelming for me, and I need
some help!  If you do have any suggestions, please send them to me.  Any
ideas will help!

Thanks in advance.

April Milliken

"I may be in Nebraska, but my heart belongs to Kansas State University!"
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Newsgroups: bit.listserv.stat-l
Date:         Fri, 13 Nov 1992 08:44:22 PST
From:         "Thomas E. Sabin" <sabint@CCMAIL.ORST.EDU>
Subject:      Re: power for repeated measures/split plot designs

John Walsh writes:
>I am interested in software that will do power analyses for repeated
>measures designs(behavioral science language) or split plot and
>split-split plot designs.
>Programs can be either PC or mainframe.  Our mainframe is a VAX
>configuration v5.1.  Our sas software does NOT include IML.


    PASS by NCSS (801-546-0445) will do this, but it has the worst user
    interface that I have ever seen in a PC program.  I also get frustrated
    because you have to do some hand calculations before you can use the
    program.

    Anything better out there?

  **************************************************************************
  * Tom Sabin                     |          sabin@fsl.orst.edu            *
  * Statistical Consultant       \|/         (503) 737-6577                *
  * Dept. Forest Science        \\|//                                      *
  * Oregon State University    \\\|///                                     *
  * Corvallis, Oregon 97331       |                                        *
  **************************************************************************
From: MPRASS02@UCBCMSA.BITNET
Newsgroups: bit.listserv.stat-l
Subject: Complex Sampling Design Effects for Event History
Date: 12 Nov 92 15:58:00 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I would appreciate any references to work, published or unpublished,
that discusses methods to handle the effects of complex sampling designs
on parameters estimated using event history methods. Most of the work I
do with highly stratified and clustered samples can be handled by
specialized software that adjusts standard errors for the sampling
design, but so far I haven't found any software, and more importantly,
any articles, dealing with this issue.
Please reply directly to me and I will summarize the results for the
list.
Many thanks,
Bob Fitzgerald
Xref: sparky bit.listserv.stat-l:1939 alt.usage.english:8380
From: maj@waikato.ac.nz
Newsgroups: bit.listserv.stat-l,alt.usage.english
Subject: Re: confidentiality in medical records
Date: 11 Nov 92 17:25:18 +1300
Organization: University of Waikato, Hamilton, New Zealand

In article <STAT-L%92111004492316@VM1.MCGILL.CA>, R.A.Reese@SEQ.HULL.AC.UK (R.A.Reese) writes:
>> ... Apologies to the uninterested and/or disinterested.
> 
> I would hope that people in charge of confidential records SHOULD
> be disinterested.
> 
> --
> (R.) Allan Reese        Janet:          r.a.reese@uk.ac.hull
> Head of Applications    Direct voice:   +44 482 465296
> Computer Centre         Voice messages: +44 482 465685
> Hull University         Fax:            +44 482 466441
> Hull HU6 7RX, U.K.

Surely you mean "WOULD be disinterested"?
 
[alt.usage.english added]
-- 
Murray A. Jorgensen [ maj@waikato.ac.nz ]    University of Waikato
Department of Mathematics and Statistics     Hamilton, New Zealand      
__________________________________________________________________
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