HDTenEightyP/NetNews huggingface dataset:
From: nichols@spss.com (David Nichols)
Newsgroups: bit.listserv.stat-l
Subject: Re: power for repeated measures/split plot designs
Date: 12 Nov 92 22:34:40 GMT
Organization: SPSS Inc.

In article <STAT-L%92111107261640@VM1.MCGILL.CA> AOWALSH@FORDMURH.BITNET 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.

SPSS on Windows, Mac or OS/2, as well as mainframe, will do post-hoc
power analysis on any design MANOVA will handle (including those listed
above). PC+ will be able to do this down the road, but cannot do it now.
What I mean by post-hoc power analysis is that for a given data set with
a particular noncentrality parameter, etc., MANOVA will give the power
for each test. It will not, however, do all of the kinds of calculations
you might want if you wanted to use it to design studies and wanted to
know what the sample sizes would need to be in order to get a certain
level of power, unless you're willing to do some trial and error with
the post-hoc setup stuff (that is, you would have to dummy up data with
the characteristics assumed for the study and see what the power would
be).

--
 David Nichols        Senior Statistical Support Specialist         SPSS, Inc.
 Phone: (312) 329-3684     Internet:  nichols@spss.com     Fax: (312) 329-3657
*******************************************************************************
 Any correlation between my views and those of SPSS is strictly due to chance.
Newsgroups: bit.listserv.stat-l
From: baer@uwovax.uwo.ca
Subject: Re: Lisrel analysis (software)
Organization: University of Western Ont, London
Date: Wed, 11 Nov 1992 04:04:30 GMT

In article <9211091615.AA19549@osiris.usi.utah.edu>, byron@OSIRIS.USI.UTAH.EDU ((Byron Davis)) writes:
> 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.
>
The SAS procedure CALIS performs SEM/ latent variable analysis as well,
though it does not currently have a facility for simultaneous analysis
in multiple groups.

SYSTAT has an add-on module (I believe it is called EZ-PATH or something
like that).

There is a set of GAUSS routines called LINCS, written by Ron Schoenberg.
 
> I believe that SAS also has a proc to do SEM though I have not personally used
> this program, I have the other two.
 (see above) 
-- 
---------------------------------------------------------------------
Douglas Baer, Department of Sociology,
University of Western Ontario, London, Canada N6A 5C2
Internet: BAER@UWOVAX.UWO.CA
From: rbrown@128.104.239.40 (Roger Brown)
Newsgroups: bit.listserv.stat-l
Subject: Re: Categorical data with repeated measures
Date: 13 Nov 92 15:33:32 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

Walter R. Pirie writes

>> Does anyone know anything that's been done on repeated measures in
>> categorical data? It's a problem that occurs fairly often and seems
>> difficult to deal with.

Steve Simon writes

>The definitive approach, as I have heard from many sources, is Generalized
>Estimating Equations (GEE).  The primary references are:
>
>     Liang and Zeger "Longitudinal data analysis using generalized linear
>     models" Biometrika (1986):73;13-22.
>
>     Zeger and Liang "Longitudinal Data Analysis for Discrete and
Continuous
>     Outcomes" Biometrics (1986):42;131-130.

Another approach to look at is the one by Stram, Wei and Ware (SWW):


        Stram, D. O., Wei, L. J. and Ware, J. H. "Analysis of repeated
ordered
        categorical outcomes with possibly missing observations and time-
        dependent covariates.: Journal of the American Statistical
Assocation,
        (1988):83;631-637.

Also Chuck Davis wrote in nice overview paper:

        Davis, C. S. "Simi-parametric and non-parametric methods for the
        analysis of repeated measurements with applications to clinical
        trials." Statistics in Medicine, (1991):10;1959-1980.

Hope this helps.

RLB



R. L. Brown, Ph.D.
Professor
Statistics & Design Unit
School of Nursing                                           ----
Clinical Science Center and                                /    \
Biostatistics Unit/Dept. of Family Medicine               |      |/|
University of Wisconsin Medical School                    \       /
Madison, WI 53715                                          |  *  |
(608) 263-5281/263-0830                                     -----

=====================================================================
You can, for example, never foretell what any one man or woman will
do, but you can say with precision what an average number will be
up to.
                       (Sherlock Holmes, The Sign of the Four, 1890)
=====================================================================
From: sgju101@SGHMS.LON.AC.UK (C Evans)
Newsgroups: bit.listserv.stat-l
Subject: Logical equivalence matrices = principal coordinate analysis
Date: 5 Nov 92 22:51:27 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:        UK.AC.LON.SGHMS;  5 NOV 92 22:51:55 GMT

Dean Ginther says that he has a student seeking more information on the
analysis of "Logical equivalence matrices".  Hooray, I can contribute
something!

LEMs were the invention of the late Patrick Slater (who used the occupy the
very room in which I'm typing this.  He'd be overjoyed.)  Jane Chetwynd who
is his literary executor did a very good thing in getting a paper published
with he and she as co-authors after his death.  The analysis is a principal
coordinate analysis which is a variant of multidimensional scaling of a
symmetrical matrix and actually not unlike a principal component analysis
but capable of analysing matrices with triangular
 non-commutative[D[D[D[D[C[C[C[C

(oops, not sure what happened there)
with non-communtative triangles within them.  The analysis was indeed
available within GENSTAT which is a package produced by the Royal Statistical
Society (the GENSTAT macro that does it was written by Gower who, I think,
gave the idea to use this to Patrick).  I have the GENSTAT code to do such
analyses somewhere and I think I have access to GENSTAT (though a more recent
version than I used to use) so could analyse a few matrices.  I believe that
other packages do implement principal coordinate analysis but I'm not sure
which.  Certainly neither SAS nor SPSS do to my knowledge.  It might be
possible to implement PCO (as the GENSTAT macro had it) in a matrix language
such as M++, SAS/IML, Splus etc.  I'd be really interested to hear other's
suggestions in this respect, particularly any suggestions about where I'd
find the matrix algebraic presentations that would let me write something such.

I'd be happy to help the student if she contacts me by Email.  Since I'm a
psychiatrist and only an amateur statistician/programmer I'm probably more
help on the general ideas Patrick had about the method and about ways of
using it.

Chris Evans
(Dept. of Mental Health Sci., St. George's Hospital Medical School,
London University, Cranmer Terrace, London SW17 0RE, Britain
4481 672 9944 x55563
100065,1676 on Compuserve/CIS)
From: spector@GANDALF.BERKELEY.EDU ((Phil Spector))
Newsgroups: bit.listserv.stat-l
Subject: Re: Precision Probability for Chi Square statistics
Date: 6 Nov 92 22:31:54 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

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
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Sat, 7 Nov 1992 08:42:26 ITA
From:         FACCIOLI@ITOCSIVM.BITNET
Subject:      Bootstrap

A user  of my  Computer Laboratory  (an anthropologist
who has 1500 skeletons in  his cupboard) is studying a
sample  of  30  skeletons  belonging  to  pre-dynastic
egyptians.  He  is  trying to  draw  inferences  about
sexual  differentiations  having   14  female  and  16
males skeletons.  He poses two questions:
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.

I don't  know if I  stated the problem  clearly enough
but I willingly will  provide more information (asking
the professor  involved) if  you need  it in  order to
help me

                 Gianna Faccioli
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Fri, 6 Nov 1992 02:55:00 CST
From:         RJZ@UTXVM.BITNET
Subject:      measure of agreement

I am looking for some measure of agreement between continuous variables.
A correlation is not what I have in mind.  For example, given two variables,
Y and X, the correlation between Y and 3*X will be the same as the correlation
between Y and X.  However, differences between observations will not be the
same.  I was thinking of using an avarage deviation, or something along
the same lines.  Any good ideas?

Bob Zambarano
university of texas
ccbe001@emx.cc.utexas.edu
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Tue, 10 Nov 1992 14:32:03 CST
From:         "(Roger Brown)" <rbrown@128.104.239.40>
Subject:      Info on LDANP or Charles Davis

Does anyone know if there is a recent version of LDANP (Longitudinal Data
Analysis using Nonparametric Methods for Incomplete Repeated Measurements
from Two Samples) somewhere, maybe statlib? I think the software was
written by Charles Davis (University of Iowa College of Medicine). Any
information would be appreciated. Thanks.

Roger


R. L. Brown, Ph.D.
Professor
Statistics & Design Unit
School of Nursing                                           ----
Clinical Science Center and                                /    \
Biostatistics Unit/Dept. of Family Medicine               |      |/|
University of Wisconsin Medical School                    \       /
Madison, WI 53715                                          |  *  |
(608) 263-5281/263-0830                                     -----

=====================================================================
You can, for example, never foretell what any one man or woman will
do, but you can say with precision what an average number will be
up to.
                       (Sherlock Holmes, The Sign of the Four, 1890)
=====================================================================
Newsgroups: bit.listserv.stat-l
Date:         Fri, 13 Nov 1992 11:53:00 CST
From:         TCAC022@UTXVM.BITNET
Subject:      metaanalysis

---
-------------( Forwarded letter follows )-----------------------
---
Date: Friday, 13 November 1992 11:51am CT
To: STAT-L
From: BILL.KOCH
Subject: metaanalysis

Hi Bob.  Why don't you talk to Prof. Don Veldman here at UT-Austin
in the Ed. Psych. Dept.  He regularly teaches a course on metaanalysis.
From: tarkkone@klaava.Helsinki.FI (Lauri Tarkkonen)
Newsgroups: bit.listserv.stat-l
Subject: Re: Outliers
Keywords: Parameter estimation, outliers
Date: 11 Nov 92 22:27:50 GMT
Organization: University of Helsinki

In <Nov.11.16.46.06.1992.15885@gandalf.rutgers.edu> kluger@gandalf.rutgers.edu (Dr. Avi Kluger) writes:

>I was wondering if any of the readers know of material regarding
>the actual (as opposed to hypothetical) impact of outliers on
>estimation of correlations.  There is one article by Orr et al
>in Personnel Psychology 1991 vol 44 no 3 which reports that various
>outlier deletion methods had only a small impact on average rho
>estimation and various effects on the variance of the estimates.
>Do you know of any other recent emprical treatment of the question?

>Avi Kluger
>Institute of Management and Labor Relations
>Rutgers University
>(908) 932 5823

Have you heard about influence curves. This means you study the change
in correlation if a boservatin is removed from the data:

Say  z(x,y) = r(x,y) - r
r = correlation for the whole data, r(x,y) is the correlation of the
data minus that observation. The function r(x,y) can be written by
means of n, x, y, r, mx, yx, sx, sy. (m for mean and s for standard 
deviation.).

- Lauri Tarkkonen
From: WLTPIRIE@VTVM1.BITNET (Walt Pirie)
Newsgroups: bit.listserv.stat-l
Subject: repeated measures categorical data
Date: 11 Nov 92 04:07:21 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

The tools readily available for categorical data have expanded tremendously in
the past ten years or so. Popular software permits analysis of log-linear and l
ogit models, both heierarchical and non-h---, including ordinal categories, log
istic regression, etc.
Analysis of repeated measures in linear models has advanced a lot during the sa
me general time frame.
Does anyone know anything that's been done on repeated measures in categorical
data? It's a problem that occurs fairly often and seems difficult to deal with.

|==================================|============================|
|  Walter R. Pirie                 |                            |
|  Department of Statistics        |                            |
|  Virginia Tech                   |                            |
|  Blacksburg, VA 24061-0439       |                            |
|                                  |                            |
|  Tel. 703-231-5441               |                            |
|  Bitnet WLTPIRIE@VTFVM1          |                            |
|  Telnet WLTPIRIE@VTVM1.CC.VT.EDU |                            |
|                                  |                            |
|  Fax  703-231-3863               |                            |
|==================================|============================|
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-VMS-To: IN%"STAT-L@VM1.MCGILL.CA"
Newsgroups: bit.listserv.stat-l
Date:         Wed, 4 Nov 1992 09:54:00 PST
From:         Charles Chamberlin <CHAMBERLINC@AXE.HUMBOLDT.EDU>
Subject:      Re: Nonparametric trend tests

Tony Downing asks about "about the relative virtues and drawbacks of the
 following
ways of testing for a trend ..."

In examining groundwater quality records and surface water quality records for
evidence of trend, the Mann-Kendall test has very useful.  see:

Mann (1945) Econometrica 13:245-259

Kendall (1975) Rank Correlation Methods, 4th ed, Charles Griffin, London

Hollander & Wolfe(1973) Nonparametric Statistical Methods


-------------------------------------------------------------------------------
Charles E. Chamberlin
Environmental Resources Engineering
Humboldt State University
Arcata, CA  95521
USA
(707) 826-4346
(707) 826-3616 (fax)
ChamberlinC@axe.humboldt.edu
-------------------------------------------------------------------------------
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:         UK.AC.HULL.SEQ;  6 NOV 92 11:28:03 GMT
X-Mailer:    ELM [version 2.3 PL11]
Original-Sender: R.A.Reese@uk.ac.hull.seq
Newsgroups: bit.listserv.stat-l
Date:         Fri, 6 Nov 1992 11:22:43 WET
From:         "R.A.Reese" <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Logical equivalence matrices (PS on Genstat)

Genstat is marketed (on behalf of RSS) by

Nag Ltd
Wilkinson House
Jordan Hill Road
Oxford OX2 8DR UK

Tel +44 865 511245

or in North America

Nag Inc
1400 Opus Place, Suite 200
Downers Grove, Il 60515-5702
USA

Genstat is very good.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Return-Receipt-To: <sis1@NIOBBS1.EM.CDC.GOV>
Encoding: 51 TEXT
X-Mailer: Microsoft Mail V3.0 (beta-2)
Newsgroups: bit.listserv.stat-l
Date:         Fri, 13 Nov 1992 10:27:00 EST
From:         sis1@NIOBBS1.EM.CDC.GOV
Subject:      I CHECK but I don't TEST for assumptions

Ed Johnson writes

> I would like to know how regularly people on this list test for assumptions.
> For example, when a t test assumes a normal distribution and equal
> variances, how often do you run a Lilliefor test for normalcy and an F test
> for the dif-ference of means? I'm told that it should aways be done, but
> these tests don't seem to be a standard part of statistical packages such
> as SAS, nor do I recall having seen these tests being mentioned in the
> methodology of experimental re- search. Comments, please.

Funny you should ask; I'm preparing a talk for the local SAS Users Group
with the title "Using SAS to check your statistical assumptions".

Although there are certainly times that I've forgotten or I've been too
busy, I try check assumptions religiously.  But I use descriptive procedures
like residual and normal probability plots instead of formal tests.  Here's
why.

A test, especially a test for normality like Lilliefors, has low power for
small sample sizes when you need normality most, and high power for large
sample sizes where you need normality the least (because of the Central Limit
Theorem).  A descriptive procedure will tell you how much your data deviates
from normality and in what direction.  Certain types of non-normality, like
light tails are less serious than others; Lilliefors doesn't give you any
clue about this.

A similar comment holds about testing equality of variances.  I use the rule
of thumb that if the standard deviations or the ranges differ by a factor of
three or more, there is cause for concern.  I've seen this rule in several
different places; if you want a reference, I can dig one out.

Most of the Statistics text books I am familiar with place a strong emphasis
on checking assumptions.  Three examples are:

  Draper and Smith.  Applied Regression Analysis;
  Montgomery.  Design of Experiments; and
  Neter, Wasserman, and Kutner.  Applied Linear Statistical Models.


Steve Simon
sis1@niobbs1.em.cdc.gov
hhscdc!sis1/o=cdc/ou=cinc/ou=niobbs1@mhs.attmail.com

Views expressed here are my own.  Mention of a product or
company name does not constitute an endorsement by NIOSH.
From: WENDELL@UHCCVX.UHCC.HAWAII.EDU (John Wendell)
Newsgroups: bit.listserv.stat-l
Subject: Differences between correlation coeffients
Date: 6 Nov 92 21:01:07 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Mail-System-Version: <VAX-MM(235)+TOPSLIB(133)+PONY(228)@UHCCVX.UHCC.HAWAII.EDU>

I received several requests from people for a summary of what the
replies to my question on how to calculate the significance between
correlation coefficients calculated between a dependent variable and a
set of two or more independent variables (from the same sample), so I
will share the results for the whole list. I found the most useful
source for my specific problem to be Xiao-Li Meng, Robert Rosenthal,
and Donald B. Rubin, "Comparing Correlated Correlation Coefficients,"
_Psychological Bulletin_, 1992, vol. 111, No. 1, 172-175. It provides
a relatively easy to calculate approximation that seems to work well
with most data likely to be encountered in behavioral studies and it
provides numerical examples with intermediate results. They also
provide a more accurate but more computationally intensive method in
an Appendix. A very comprehensive treatment of the whole issue of
testing correlation coefficients is James H. Steiger, "Tests for
Comparing Elements of a Correlation Matrix," _Psychological Bulletin_,
1980, Vol. 87, No. 2, 245-251. Meng et al. and Steiger both make the
point that the "traditional" method for comparing two correlation
coefficients based on Hotelling's t-test has some serious drawbacks in
most practical situations. Mahalo to James@ucrac1.ucr.edu, Dennis
Roberts, Allan Reese, Karl Wuensch, and David Howell for their
comments.
Aloha, - John WENDELL@UHCCVX.UHCC.HAWAII.EDU.internet
           or WENDELL@UHCCVX.bitnet
P.S. oops, missed Jerry Dallal in above. Mahalo Jerry!
-------
From: RJZ@UTXVM.BITNET
Newsgroups: bit.listserv.stat-l
Subject: metaanalysis
Date: 12 Nov 92 16:02:00 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

I am interested in information on metaanalysis software.  Is there anything
readily available for purchase or as free-ware?  Printed documentation or
some reference source would be nice.  Thanks.

Bob Zambarano
ccbe001@emx.cc.utexas.edu
or
ccbe001@utxvm.cc.utexas.edu
Newsgroups: bit.listserv.stat-l
From: kattan@Rosie.UH.EDU (Mike Kattan)
Subject: Re: Lisrel analysis (software)
Reply-To: kattan@Rosie.UH.EDU
Organization: University of Houston
Date: Mon, 9 Nov 1992 13:22:11 GMT

In article <921107141359.152@UADECVMS.SAL.UADEC.MX>, COROZCO@UADECVMS.SAL.UADEC.MX writes:
>I will appreciate information about brand name of software to perform Lisrel
>Analysis. I have statistical references on books, but since I am working on
>educational research, I need to find a software to analize the data.

The new version of SAS (6.07) has a procedure called CALIS which will act like
LISREL.

-------------------------------------------------------------------------
Mike Kattan // Decision and Information Sciences // University of Houston
Houston, TX  77204-6282 // (713) 743-4734 // Fax: -4693 // kattan@uhupvm1
=========================================================================
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Fri, 13 Nov 1992 12:10:20 SST
From:         COFLUNKC@NUSVM.BITNET
Subject:      E-MAIL ADDRESS OF THE NCHS

Hi,

Does anyone know the e-mail address of the NCHS, USA?
I am interested to get an update on their statistical publications.

Please reply to me directly: COFLUNKC@NUSVM.

Thanks

LKC
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Mon, 9 Nov 1992 17:04:14 +1000
From:         Michael Smithson <bhmjs@MARLIN.JCU.EDU.AU>
Subject:      Measure of agreement for continuous variables

Regarding R. Zambarano's request for a measure of agreement between
two continuous variables, I can recommend two sources that might be helpful.
One is an article by F.E. Zegers and J.M.F. Ten Berghe in Psychometrika (1985,
v. 50, pp.17-24) on a family of association coefficients for metric scales.
They consider a special case where the scales are uniquely (or absolutely)
determined, which gives a similarity measure. There was some discussion
in the literature which resulted in a later modification of their coefficients
to take chance effects into account.

Also, D. Dubois and H. Prade's 1980 book on Fuzzy Sets and Systems: Theory
and Applications (Academic Press) gives some measures of similarity
proposed for fuzzy sets that can be generalized to continuous scales in the
nonnegative domain of the real number line. If the two variables
are X and Y, then one such measure just requires summing min(X,Y) for all
observations and dividing by the sum of max(X,Y).

Dr. Michael Smithson
Behavioural Sciences
James Cooko University
Queensland 4811 Australia        bhmjs@marlin.jcu.edu.au
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU