HDTenEightyP/NetNews huggingface dataset:
From: Meredith_Warshaw@FRANKSTON.COM
Newsgroups: bit.listserv.stat-l
Subject: Re: "proving" no difference
Date: 13 Oct 92 15:29:00 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

Andy Taylor brings up two important questions:
"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?"


If there is a nonsignificant p-value, it should be reported as just that.
Then,
if we believe that there would have been adequate power to find a meaningful
difference
and if any differences found were trivial, we can state just that. If the
differences
found are large enough that they would have been of interest had they passed
the
"p-value test" and we believe that the problem is lack of power, then we can
state
that as well.  This is where you then go and look for funding to pursue the
result more properly!
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Mon, 12 Oct 1992 22:25:00 -0400
From:         Meredith_Warshaw@FRANKSTON.COM
Subject:      Re: Need help with PCA

I agree completely with Phil Gallagher in all parts of his thoughtful reply.
I uderstand Andy Taylor's
concerns about "data snooping".  On the other hand, with only 15 animals,
some of which have
missing data, there is a high probability that there will be a lack of power
to detect real differences
even if they exist.  In that case, looking around in an exploratory manner is
probably the most
useful tack.  If anything appears promising, it can then be pursued in a
follow-up study with
a reasonable number of animals and better error checking on the equipment.

As a statistical reviewer for a psychiatry journal, one of the things that
bugs me most is when
authors claim that not finding a significantly significant difference between
groups (in one case,
with only 12 subjects!) means that there is NO difference between groups.  A
priori hypotheses
and avoiding data snooping are important, but irrelevent if there isn't
enough power to find
any but the hugest differences statistically significant.

Meredith Warshaw
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Wed, 14 Oct 1992 21:56:00 EDT
Comments:     208 Cedar Bldg.
Comments:     University Park, PA USA
Comments:     AC 814-863-2401
From:         Dennis Roberts <DMR@PSUVM.BITNET>
Subject:      Re: discrim. analysis with dichotomies

Do correlations and use the ones with the highest r values; or step wise regres
sion. What is the big deal about dichotomies?
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Mon, 12 Oct 1992 00:37:00 EDT
From:         Cande V Ananth <ANANTH@UNC.BITNET>
Subject:      References : Polychotomous Logistic Regression

Dear STAT-l'ers,

##  This is in response to Mike Granaas (mgranaas@charlie.usd.edu)
    request for references on polychotomous logistic regression.

    1.  Agresti A : Analysis of ordinal categorical data. John Wiley
        New York, 1984.

    2.  Anderson JA : Regression and ordered categorical variables
        (with discussion). J Royal Statist Soc (B) 46 : 1-30, 1984.

    3.  Armstrong BG, Sloan M : Ordinal regression models for epidem-
        iologic data. Am J Epidemiol 129 : 191-204, 1989.

    4.  Ashby D, Pocock SJ, Shaper GA : Ordered polytomous regression:
        An example relating serum biochemistry to alcohol consumption.
        Applied Statist 35 : 289-301, 1986.

    5.  Engel J : Polytomous logistic regression. Statistica
        Neerlandica 42 : 233-252, 1988.

    6.  Greenwood C, Farewell V : A comparison of regression models
        for the analysis of transplanted-kidney function. Canadian J
        Statist 16 :325-335, 1988.

    7.  Holtbrugge W, Schumacher M : A comparison of regression models
        for tha analysis of ordered categorical data. Applied Statist
        40 : 249-259, 1991.

    8.  McCullagh P : Regression models for ordinal data (with
        discussion). J Royal Statist Soc (B) 42 : 109-142, 1980.

    9.  McCullagh P, Nelder JH : Generalized linear models. Chapman
        and Hall, London. Second Eds, 1989.

   10.  Peterson B, Harell FE : Partial proportional odds models
        for ordinal response variables. Applied Statist 39 : 205-217,
        1990.

   11.  Peterson BL : Re. Ordinal regression models for epidemiologic
        data. Amer J Epidemiol 131 : 745-746, 1990.

   12.  Walker SH, Duncan DB : Estimation of the probability of an
        event as a function of several independent variables.
        Biometrika 54 : 167-179, 1967.

##  These are some of the important references that I have dealt
    with the development of logistic models for ordinal data.
    The book on Logistic Regression by

    Hosmer DW, Lemeshow L : Applied Logistic Regression. John Wiley,
    New York, 1989.

    has a section on polytomous logistic regression (under the chapter
    "Special Topics in Logistic Regression"), which the reader might
    find useful.

... Cande V Ananth               (ananth@UNC.bitnet)
    Department of Biostatistics, SPH
    University of North CArolina at Chapel Hill
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Subject: (no subject given)
From: FACCIOLI@ITOCSIVM.BITNET
Newsgroups: bit.listserv.stat-l
Subject: (no subject given)
Date: 15 Oct 92 19:05:18 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

Does anybody know a software named ACUMEN?
It is suppose to handle matrices?
Does it work all right? How to get it?
              Gianna
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:           UK.AC.KING;  8 OCT 92 17:10:04 BST
X-Envelope-to: stat-l@mcgill1.bitnet
X-VMS-To:      IN%"stat-l@mcgill1.bitnet"
X-VMS-Cc:      BS_S467
MIME-version:  1.0
Content-transfer-encoding: 7BIT
Newsgroups: bit.listserv.stat-l
Date:         Wed, 7 Oct 1992 13:22:18 -0300
From:         Stephen Gourlay <BS_S467@NEPTUNE.KINGSTON.AC.UK>
Subject:      Help! I Can't read the index!

Can someone please help me.  I am a new subscriber - I have got a copy of
the index, and (as the document suggests) a copy of the Listlpun.memo file.
However, that file is "a reference guide for applications programmers", for
for ordinary mortals.

The system I use here is VAX VMS 5.4.

Thanks

Stephen Gourlay
From: flim@weber.ucsd.edu (Francis Lim)
Newsgroups: bit.listserv.stat-l
Subject: Modeling "non-independent" data
Date: 14 Oct 1992 20:13:00 GMT
Organization: University of California, San Diego
NNTP-Posting-Host: weber.ucsd.edu

As long as you are willing to assume that each student is
independent from all other students, you can model your
data as having been generated by a survival model or
perhaps as a panel dataset.

Here are two good books on survival analysis:

Cox (1984), "The analysis of survival data."
Lancaster, Tony (1990), "The econometric analysis of transition data."

For panel data, see:
Hsiao (1986), "The analysis of panel data."

-- 
==============================================================================
     FRANCIS W. LIM / email: flim@ucsd[.edu] / dept phone: (619) 534-3383
     Dept of Economics, Univ of Cal at San Diego, La Jolla, CA 92093-0508
==============================================================================
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 08:41:11 CDT
From:         "(Peter Wollan)" <wollan@MAYO.EDU>
Subject:      statistics for rotations in 3-d

Could someone point me to statistical methods for measurements of rotations
of a rigid body in three dimensions?  This is not "directional" data, or at
least not obviously;  the books I'm familiar with, eg Watson, Mardia, and
Fisher-Lewis-Embleton, talk about distributions of vectors on the sphere, two
degrees of freedom, and what I have is rotations of a sphere, three degrees of
freedom.  From some comments in various places, I **think** what I want is a
representation as points on the 3-sphere, in 4-space.

  Thanks for any suggestions.

	Peter Wollan
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Encoding: 25 TEXT
X-Mailer: Microsoft Mail V3.0 (beta-2)
Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 14:03:00 EST
From:         jhh0@PHPDLS1.EM.CDC.GOV
Subject:      Disciminant analysis with dichotomies?

>I have a student who has two clinical groups and one non-referred group.
>In her study she is interested in determining how a number (about 14)
>variables predict group membership in order to infer something about
>the effectiveness of using these variables in clinical identification.
>Two of the variables are continuous the rest are dichotomies. I would
>have liked to perform a discriminant analysis but I believe the mix
>of continous and dichotomous variables prevents this. Does any one
>have an alternative suggestions for a similar kind of analysis?

Have you given any thought to using a polytomous logistic regression?  I
believe that both the categorical and continuous variables can be handled
with little difficulty.  BMDP 1990 release has a module which will do the
polytomous logistic regression, and the documentation is quite complete.
Check out the references at the end of the chapter.

--------------------------------------------------------------------------
Jim Handsfield                          |       Voice:  (404)639-1703
Centers for Disease Control             |       FAX:    (404)639-1777
Mail Stop G23                           |
1600 Clifton Road, NE                   |
Atlanta, GA 30333                       |     jhh0@phpdls1.em.cdc.gov
Newsgroups: bit.listserv.stat-l,sci.math.stat
From: sasrdt@shewhart.unx.sas.com (Randall D. Tobias)
Subject: Balanced Fractional Factorials
Originator: sasrdt@shewhart.unx.sas.com
Date: Thu, 8 Oct 1992 20:09:49 GMT
Organization: (none)


I'm interested in finding out more about the class of "balanced"
fractional factorial designs.  I've got a slew of papers from the
_Journal_of_Statistical_Planning_and_Inference_ by the likes of
Srivastava, Chopra, Kuwada, etc., but the subject seems horribly
disjoint.  From what I can tell, it's an important design idea which
has been somewhat neglected, I suppose because the construction theory
is so piecemeal and combinatorially heavy.  But now that many
experimenters simply choose their designs from catalogs on computers,
perhaps it's time to take another look.  Is there a single reference
that handles, say, the construction and information characteristics of
all designs of odd resolution?  I'd be interested in corresponding
with anyone who is knowledgeable/interested in the topic.
-- 

Randy Tobias          SAS Institute Inc.     sasrdt@unx.sas.com
(919) 677-8000 x7933  SAS Campus Dr.         72450.2545@compuserve.com
(919) 677-8123 (Fax)  Cary, NC   27512-8000  norat@aol.com

 ... just my $(-exp(2*sqrt(-1)*arcos(0))/(((2**(2 + 1)) - 1)**2 + 1)).

Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Via:      UK.AC.HULL.SEQ;  7 OCT 92 13:03:05 BST
X-Mailer: ELM [version 2.3 PL11]
Original-Sender: R.A.Reese@uk.ac.hull.seq
Newsgroups: bit.listserv.stat-l
Date:         Wed, 7 Oct 1992 08:04:53 -0400
From:         Allan Reese <R.A.Reese@SEQ.HULL.AC.UK>
Subject:      Re: Point biserial correlation

This is a FAQ. I suggest anyone bringing it up anew should be
referred to the extensive discussion in the archives.

--
(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.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Wed, 7 Oct 1992 10:20:30 PDT
Comments:     Warning -- original Sender: tag was NETNEWS@UVVM.UVIC.CA
From:         Melvin Klassen <KLASSEN@UVVM.BITNET>
Subject:      Updating SAS and SPSS manuals (was: point biserial correlation

"Karl L. Wuensch, Dept. of Psychology, East Carolina Univ." <PSWUENSC@ECUVM1>
writes:
> Is it just me, or does the question "How do I do a point biserial
> correlation in the XYZ package" 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?

Excellent idea!  Note that the **last** page of **every** SAS manual
is the "Your Turn" page.  Fill it out, with your suggestion(s) for
improving the SAS manuals, and send it to the indicated address!
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: STAT-L@MCGILL1.bitnet
X-VMS-To: IN%"STAT-L@MCGILL1.bitnet"
Newsgroups: bit.listserv.stat-l
Date:         Wed, 7 Oct 1992 16:34:00 EST
From:         NYHAN@FAUVAX.BITNET
Subject:      ITEM-REMANIDER COEFFICIENTS

t(I would like to find software to compute item remainder coefficents associated
    with determining internal consistency of items in survey development.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Encoding: 33 TEXT
X-Mailer: Microsoft Mail V3.0 (beta-2)
Newsgroups: bit.listserv.stat-l
Date:         Thu, 15 Oct 1992 16:58:00 EST
From:         POSTOFFICE@ROUTER.EM.CDC.GOV
Subject:      RETURNED BY ADMINISTRATOR

> This bioequivalence testing thing is interesting. But is it
> any different to requiring that the confidence interval for
> the effect be confined to a pre-specified interval about
> zero or whatever?
>
> If so why not express it this way and avoid all the mystique?
> --
> Murray A. Jorgensen [ maj@waikato.ac.nz ]    University of Waikato
> Department of Mathematics and Statistics     Hamilton, New Zealand

Yes, I think it is different, but related.  In particular, if a
confidence interval for theta is

        theta-hat  +/-  delta

then the power of detecting an effect of size delta is approximately
50%.  That's because if we shift theta by delta, the mode of the
distribution of theta-hat is about at the boundary of the critical
region.

Most people aren't satisfied with a type-II error probability of .50,
so it's best to go for higher power, say 80% or even 95%.  Making the
power 95% for an effect size of delta makes the width of the confidence
interval much narrower than +/- delta.

------------------------------------------------  --------------,
Russ Lenth                 rlenth@stat.uiowa.edu  \              )
Department of Statistics                           |             \_ "The
Athens
  and Actuarial Science    Tel: (319)335-0814      \    Iowa City  \ of the
The University of Iowa     FAX: (319)335-3017       \          *  _/ Midwest"
Iowa City, IA 52242  USA   CW:  *= * ====== *=*      |           /
------------------------------------------------      ----------v
From: dick_darlington@QMRELAY.MAIL.CORNELL.EDU (dick darlington)
Newsgroups: bit.listserv.stat-l
Subject: Crime rates
Date: 7 Oct 92 03:04:55 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU


                                                                     2:30 AM
  OFFICE MEMO                                                 Time:
                      Subject:
                      Crime rates#000#                                  09-30-92
                                                              Date:

To test for differences in crime rates among two or more cities, use the rates
to calculate the actual number of crimes in each city.  Then taking the total
number of crimes across all cities as given, use the binomial or chi-square to
test the null hypothesis that the expected values of these numbers are
proportional to the cities' populations.  E.g. if you have 30 murders in city A
and 50 in city B, and B is three times A's size, then out of 80 murders total
the expected numbers in cities A and B are 20 and 60.  Test with a binomial or
chi-square.  The chi-square is easily adapted to testing the composite
hypothesis that crime rates are equal in several cities.
Dick Darlington, Psychology, Cornell
#000#
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Sat, 10 Oct 1992 13:32:11 CET
From:         Joop Hox <A716HOX@HASARA11.BITNET>
Subject:      multilevel logistic regression

Longford's VARCL program has a build-in procedure for logistic regression,
and Goldstein's ML3 can fit the same model using user-provided macro's.
A problem is that both use a quasi-likelihood approximation, and not
much is known about its accuracy.

Joop Hox
University of Amsterdam
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 10:11:00 EDT
Comments:     208 Cedar Bldg.
Comments:     University Park, PA USA
Comments:     AC 814-863-2401
From:         Dennis Roberts <DMR@PSUVM.BITNET>
Subject:      Item remainder r's

The item remainder r is nothing more than the correlation between a part score
with some total score, of which the part is part of the total. In item analysis
, the correlation of an item with the total test score is an example. Since the
 item score is INCLUDED IN THE TOTAL, some of the variability of the total is D
UE TO the item score. The more the part influences the total, then the more bia
s the part, total correlation is. For example, if you look at the relationhsip
between course test scores, and the total points in a course, the total points
variable is primarily a function of the parts, since the total is not independe
nt of the parts. Most good item analysis programs produce these item, total cor
relations, with or without the item being a part of the total score. If not, th
ere is a simple formula to do this calculation; one could make a macro file to
do it. It is as follows in VERBAL TERMS.

   Numerator       [r(part,tot)*SDtot - SDpart]
                  -------------------------------
   Denominator    SQRT [VARpart + VARtot  - 2 * SDpart*SDtot*r(part,tot)]

This will produce a correlation between the part and the total with the part sc
ore excluded from the total: ie, the item remainder r.
From: FDANE@UGA.BITNET (Frank Dane)
Newsgroups: bit.listserv.stat-l
Subject: Re: data snooping
Date: 14 Oct 92 20:10:37 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
              14 Oct 1992 13:59:45 -0500 from <MGRANAAS@CHARLIE.USD.EDU>

I, too, support data snooping, provided it is acknowledged as such and
not confused with hypothesis testing on the same order as a designed
study with _a priori_ hypotheses. IMHO, a study in which one has the
resources to collect "a litle extra information" to be used for
subsequent hypothesis generation (for designing new studies) or to be
used to better undestand the effects for which one has designed the
study is an efficient way to practice science.  If we only collected
exactly the data needed to test a prioiri hypotheses and only conducted
the analyses that test those hypotheses, we rule out most, if not all,
chance of serendipity.

Dr. Francis C. Dane, Associate Professor and Chair
Department of Psychology, Mercer University
Macon, GA  31207-0001  USA
Bitnet: FDANE@UGA   Internet: FDANE@UGA.CC.UGA.EDU
Tel: (912) 752-2972
Fax: (912) 752-2108
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Encoding: 7 TEXT
X-Mailer: Microsoft Mail V3.0 (beta-2)
Newsgroups: bit.listserv.stat-l
Date:         Sat, 10 Oct 1992 10:41:00 EST
From:         mmz1@CCDDCD1.EM.CDC.GOV
Subject:      Independence of observations in logisti

The generalized estimating equations approach by Liang and Zeger from Johns
Hopkins University (cf., issues of Biometrics and Biometrika in 1986-1990) is
one approach.  They have a SAS macro program to calculate the required test
statitistics if you send them a floppy disk.

                                Matthew Zack
From: THOMPSON_R@CUBLDR.COLORADO.EDU (RICHARD Z.THOMPSON)
Newsgroups: bit.listserv.stat-l
Subject: Survival Analysis
Date: 15 Oct 92 19:49:00 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
X-Envelope-to: stat-l@mcgill1.bitnet
X-VMS-To: IN%"stat-l@mcgill1.bitnet"
Content-transfer-encoding: 7BIT

I have received a number of responses to my request for statistics packages
that handle survival (or event-history) analysis.  Enclosed is a list of those
programs and some reading references (to supplement Francis Lim's recent e-mail
regarding another subject).  Thanks again to all who have responded.

SURVIVAL SOFTWARE (in addition to the routines in SPSS and SAS) Please note
that all prices are either rounded or approximate.   These are all PC programs.

I have checked on these:

BMDP has SOLO (basic stats - $250) and a SURVIVAL add-on ($150).
BMDP is the glossy marketed version of NCSS which is more recently updated.
NCSS is $125 and is currently selling with 3 add-ons for another $125 -
students then get a %50 discount.  Available from Dr. Jerry Hintze (801)
546-3097. I did purchase this package.  Arrived in two days.  Very short
learning curve.  Good documentation.  Works quite nicely!

STATA has some built in survival capabilities and "excellent graphics".
1-800-STATAPC   Academic version is about $165.  They also make Intercooled for
about $100 more. (same as STATA but UNIX performance requiring at least a 80386
and math co-processor).

Survival ($10) available from David P. Smith, University of Texas, School of
Public Health, P. O. Box 20186, Houston, Texas 77225, telephone 713-792-4364.
A batch processor, no graphics.  They are currently updating the package.

I have not verified the following:

SYSTAT also has an add-on for survival analyses

DMA Corp. in Palo Alto (?) markets Nancy Tuma's RATE.  Available in mainframe,
UNIX and PC versions (?).  This program seems to require compiling and is not
for the computer novice (like myself).


LITERARY REFERENCES:
Cox (1984), "The analysis of survival data."

Lancaster, Tony (1990), "The econometric analysis of transition data."

Allison, P.D. 1984. Event history analysis: Regression for
longitudinal event data. Newbury Park, CA: Sage Publications.

Gross and Clark 1975. Survival distributions: Reliability
applications in biomedical sciences. New York: John Wiley.

KalbFleisch, J.D. & Prentice, R.L. 1980. The statistical analysis
of failure time data. New York: John Wiley.

Lee, E.T. 1984. Satistical methods for survival data analysis.
Belmont, CA: Wadsworth.

Nelson, W.B. 1982. Applied life data analysis. New York: John
Wiley.

Yamaguchi, K. 1991. Event history analysis. Newbury Park, CA:
Sage Publications.

Have fun
Rich Thompson
CU/Boulder
From: STR002@UKCC.UKY.EDU (Bob Moore)
Newsgroups: bit.listserv.stat-l
Subject: Lisrel 7 parameter estimates that may not be identified
Date: 13 Oct 92 17:02:19 GMT
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU

As a new LISREL user I repeatedly run into the message that some parameter bein
g estimated "may not be identified" with the result that standard error
estimates, t-values, modification indices, and standardized residuals
cannot be computed.  This happens most frequently when I'm using
diagonally weighted least squares (DWLS) estimates that take into accountthe AC
 and AV matrices (asymptomotic covariance and variance matrices of the
covariance matrix) which are generated by PRELIS along with the
PM matrix (ploychoric, polyserial, and product moment correlations).
I've read the SPSS Lisrel 7 manual by Joreskog and Sorbom, and also
Hayduck where issues of identification are discussed.  However, I've not
discerned how it is suggested that these problems of parameter estimates
that may not be identified be tackled.  Any suggestions will be much
appreciated.
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU
Newsgroups: bit.listserv.stat-l
Date:         Fri, 9 Oct 1992 11:44:53 CET
From:         Joop Hox <A716HOX@HASARA11.BITNET>
Subject:      item remainder correlations

SPSS RELIABILITY computes item-remainder correlations under the heading
'corrected item-total correlations'. If you don't have SPSS, I can send
you a PC turbo pascal (3.0) program that does the same.

Joop Hox
University of Amsterdam
Comments: Gated by NETNEWS@AUVM.AMERICAN.EDU