HDTenEightyP/NetNews huggingface dataset:
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Date:         Thu, 21 Jan 1993 10:57:11 -0100
From:         MAGNUS STENBECK <mastfoh%tellus.sos.se%sos@MAIL.SWIP.NET>
Subject:      Re: Nominal, Ordinal, Interval,
              and Ratio Typologies are Misleading

> As the topic has frequently been raised on this list, I think
that most
> subscribers should read a recently published article by Paul
Velleman and
> Leland Wilkinson (both names which should be familiar to readers
here) in the
> February American Statistician entitled Nominal, Ordinal,
Interval and Ratio
> Typologies are Misleading.  Just a single quote
>
>         Unfortunately, the use of Stevens's categories in
selecting or
>         recommending statistical analysis methods is
inappropriate and can
>         often be wrong.  They do not describe the attributes of
real data that
>         are essential to good statistical analysis.  Nor do they
provide a
>         classification scheme appropriate for modern data
analysis methods.
>
> Before critics start responding here, I suggest they go and read
Paul and
> Leland's thoughtful commentary.
> -phil

> --
>      J. Philip Miller, Professor, Division of Biostatistics, Box
8067

I have not yet read this article, but I suggest that anyone
interested in this topic also looks at Chapter 4: "On Scales of
Measurement" from Otis Dudley Duncan's book "Notes on Social
Measurement: Historical and Critical" (New York, Russel Sage,
1984). I cite from Duncan's own description of the contents of the
chapter:
   "The theory of scale types proposed in 1946 by S.S. Stevens
focused on nominal, ordinal, interval, and ratio scales of
measurement. Some of his examples of these types - notably those
concerning psychological test scores - are misleading. Steven's
equating of scientific classification with measurement on a
'nominal scale' and his consequent underemphasis on counting (which
actually employs an absolute scale) has mischievous consequences
when taken seriously by population scientists. There is no clear
place in his theory for the probability scale which, like counting,
is central to the population sciences as well as important for some
parts of physics."
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Date:         Thu, 21 Jan 1993 17:16:00 MST
From:         Haiyi Xie <SOCHX@FREUD.SBS.UTAH.EDU>
Subject:      Thanks for your help

Hi,

I'd like to say thanks to Dr. J. Michell, Adrew Karp, M. Klassen,
R. Conroy and others for telling me the "short cut" in skiping some
variables in SAS.  Your help have saved me a lot time and trouble.

Thanks again,

H. Y. Xie
(801) 581-7922

+++++++++++++++++++++++
Could somebody tell me how to subscribe to SAS list by the way?
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Date:         Tue, 26 Jan 1993 15:34:49 PRT
From:         Paulo Nogueira <SDEIOC07@PTEARN.BITNET>
Subject:      Cohen's Kappa

I'm working on Kappa statistic, I'm somewhat amazed by the literature on
this coefficient of agreement for nominal data. I would like to hear from
people that has met Kappa and has faced it's probles (or not), opinions
are most appreciated, I'll be happy to share my opinions with who's
interested.

faithfully,
Paulo Nogueira.

Faculdade de Ciencias da Universidade de Lisboa
Dep. Estatisca e Investigacao operacional
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Date:         Thu, 28 Jan 1993 09:24:06 GMT
From:         A.C.Downing@NEWCASTLE.AC.UK
Subject:      Re: testing for periodicity

How about autocorrelation?  I'm no expert on this, so I'm not sure
whether or not that would just shift the problem back onto appraising
the periodicities in the autocorrelation function, but I imagine
such periodicities would be more obvious. (?)

Tony Downing,
Dept. of Psychology,
University of Newcastle upon Tyne,
England.
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Date:         Fri, 22 Jan 1993 11:24:18 SST
From:         K C Lun <COFLUNKC@NUSVM.BITNET>
Subject:      DOS-BASED SOFTWARE FOR NON-PARAMETRIC METHODS: THE SEQUEL

Hi, STAT-Lers,

Many thanks to the nice people who responded to my request for
information on dos-based, shareware software for non-parametric
methods.  Among the recommendations were MYSTAT and KWIKSTAT.

I found data entry rather awkward for MYSTAT as characters have
to be encased within quotes and my students are certainly not
going to like that.  KWIKSTAT was more user-friendly and I
was beginning to like it until I tried out the non-parametric
procedures.  To my chagrin, KWIKSTAT does non-parametric
analysis for independent groups only.  Hence, there was no way
in which I could use it for Wilcoxon's matched-pairs, signed-ranks
test, unless I am still looking at the older version. I was
trying out version 2.12.

Any comments from KWIKSTAT veterans?





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Date:         Tue, 26 Jan 1993 08:10:16 EST
From:         Antoine Morin <JJASB@ACADVM1.UOTTAWA.CA>
Subject:      Re: Violated t-test assumptions: how to adjust?
              25 Jan 1993 19:55:06 GMT from <ph@PHYSIOLOGY.OXFORD.AC.UK>

Re: alternative to t-test when sample size and variance is unequal.

In my naive approach (Im am not a statistician, have no formal training
in stats, and so on...) I would use a permutation test to build an empirical
distribution of the statistic and compare the observed value of the statistic
for the actual sample to the distribution obtained by permutation.

Now, my question to the gurus reading this: Is this a valid approach?
Other than independence, what other assumption am I making if I make
an inference by this procedure?

Antoine Morin
University of Ottawa
jjasb.acadvm1.UOttawa.ca                  Honni soit qui mal y pense!
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Date:         Fri, 22 Jan 1993 17:01:23 -0500
From:         Don <PLOCH@UTKVX.BITNET>
Subject:      interaction effects

Foulger notes that one might test the interaction directly but ought
to be aware of its form.

Basically, if an interaction is hypothesized, then one ought to test a
multiplicative model since the equation with interaction can always be
re-written as a multiplicative one between linear transposes of the
original variables.

Don Ploch
Sociology
U of Tennessee Knoxville
ploch@utkvx.utk.edu
From: jerry@hnrc.tufts.edu (Jerry Dallal)
Newsgroups: bit.listserv.stat-l
Subject: Re: correct chi-square character
Date: 23 Jan 93 13:33:45 GMT
Organization: USDA HNRC at Tufts University

In article <01GTU2RC5SHS91VSQG@phs.bgsm.wfu.edu>, UEBERSAX@PHS.BGSM.WFU.EDU writes:
> Does anyone know the correct typographical form for the Pearson chi-square
> statistic?  Standard texts like Bishop, Fienberg & Holland show it as
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> X^2, but is the X an upper-case "ex" or an upper-case Greek chi?
  ^^^
> 


When I was finishing my doctorate, my advisor, John Hartigan, decided we would
settle all such questions by using the index of Wilks.  Great advice, and a 
technique I continue to use to this day.  Actually, any of the older Wiley
texts will do, since they were all edited by Bea Shube.  (I don't trust the
newer ones for this purpose.  Right after Bea left, Wiley sent out an add 
describing books that discussed "principle [sic] components.")

Among the other fun facts you'll learn is that "degrees of freedom" is never
hypenated.  In fact, the only often used term that is is "goodness-of-fit".

If you really what to hurt your head:  Is it "chi-square" or "chi-squared"?
(There are two schools of thought, separated by the Atlantic Ocean.)  How many 
t's in Gosset(t)?  (One, but even Fisher misspelled it!) 

Can a comment from Stephen Stigler be far behind?
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Date:         Fri, 22 Jan 1993 09:49:59 CST
From:         April Milliken <BIOM030@UNLVM.BITNET>
Subject:      Multiple Mulitvariate Analyses???

I am still in the "playing" stage with a set of data, but I am curious if
anyone has experience with what combining several multivariate analyses?
I have principle component analysis results for 12 different time periods,
on the same variables, same animals.  I am really intersted in the OVERALL
effect of treatment, but time is also a factor.  How can I combine my results?
Or, is there a better way?  So far, PCA is similar for components 1 and 2,
but from there it starts getting quite variable.  Somewhere in the range of
75% to 90% of total variation is accounted for by PC 3 (over all time periods).
So, 3 PC's may be enough.  AND, the biology makes sense (for the most part)
for each time period.  Now, is there a way to model what is going on over
time, across the PCA's?  Am I asking too much?  I need some ideas!

If you have even a small suggestion, please say so.  I am open to any new
direction.

Thanks,
April

! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !

April Milliken

Department of Biometry
University of Nebraska-Lincoln

BIOM030@UNLVM.UNL.EDU

"I may be in Nebraska, but my heart is still at Kansas State University!"
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Date:         Fri, 22 Jan 1993 13:43:41 EST
From:         "D. Kim" <DONGG@VTVM2.BITNET>
Subject:      QP

Dear STAT-L subscribers;
  Once I asked about the convex Quadraric Programming problem via STAT-L. I've
got several replies from STAT-L and ORCS-L@OSUVM1 (Operation Research and Compu
ter Science). I really appreciate for your kind replies.
  Whenever I received a reply, I saved it on a file (around 10 replies). From
the replies, I obtainded from the public domain FORTRAN code for QP (QLD.F)
and FAQ on Linear Programming (including NP,IP,QP). So If there is any STAT-L
subscribers interested in QP, I can send the file (without program- it is quite
large). I don't think posting the programming is a good idea. But if anyone
wants, then I can mail them directly(Please send me a note, if you want.)
 Thanks again for your great help.

D. Kim
(DONGG@VTVM2.CC.VT.EDU)
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Date:         Tue, 26 Jan 1993 16:02:51 -0600
From:         "J. Philip Miller" <phil@WUBIOS.WUSTL.EDU>
Subject:      t-test assumptions and SAS (fwd)

>     SAS tests the F ratio of the two samples and then computes t both
>     ways so you can choose the unequal t if F is significant.
>
That two step procedure is not an optimal strategy, see Miller & Wette, Proc
of ASA Stat Computing Section, 1981 160-4 or Miller, Wette & Parks, SUGI
Proceedings, 1981, 156-61.

Having said that, those papers only talk about the case where both are normal.
In most applications, the reason the variances are different is that they are
not normally distributed and therefor some alternative procedure, such as a
data transformation or a rank order test might be considered.

-phil


>     Will@unh.edu
>


--
     J. Philip Miller, Professor, Division of Biostatistics, Box 8067
         Washington University Medical School, St. Louis MO 63110
          phil@wubios.WUstl.edu - (314) 362-3617 [362-2694(FAX)]
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Date:         Fri, 22 Jan 1993 10:47:23 +0100
From:         neurocomp@LUDENS.ELTE.HU
Subject:      ask for biomed books

Hallo. Could somebody recommend
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Date:         Thu, 28 Jan 1993 10:29:20 -0500
Reply-To:     "Paul A. Thompson" <pat@po.CWRU.Edu>
From:         "Paul A. Thompson" <pat@PO.CWRU.EDU>
Subject:      Re: Interactions

>Mr. Keppel is quite wrong, or, more to the point, he is engaging in classic
>ANOVA interaction thinking.  An interaction variable is the product of two

Well, it seems to me that when you use an ANOVA, you engage in classic
ANOVA interaction thinking, otherwise you would use another technique.

The whole point to the ANOVA is the conditionality of the statement.  If
you have an interaction, you can't make an unconditional statement.  You
can always say something, and that is what simple main effects are for,
to allow you to make main-effect-like statements when the interaction
precludes making any unconditional statement.  Simple main effects are
statistically reasonable statements, usually constructed with an
appropriate power level.

If an interaction is present, there may be interesting overall main
effect differences and there may not be.  If the interaction is not
significant, and the main effect for A is, we can always be sure that at each
level of B, there is some interesting difference between the levels of
A.  This is the type of thinking usually done in ANOVA.

--
Paul Thompson, Ph.D.  |  Department of Psychiatry  |  (216) 844-7463
Case Western Reserve University  | Cleveland, OH  44106
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Date:         Thu, 21 Jan 1993 08:36:44 EDT
From:         Alan Neustadtl <ALAN@BSS1.UMD.EDU>
Subject:      TOBIT analysis in SAS

**********************************************
This has been cross-posted on SAS-L and STAT-L
**********************************************

        I have a student working on an M.A. thesis, and his data
warrant using a TOBIT analysis.  Does anyone out there know how to
do this in SAS?  Our other option is to download the data from the
mainframe to a PC and run it in SHAZAM or LIMDEP.  Since he is in
a hurry, he would prefer not to take these extra steps (the data
are in a SAS system file).

        Thanks in advance...

Best,
Alan

Alan Neustadtl
Department of Sociology
University of Maryland
College Park, MD  20742

1-301-405-6411
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Date:         Mon, 25 Jan 1993 21:34:55 EST
From:         DWINSLOW@UCS.INDIANA.EDU
Subject:      X2

Wasn't it Sokal & Rohlf who suggested using an "ex" instead of a "chi",
under the reasoning that there are other statistics (besides sum((o-e)2/e))
which under some conditions approximate the chi-square(d) distribution
more closely?   Donald Winslow (DWINSLOW@UCS.INDIANA.EDU)
From: IARSPAL@UCLAMVS.BITNET (Peter A. Lachenbruch)
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Subject: outlier test
Date: 26 Jan 93 17:11:00 GMT
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The procedure looks exactly like Dixon's test which is described in the
elementary text by Dixon and Massey. It should also be in the book by Barnett
et al entitled "Outliers in Statistical Data"
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Date:         Mon, 25 Jan 1993 18:22:45 EST
Reply-To:     "Davis A. Foulger (914) 945-2077 (t. 862-2077)"
              <Foulger@WATSON.BITNET>
From:         FOLGER@WATSON.BITNET
Subject:      interaction effects

I certainly agree with Don Ploch that in testing an interaction one should
test a multiplicative one, but still note that, as long as one is doing
regression and has the freedom to shape the interaction, one should be aware
of the form the hypothesized interaction is expected to take.

There are a variety of multiplicative interaction shapes that can be tested,
including AND, OR, XOR, NAND, NOR, NXOR, nonzero positive, and various
combinations thereof.  All are equivalent in terms of the total variance
accounted for when both interactions and main effects are tested.  Each
shape represents a very different interpretation of the data, however, such
that selection of the right shape (e.g.  the hypothesized interaction) will
make the results of the analysis easier to interpret; selection of the wrong
shape may make misinterpretation likely.

Consider, for instance, the classic interaction hypothesis that one effect
happens only in the presence of condition 1 of variable X and condition 1 of
variable Y (e.g.  not in the presence of condition 0 of either variable --
an AND interaction if you will).  Assuming these results obtain, the
standard XOR interaction used in an analysis of variance (with the main
effects in the two x two condition coded as -1 and 1; condition 0 and
condition 1, respectively) will show two significant main effects and a
significant interaction; this despite the lack of any real main effect.
We can, however, easily code the interaction to the hypothesized shape by
coding the conditions as 0 and 1 (condition 0 and condition 1, respectively;
the AND interaction).  Again assuming that the results obtain as
hypothesized, only the interaction will be significant; neither of the main
effects will be.

These are only 2 of a variety of possible hypothesized shapes (although they
are the principle shapes for the simple bivariate case).  It should be
obvious, however, from my description, that, at least for the hypothesis
given, the second shape above is more likely to be correctly interpreted
than the first.  This problem of interpretability is one of the reasons I am
very much against the routine use of ANOVA in testing interaction effects.
I am sure that many a main effect has been incorrectly reported as
significant because an AND interaction was tested by ANOVA's standard XOR
interaction shape.

Davis

Snailmail..........................Davis A. Foulger
Internet: FOLGER@WATSON.IBM.COM    IBM T.J. Watson Research Center
Prodigy:  XFRR20A                  P O Box 218, Yorktown Ht, NY 10598
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Date:         Tue, 26 Jan 1993 16:35:00 EST
From:         "MARS::LEE_RB" <LEE_RB%MARS.decnet@APG-EMH7.ARMY.MIL>
Subject:      Chi-square test of a Binomial Distribution

Here is my dilema: I have n=4 animals with t=100 trials each. Each animal
selects either a familiar or novel object on each trial.  Each animal's
probability of selecting the familiar object (or the novel object) follows a
binomial distribution.  The number of choices in which the animal selects
the familiar object is X.  Therefore, each animal's probability of selecting
the familiar object is p=X/t with variance tp(1-p).
      Now I am reading a paper that states that the mean of the animals
probabilities is P with expected variance tP(1-P) and the observed variance is
calculated using each animal's p according to the equation for variance for
a normal distribution.  It is further stated that the ratio of the observed to
the expected multiplied by (n-1) will yield a Chi-square statistic.
      Now I understand that if I get a large Chi-square, I will reject the
hypothesis that the animals as a group do not fit a binomial distribution.
But what does it tell me when I get and extremely low Chi-square and can I
place a probability value on getting such a value?  This paper states that an
extremely low Chi-square would also be indicative of not fitting a binomial
distribution.  Is this really true?
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Date:         Tue, 26 Jan 1993 17:12:53 -0800
From:         JAMES@UCRAC1.UCR.EDU
Subject:      RE: testing for periodicity

you might want to look at books on time series e.g. Newton 1988 TIMESLAB: A
time series  analysis laboratory. It is explicit (for the mathematically
sophisticated -- thru culculus & trig anyway) and it comes with software.

Else look at the SAS/ETS manual...
james ssemakula
uc riverside
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Date:         Mon, 25 Jan 1993 09:36:07 -0500
Reply-To:     "Paul A. Thompson" <pat@po.CWRU.Edu>
From:         "Paul A. Thompson" <pat@PO.CWRU.EDU>
Subject:      Re: correct chi-square character

>
>
There is another issue here as well.  The chi-2 computation

X2 = sum ((obs - exp)**2/exp)

can be computed with any data.  On the other hand, certain conditions must
be fulfilled before this is a chi-2 r.v.  These include independence, obs
is (0, 1) normal r.v., etc.  This careful distinction is clearly exhibited
on BFH pg.  124, in which the computed value is shown as X2, and the value
is stated to be "asymptotically distributed as chi-2 (greek letter)"
Thus, the Roman X indicates computation result, while the Greek chi
indicates theoretical result.  We can compute X2 with any data, but
must show conditions for chi to apply.  In a recent article in Multivariate
Behavioral Research where I used bootstrap with X2 values, I used X2 because
chi did not hold.

--
Paul Thompson, Ph.D.  |  Department of Psychiatry  |  (216) 844-7463
Case Western Reserve University  | Cleveland, OH  44106
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Date:         Thu, 28 Jan 1993 13:13:26 GMT
From:         Ronan M Conroy <RCONROY@IRLEARN.UCD.IE>
Subject:      Re: Interactions

Davis Foulger is adamant, in his example about seatbelts and airbags,
that we would want to examine the effects of each factor
separately even if their interaction was significant, because
some people have only seatbelts and others only airbags.
But these effects are NOT the main effects of an anova.
What you find out if the interaction is significant is that
you cannot talk about the effect of seatbelts or of airbags
^per se^. The effect of each depends on the presence or absence
of the other. So if you have no safety device, installing a
seatbelt will have a different effect to installing it if you
already have an airbag. His example highlights the problem that
expressing the hypothesis tested by the main effects where
there's a significant interaction rarely comes up with a
meaningful statement.

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"
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