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Date:         Thu, 10 Sep 1992 08:58:00 MST
From:         DAVE HUTH <DHUTH@UTAHCCA.BITNET>
Subject:      subscribe

please subscribe dhuth@cc.utah.edu

thank you

dave huth
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Date:         Wed, 9 Sep 1992 14:39:55 EDT
From:         Frank Dane <FDANE@UGA.BITNET>
Subject:      Re: likert scales and ordinal and interval measurement

Art Ellen asksif it makes sense to sum two apparently different items,
e.g. I am sometimes blue and I am depressed daily, to arrive at a
total score.  IF the correlation is high enough, then it does make sense.
Given thesetwo sample items, one would not expect the correlation between
them to be very high. But the point of scaling is to determine what
consistencies lie in the subjects' responses to the items.  If a
sufficient number of subjects see these two items as related, then
one's own expectations about the items must bechalked up to differing
phenomenological outlooks.  Perhaps the subjects think being "blue"
and being "depressed" are very different things, and that things are
very bad if they are feeling both.

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
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Date:         Wed, 9 Sep 1992 08:18:29 EDT
From:         Wally Dixon <dixon@NIKE.HEIDELBERG.EDU>
Subject:      Likert

Recently someone on this list noted that more educated people tended to use
more extreme scores on Likert scales.  I wasn't aware of this.  What would
be the reference for this finding? Thanks.

Wallace Dixon
Department of Psychology
Heidelberg College
Tiffin, OH  44883
dixon@nike.heidelberg.edu
From: jerry@ginger.hnrc.tufts.edu (Jerry Dallal)
Newsgroups: bit.listserv.stat-l
Subject: Re: Metaanalysis Flame
Date: 6 Sep 92 01:19:44 GMT
Organization: USDA HNRC at Tufts University

[If you are receiving this twice, please accept my regrets.  A Postmaster
somewhere along the line bounced it back to me.  Hence, I am reposting.]

In article <1992Sep4.111159.346@nutmeg.hnrc.tufts.edu>, I wrote:
> 
> I'm speechless!  

My speech has returned.  

I believe Mr. Jensen's reaction to my posting was the result of a 
misunderstanding.  I am posting this followup rather than replying to Mr.
Jensen directly in case others others have misunderstood my intent, as
well.

When Mr. Jensen asked for references to meta analysis so that he might 
combine data from small, expensive experiments, I replied that, in my 
professional opinion, it would be more of a disservice to to provide the 
references and suggested that he consult with a statistician having 
experience with such data. (In a nicer tone than this summary suggests!)

I infer from his followup that he felt that my response questioned his 
competence and was an attempt to withhold information because data 
should be analyzed solely by the all-knowing, high priests of the religion 
of statistics (to borrow a metaphor from David Salsburg).

In fact, my *intent* was to dissuade him from meta analysis (he would be 
better served by the mutli-center clinical trials literature) and, 
assuming there was a problem at hand that needed to be solved quickly, to 
point out he would be better off working with someone who had experience 
with such problems.  The phrase "having experience with such data" in my 
original posting "hire a statistician having experience with such data" was 
not included as idle filler.  As Mr. Jensen notes, no one, regardless of his 
of her field of expertise, knows everything.  Hence, my recommendation that 
he hire someone with a track record with such problems.  (Apparently the word
"hire" introduced *another* ambiguity.  When I say "hire" I mean "work 
with", not "hand over the problem to.")

Every time I respond to a newsgroup posting, I am concerned that a reader 
might misinterpret a sentence that hangs there in black and white without 
any vocal inflection or body language to modify it and without the 
opportunity to resolve ambiguities in a fast efficient manner. 

(I recently circulated a two-sided questionnaire.  At the bottom of the 
first side was the tag <*** *** OVER *** ***>.  One respondent returned his 
with the second side blank.  When I asked why, he replied that he thought 
the tag meant the series of questions was over, i.e., done, finished, 
completed.  Now both sides of my two-sided questionnaires say <See other 
side>.)  

I hope this message clears up any issues rasied by my original posting.
From: jerry@ginger.hnrc.tufts.edu (Jerry Dallal)
Newsgroups: bit.listserv.stat-l
Subject: Re: Metaanalysis Flame
Date: 5 Sep 92 13:28:29 GMT
Organization: USDA HNRC at Tufts University

In article <1992Sep4.111159.346@nutmeg.hnrc.tufts.edu>, I wrote:
> 
> I'm speechless!  

My speech has returned.  

I believe Mr. Jensen's reaction to my posting was the result of a 
misunderstanding.  I am posting this followup rather than replying to Mr.
Jensen directly in case others others have misunderstood my intent, as
well.

When Mr. Jensen asked for references to meta analysis so that he might 
combine data from small, expensive experiments, I replied that, in my 
professional opinion, it would be more of a disservice to to provide the 
references and suggested that he consult with a statistician having 
experience with such data. (In a nicer tone than this summary suggests!)

I infer from his followup that he felt that my response questioned his 
competence and was an attempt to withhold information because data 
should be analyzed solely by the all-knowing, high priests of the religion 
of statistics (to borrow a metaphor from David Salsburg).

In fact, my *intent* was to dissuade him from meta analysis (he would be 
better served by the mutli-center clinical trials literature) and, 
assuming there was a problem at hand that needed to be solved quickly, to 
point out he would be better off working with someone who had experience 
with such problems.  The phrase "having experience with such data" in my 
original posting "hire a statistician having experience with such data" was 
not included as idle filler.  As Mr. Jensen notes, no one, regardless of his 
of her field of expertise, knows everything.  Hence, my recommendation that 
he hire someone with a track record with such problems.  (Apparently the word
"hire" introduced *another* ambiguity.  When I say "hire" I mean "work 
with", not "hand over the problem to.")

Every time I respond to a newsgroup posting, I am concerned that a reader 
might misinterpret a sentence that hangs there in black and white without 
any vocal inflection or body language to modify it and without the 
opportunity to resolve ambiguities in a fast efficient manner. 

(I recently circulated a two-sided questionnaire.  At the bottom of the 
first side was the tag <*** *** OVER *** ***>.  One respondent returned his 
with the second side blank.  When I asked why, he replied that he thought 
the tag meant the series of questions was over, i.e., done, finished, 
completed.  Now both sides of my two-sided questionnaires say <See other 
side>.)  

I hope this message clears up any issues rasied by my original posting.
From: nichols@spss.com (David Nichols)
Newsgroups: bit.listserv.stat-l
Subject: Re: communalities > 1
Date: 14 Sep 92 23:54:30 GMT
Organization: SPSS Inc.

Since the original question was solved as to what a Heywood case is
before I had a chance to respond to it, I will only add the other
information concerning what SPSS does in these situations.

The PC and IMAGE methods are not iterative and do not encounter this
problem.

ALPHA will continue iterating into oblivion under releases prior to
5.0 (currently out only on Windows). For 5.0 it has been made to stop,
as PAF does.

The ULS, GLS and ML methods all constrain the communalities and
continue iterating.

--
 David Nichols           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.
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Date:         Mon, 14 Sep 1992 09:01:00 EDT
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From:         Dennis Roberts <DMR@PSUVM.BITNET>
Subject:      meta analysis

There have been various comments, both yea and nay, regarding meta analysis.
As I see it, meta analysis is NO different than statistical methods we use
to SUMMARIZE and GLEAN from data a trend, or general tendency.  In that
context, it is but one of many tools.  Are there problems with this tool?
Of course, just like there are problems with t tests, and manovas when
assumptions are not adhered to.  But, we live in an imperfect world, and
there is NO one miracle study to end debate on a given issue.  Thus, there
will always be multiple investigations on a similar topic.  Actually,
replications SHOULD be encouraged.  Thus, there will always be a need to
summarize results across similar studies to see if there is some overall
trend.  Meta analysis can ASSIST in this venture.  It is not the end all,
and the final statement; merely an attempt to make sense out of data.  Used
properly, it is valuable; used poorly, it is just as bad as other techniques
we use under suspicious conditions.
Newsgroups: bit.listserv.stat-l
From: jonathan@psych.psy.uq.oz.au (Jonathan Dwyer)
Subject: Re:      Primer on chaos theory??
Followup-To: bit.listserv.stat-l
Organization: Psychology, UQ
Date: Fri, 11 Sep 1992 02:34:45 GMT


I do not know of any decent introduction texts, or papers, although
Scientific American indexes would probably turn up something
appropriate.  I've used Scientific American articles on some very
esoteric mathematical concepts to teach students, they generally
get the original author of the technique or finding, and commission
them to write a "layman's article" on their work.  Very useful.

If you're using Chaos in a statistical modelling context, you may
personally (as opposed to being useful to your class?) find these
three articles worth reading:

Berliner, L. Mark (1992) "Statistics, probability and chaos"
                 Statistical Science (7:1) 69-122

Chatterjee, Sangit & Mustafa R. Tilmaz (1992) "Chaos, fractals
                 and statistics" Statistical Science (7:1) 49-68

Schubert, Siegfried D., Suarez, Max J. & Schemm, Jae-Kyung (1992)
                  "Persistence and predictability in a perfect model"
                 Journal of the Atmospheric Sciences (49:3) 256-270

The reference lists from these should set you onto other useful things too...

Good luck, and have fun!

Jon.
--
    __  __ ____
   / / / // __ \ Jonathan Dwyer (FAX+6173654466) 
  / / / // / / / Department of Psychology
 / / / // / / /  The University of Queensland
/ /_/ // /_/ <   St. Lucia, 4072. AUSTRALIA 
\____/*\_____/*  jonathan@psych.psy.uq.oz.au  
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Date:         Thu, 10 Sep 1992 08:54:44 EDT
From:         Frank Dane <FDANE@UGA.BITNET>
Subject:      Re: Blue + sad = ?
              10 Sep 1992 09:32:49 GMT from <RCONROY@IRLEARN.UCD.IE>

Ronan Conroy and others have indicated that summation may produce
"double weighting" in the form of giving a subject 2 points for
agreeing with the same symptom (or other statement) that is
represented by two differently worded items.  If subjects are
truly responding to the items as though they are the same, then the
item intercorrelation will be 1.00 and the researcher would know
(should know) to eliminate one of the redundant items.

Essentially, all I'm arguing in this exchange is that anyone who
employs multiple items that may be related subject those items to
preliminary analyses to determine the extent to which they represent
(from the viewpoint of the respondents) a unidimensional concept.

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
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Date:         Tue, 8 Sep 1992 17:02:11 EDT
From:         arthur s ellen <ELLEN@PACEVM.BITNET>
Subject:      likert scales and ordinal and interval measurement

Date:         Tue, 08 Sep 92 16:41:38 EDT
From:         arthur s ellen <ELLEN@PACEVM>
Subject:      Re: continuous likert scales
To:           Frank Dane <FDANE@UGA>

On Tue, 8 Sep 1992 12:23:12 EDT you said:
>
>A Likert Scale hasnothing to do with the response format.  A Likert
>Scale is a multi-item questionnaire containing a number (usually ten
>or so) statements with which respondants are asked to indicate
>agreement/disagreement.  The key to making the questionnaire a real
>scale is a series of analyses throuh which one eliminates items
>that do not have high interitem or item-total correlations.  The
>total score on the scale may be treated as an interval measure
>(that's what it was designed for), but one treats individual items
>as inerval measure with great risk.  Bottom line: a single item
>is not a scale.
>
A key question that should and I hope does arise  for many of us is:
Does consistency allow for addition?  That is,  if we find high item-total
correlation or high internal consistency (split-half or cronbach's) does it
then make sense to treat individual items as equilvalent units on some scale?
I don't believe that item similarity justifies summing individual items to then
obtain a total score that is meaningful. For example, is it meaningful
to add a score on a question like: I sometimes feel blue with the
response on question like: I am depressed every day in order to get
a score on a depression scale.  Yet these two items could be part of a very
consistent scale.

This problem also strikes at the prior suggestion that likert items be
changed to 0/1 and them summed.

------------------------------------------------------
      ART ELLEN             PSYCHOLOGY DEPARTMENT  
 BITNET:  ELLEN@PACEVM         PACE UNVERSITY      
  VOICE:  (212) 346-1506         41 PARK ROW       
                           NEW YORK, NY 10038-1502 
------------------------------------------------------
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Date:         Tue, 8 Sep 1992 08:51:18 -0500
From:         MGRANAAS@CHARLIE.USD.EDU
Subject:      continuous likert scales

I was told a few years back that a likert scale could be made continuous
by using a line with anchors at each end rather than the traditional
5 (or 7 or 9) point scale.  Instead of picking a number between 1 and 5,
the respondent marks a point along the line and the researcher measures
the distance from the mark to one of the anchors to obtain a score.

e.g.

      ___________________________/________________________
    Always                                             Never

Does anybody know if this in fact works?  References where I can learn more?

Thanks
mike

mgranaas@charlie.usd.edu
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Date:         Fri, 11 Sep 1992 08:11:57 -0500
From:         "Robert K. Toutkoushian" <TOUTKOUS@UMNACVX.BITNET>
Subject:      Re: Primer on chaos theory

Hello again,

        Thanks to all for the information on chaos theory. I am
going through some of the suggestions now, and will post what I`ve
found to be the most helpful. Several people have pointed to articles
in Scientific American, and another popular choice is a book by
James Gleick, "Chaos: Making A New Science", Penguin Books, 1987.


        I checked out the remaining copy of the Gleick book from the
library here at the U of M yesterday after bribing the librarian. When I
asked about the book, she replied that they "keep the book under lock and
key." Are mathematicians really that scared about this technique? :-) :-)

Rob Toutkoushian
University of Minnesota
INTERNET: toutkous@vx.cis.umn.edu
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Date:         Mon, 7 Sep 1992 14:48:00 EST
From:         dixon@SREL.EDU
Subject:      Robustness

Hi everyone:
  I have a simple question concerning robustness studies.  I expect
many different answers, but I am interested in the range and potential
consensus.

A typical study of robustness computes the empirical rejection frequency
for a test procedure with a nominal alpha level.   For example, for
data from a variety of non-normal distributions, the empirical rejection
frequency of a nominal 5% two sample t-test is betweeen 4% and 6%
when the sample size is larger than 10.  This is
often considered robust, but where you draw the line?  What is the largest
empirical rejection frequency (for a nominal 5% test) that you would
consider to be robust?  What about nominal 1% tests?

The same issues arise when considering the robustness of interval
estimates, so please feel free to translate the problem if you
prefer to consider interval estimation rather than hypothesis tests.
Is there any consensus, or only a case-by-case answer, based on
the costs of potential errors?

Thanks in advance,

Philip Dixon,   dixon@srel.edu
Biostatistician,
Savannah River Ecology Lab
University of Georgia
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Date:         Thu, 10 Sep 1992 08:41:00 MST
From:         LABENISH@UTAHCCA.BITNET
Subject:      LISREL 7 help

I need some help running a structural equation model using
LISREL 7.  I have a sample size of about 170, 2 indicators for
each of two exogenous latent variables and 2 indicators for one
of my 3 endogenous latent variables; I have one indicator for
the other 2 latent endogenous variables.

Prior to embarking on the structural model, I completed a
confirmatory factor analysis on the measurement piece of the
model just to make sure that the measures were loading fine  on
the hypothesized constructs.  This was confirmed.

The commands, freed and fixed elements, etc. for my basic
structural eq. model are:

 / DA NI=8  MA=CM
 / MO NY=4  NX=4   NE=3  NK=2  BE=FU  PS=DI

 / FR BE(2,1) BE(3,2)
 / FR LY(1,1)
 / FR LX(1,1) LX(4,2)

 / VA 1 LY(2,1) LY(3,2) LY(4,3)
 / VA 1 LX(2,1) LX(3,2)

 / FI   GA(2,2)  GA(3,1)  GA(3,2)


The error statement I get when I ran this model is that my PHI
and PSI matrices are not positivie definite.

With regard to my output (for what it may or may not be worth),
  LX -- looked reasonable
  LY -- one value close to 1.0
  BE -- VERY small values (virtually 0.0)
  GA -- small negative values
  PS -- VERY small values (virtually 0.0)
  TE -- Values ranging from 1.6 to 27.0
  TD -- Two VERY small values (.04) and two VERY large
        values (130.5)

  * Each of the values of the squared multiple correlation for
    the y variables was virtually 0.  Thus, the coefficient of
    determination was also 0.

  * Regarding the sq. mult. corr. for each of the x variables,
    2 of the 4 values were virtually 0 and the other two were
    about .85; the coeff. of deter. was .89

  * The values of the squared multiple correlations for the
    structural equations were -.16; 1.01; -.22

A few weeks ago a woman from Drake was encountering some problems
with one of her error matrices not being positive definite.  I
re-ran my model in accordance to the recommendations she received

        * Using EQ statements in error matrices [since the model
          is underidentified (i.e., gamma matrix)]
        * Identifying parameter estimates greater than 1.0 and
          using ST values for those estimates
        * Requesting Ordinary Least Squares and Generalized Least
          Squares solutions
        * Playing around with setting negative values in my
          error matrices at a value greater than 0.

At best, I got rid of the non-positive definite PH matrix, but PS
continued to be problematic.

I would VERY MUCH appreciate any 'pearls of wisdom' aboutit's
just difficult to express that over electronic mail)

orbenish@cc.utah.edu
la
labenish@utahcca.bitnet

(801) 626-6406 -- Work
(801) 531-1823 -- Home
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Date:         Tue, 15 Sep 1992 10:18:00 EDT
From:         "GEOFF SELIG - CONCORDIA U. COMPUTING SERVICES"
              <ILFC594@VAX2.CONCORDIA.CA>
Subject: Re: LISREL and Heywood cases

A propos the discussion of LISREL and Heywood cases, a faculty member at
Concordia University sent me the following reference.  (Please excuse the
yelling.  He doesn't know about lower case letters....)

 > I HAVE A GOOD REF. FOR YOU AND THE PERSON WHO ASKED THE ORIGINAL QUESTION:
 > DILLON, KUMAR, AND MULANI.  OFFENDING ESTIMATES IN COVARIANCE STRUCTURE
 > ANALYSIS:  COMMENTS ON THE CAUSES OF ALND SOLOUTIONS TO HEYWOOD CASES.
 > PSYCH.BULL. 1987, VOL. 101, NO.1, 126-135.

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
From: howard@iscsvax.uni.edu
Newsgroups: bit.listserv.stat-l
Subject: Re: election polls
Date: 10 Sep 92 12:22:28 -0500
Organization: University of Northern Iowa

I seriously question the randomness of the population that answer
the polls. Polls that are taken on one night seem especially suspect. 
If no one is home, are there attempts to call back repeatedly until
someone is reached, or is another name pulled out to call? This can
make a huge difference on the results. If calls are only made in the
evenings, everyone who works 2nd shift is not represented (nurses,
factory workers). People who "moonlight" by working a second job
(such as waiters), also are unlikely to be home.  So it seems to me
that the number of working people (leaning towards Clinton?) would
be underrepresented in the survey. 

Also, what is done when more than one adult resides in a household. Take
the case of a husband, wife, and over-18 year old son. If only one of 
them answers the survey, that may not represent what the others think. 
Poor people are more likely to have many adults living in a household
than more wealthy people, and are thus underrepresented (Leaning towards
Clinton? Maybe, but will they vote?). 

Not represented at all are those who do not have phones: the homeless, 
the poor, and about 100,000 homes in South Florida at the moment. Suppose
your random call gets "Tent City-Homestead": which of the 4,000 people
do you talk to?

Not only that, but people are so sick of polls that they lie- maybe
they are so sick of politics that they won't vote at all (Does that
hurt Bush or Clinton? Help Perot??). 


-Mary Howard

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