HDTenEightyP/NetNews huggingface dataset:
From: nichols@spss.com (David Nichols)
Subject: Response to Christopher Hall on FACTOR
Date: Tue, 21 Jul 1992 22:25:23 CUT
Organization: SPSS Inc.

Christopher Hall asks:

In SPSS Release 4.0 for the Macintosh what is meant by the warning # 11302 in 
Factor Analysis which states "The correlation matrix is ill-conditioned"?

Is the data unusable?  How does one "condition" the matrix and what strategies
are available for doing this?

----------------------------------------------------------------------------

The exact meaning of this message in current SPSS systems (prior to 
version 5, which I guess is now available for Windows, so everything
but this one) is that the determinant of the correlation matrix is less
than 10**-8. The determinant is equal to the continued product of the
eigenvalues, which must sum to the total amount of variance in the
matrix, which is equal to the number of variables in the correlation
matrix. What does all this mean? It means that this criterion is not a
good choice for when to issue an ill-conditioned matrix warning. This
has been changed for version 5 so that it will only be issued if the
smallest eigenvalue is less than 10*-12. 

If you can see that your smallest eigenvalue is nonzero (we print them
to 5 decimal places) your matrix shouldn't present FACTOR with any
numerical problems on account of near-linear dependencies. As far as I
know, FACTOR will not have problems even with extremely ill-conditioned
data; if it is a singular matrix, it will do what it can and then quit.

So the basic result out of all of this is that you can generally ignore
that warning message.

--
 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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Newsgroups: bit.listserv.spssx-l
Date:         Thu, 30 Jul 1992 14:18:25 SST
Reply-To:     Guy G Gable <ISCGUYGG@NUSVM.BITNET>
From:         Guy G Gable <ISCGUYGG@NUSVM.BITNET>
Subject:      Availability of Partial Least Squares from SPSS

Over the past few years I have been conducting a longitudinal study of
Information Systems Consultants and their involvement in computer system
selection projects. I have collected data on related success factors and have
thus far tested the model using factor analysis (SPSS/PC) to validate and
compute the latent constructs, followed by path analysis (SPSS/PC multiple
regression) to test the causal relationships. I have computed the various
effect components quasi-manually using a spreadsheet and matrix algebra (rather
than using LISREL or some other software which supports path analysis).

While I am happy with my results to date, I have been considering using
PC/LISREL version 7.16 from SPSS (which I now possess) to retest the model.
Primary reasons for my considering re-testing the data and model(s) using
LISREL are: (1) I believe one of the model paths should possibly be two-way,
(2) I would like to test the model using multiple dependent variables, and (3)
LISREL would appear to be the most widely employed approach to doing this sort
of analysis.

I have been reluctant to learn and use LISREL because, though my model is not
especially large or complex, my sample is small (between 50 and 100 cases
dependent upon what branches are included in the model).

This brings me to my question. I know little about Partial Least Squares or
PLS, but have been told it is more forgiving than LISREL in several respects,
including the sample size. Does SPSS have a PLS routine? Does anyone know of
any other PLS software for the PC? Does anyone have any knowledge of the
salient, relative strengths and weaknesses of LISREL and PLS? Cheers.
Newsgroups: bit.listserv.spssx-l
Organization: Penn State University
Date: Mon, 27 Jul 1992 22:11:52 EDT
From: <RFM@psuvm.psu.edu>
Newsgroups: bit.listserv.spssx-l
Subject: SUMMARY: SPSS-PC v. 4.01 under OS/2

Since I posted my query last week about SPSS-PC v 4.01 under OS/2, I've
received a half-dozen or so responses (Thanks to all who responded!)
All reported no probelms running the software under OS/2. A couple of
persons pointed out that there is an OS/2 version of SPSS: wish I could
afford....

Bob Munzenrider, Penn State-Harrisburg
Newsgroups: bit.listserv.spssx-l
From: nichols@spss.com (David Nichols)
Subject: Response to Bill Wu about neg. var. in ONEWAY
Date: Tue, 21 Jul 1992 22:10:33 CUT
Organization: SPSS Inc.

Bill Wu asks:

In my ONEWAY output shows a warning - Between commponent variance is negative
it was replaced by 0.0 ..... (Random effects model)

I am not clear how this variance was calculated? any reference? and how to
interpret a negative variance?

-----------------------------------------------------------------------------

The formula for the estimate of between components variance used in ONEWAY
is:

      N * ( G - 1 ) * ( MSB - MSE )
      -----------------------------
             2           2
            N  -  SUM ( N  )
                         i

where N is the total sample size
      N  is the sample size in the ith group (assumed to be positive)
       i
      G is the number of nonempty groups
      MSB is the between groups mean square
      MSE is the error or within groups mean square

The measure is referred to in our algorithms as Omega-squared, and the
reference is to Snedecor and Cochran.

From looking at the formula, it is obvious that the estimate will be
negative if and only if the MSE is larger than the MSB, which means
that this will occur whenever the F ratio is less than 1. 

Recall that the logic for using the F ratio to test the null hypothesis
of no difference among population group means is that, under the null
hypothesis, each mean square represents an independent estimate of the
error variance. If the null hypothesis is not true, then the MSB also
includes a component due to variance among groups. On some number of
occasions these two estimates will be such that the MSE is larger than
the MSB (this is more likely the smaller the group effect, and will
always happen when there is no difference among the sample means) and
the estimate of between components variance will be negative. Since it
is known that this estimate cannot be correct, 0 is substituted.

This is probably the second most common occurrence of negative estimates
of essentially non-negative quantities you will encounter. The most common
one (I believe) is the Heywood case in factor analysis, in which an
estimate of the unique variance of a variable becomes negative. 

--
 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.
Newsgroups: bit.listserv.spssx-l
From: dvoelker@leland.Stanford.EDU (David Voelker)
Subject: Ill-conditioned = singular = multicollinear = no determinant?
Organization: DSG, Stanford University, CA 94305, USA
Date: Fri, 24 Jul 92 20:10:29 GMT

The following phrases are often used interchangeably:

            The matrix is ill-conditioned

            The matrix is singular
           
            The determinant cannot be computed
       
            Multicollinearity is present

     Is there general agreement that these statements are equivalent?
If so, the use of a variety of terms to refer to the same condition is
bound to lead to confusion.  If they're different, a careful explication
of each would be of benefit to many.

     Anyone care to address this?
-- 
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|   Dave Voelker      Stanford University     dvoelker@leland.stanford.edu  |
=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Newsgroups: bit.listserv.spssx-l
Subject: Re: Availability of Partial Least Squares from SPSS
From: nichols@spss.com (David Nichols)
Date: Fri, 31 Jul 1992 19:14:05 CUT
Organization: SPSS Inc.

SPSS does not have a procedure to do Partial Least Squares.

In Volume 6 of the _Encyclopedia of Statistical Sciences_, edited by
Kotz and Johnson, H. Wold (who would seem to be the major inventor of
PLS) gives a long discussion of exactly the things you were asking
about, and also a list of references. I'd start there.

--
 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.
Organization: Penn State University
Date: Thu, 23 Jul 1992 22:06:46 EDT
From: <RFM@psuvm.psu.edu>
Newsgroups: bit.listserv.spssx-l
Subject: SPSS-PC 4.01 on OS/2 2.0 ???

A week or two ago, there was a posting about SPSS-PC not running under
OS/2. I lost track of thread/net for a few days, and never saw responses -
if any were made. Is it an issue? Will SPSS-PC run in a DOS session under
OS/2 2.0?
Thanks,
BobM
Newsgroups: bit.listserv.spssx-l
From: jlaplant@magnus.acs.ohio-state.edu (John R Laplante)
Subject: getting standard scores without summary data on raw data
Organization: The Ohio State University
Distribution: usa
Date: Fri, 24 Jul 1992 21:08:39 GMT


 I need to transform some data into standard scores in order to do some
 further analysis. I am not interested in the raw data itself. The only way
 I know how to get standard scores is with the comand/subcommand of
 DESCRIPTIVES variables = list of variables
  /SAVE
 It gives me Z variables, which is great. But it also gives me summary
 stats on the original (unstandardized) variables. I'm not interested in 
 _those_ stats, so the result is that it slows down my program and just
 gives me more pages of printout.

 I've looked through the advanced guide, but i can't find anything.
 I am using release 3.0 

--------------------------------------------------------------------------
john r la plante                    calling from the Ohio State University
jlaplant@magnus.acs.ohio-state.edu  political analysis done while you wait

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Newsgroups: bit.listserv.spssx-l
Date:         Thu, 23 Jul 1992 13:00:00 PDT
Reply-To:     Melvin Klassen <KLASSEN@UVVM.BITNET>
Comments:     Warning -- original Sender: tag was NETNEWS@UVVM.UVIC.CA
From:         Melvin Klassen <KLASSEN@UVVM.BITNET>
Subject:      Re: 'stacking procedures'

Eric Dowdy <edowdy@VAX1.UMKC.EDU> writes:
>        I want to 'stack' several of the same procedures (eg: mean, stdev, se)
>for the same variable generated by each procedure. I then want to treat those
>summary descriptive statistics as my raw data file to be read and analyzed by
>another spss program file.  How do I do this?
>
Use the DESCRIPTIVE procedure to compute univariate summary statistics
and standardized variables, and save them to the "active file".
Writing to the "active-file" is faster than writing to, and then reading from,
an external file.
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Mailer: Elm [revision: 66.36.1.1]
Newsgroups: bit.listserv.spssx-l
Date:         Tue, 28 Jul 1992 11:07:41 EDT
Reply-To:     John Jaskir <jaskir@UMDNJ.EDU>
From:         John Jaskir <jaskir@UMDNJ.EDU>
Subject:      Final Statistics in Factor
Comments: To: spssx-l@uga.cc.uga.edu

Can anyone shed some light on why SPSS-PC does not compute final
eigenvalues and percent of variance explained for rotated factors ?

Specifically, I'm refering to a principal components analysis with
varimax rotation.

The "final" statistics in the output, I believe, are potentially
misleading since it refers to the variance explained by the factors
only after the extraction stage. However, when factors are orthogonally
rotated the distribution of the explained variance changes amoung the
factors. The only way I've been able to determine this new distribution
is to calculate the eigenvales by hand and divide by the number of
variables.

Am I missing something?

----------------------------------
jaskir@umdnj.edu
From: den0@ns1.cc.lehigh.edu (DEAN E. NELSON)
Newsgroups: bit.listserv.spssx-l
Subject: Translating CYBER SPSS system files for AIX
Date: 22 Jul 92 19:19:43 GMT
Organization: Lehigh University

We have recently unplugged our CYBER which was running SPSSX 2.2 under NOS/VE.
After several months of attempting to ensure that our users migrated all of
their files successfully, we have encountered our first user who failed to
change sytem files to export files before moving them.

Our new system is an RS-6000 running SPSS version 4 under AIX 3.1.  Without
finding another CYBER site (which we are also pursuing as a solution), does
anyone have the means to translate these system files to a usable form.

Dean Nelson  (215)758-4988
User Consultant
Lehigh University
-- 
Dean E. Nelson  den0@lehigh.edu  (Internet)
(215)-758-4988  (That is d-e-n-zero, not the letter O)
 
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X-Envelope-to: SPSSX-L@uga.cc.uga.edu
Newsgroups: bit.listserv.spssx-l
Date:         Fri, 31 Jul 1992 11:09:36 MET
Reply-To:     Hessels@SOA.FSW.EUR.NL
From:         Hessels@SOA.FSW.EUR.NL
Subject:      re: Final Statistics in Factor
Comments: To: SPSSX-L@uga.cc.uga.edu

John Jaskir asked the following:

> Can anyone shed some light on why SPSS-PC does not compute final
> eigenvalues and percent of variance explained for rotated factors ?

> Specifically, I'm refering to a principal components analysis with
> varimax rotation.

> The "final" statistics in the output, I believe, are potentially
> misleading since it refers to the variance explained by the factors
> only after the extraction stage. However, when factors are orthogonally
> rotated the distribution of the explained variance changes amoung the
> factors. The only way I've been able to determine this new distribution
> is to calculate the eigenvales by hand and divide by the number of
> variables.

> Am I missing something?


The total amount of of variation explained by the rotated components is the
same as the amount of variation explained by the original principal
components, since the rotated coordinate axes lie in the same plane defined by
the original principal component axes.

See e.g. G.H. Dunteman (1989).
Principal Components Analysis. Sage University Paper on Quantative Application
in the Social Sciences.

 ******************************************************************************
                                     Marco G.P. Hessels
   __ ___   ___                      Rotterdam Institute for Sociological and
    |/ _ \_/ _ \                     Public Administration Research (RISBO)
   <|  0  |  0  |>                   Erasmus University
    | (   >   ) |  _   _  _  _       P.O. Box 1738
    |   \___/   | (_| |  (_ (_)      3000 DR Rotterdam

 Phone: (0)10-4082068     Fax: (0)10-4529734     E-mail: Hessels@SOA.FSW.EUR.NL
 ******************************************************************************
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Newsgroups: bit.listserv.spssx-l
Date:         Fri, 31 Jul 1992 15:03:45 -0400
Reply-To:     RJZ@UTXVM.BITNET
From:         RJZ@UTXVM.BITNET
Subject:      re: standardized betas > 1

Marco Hessels writes:

>A colleague of mine found a beta value greater than 1 for a variable
>in a multiple regression analysis.
>
>Could this be caused by multicollinearity or are there other sources
>as well that can cause this?


I assume you are talking about standardized Beta parameters, not
the unstandardized variety.  It is quite normal for unstandardized
regression parameters to take on values much greater than one or much
less than one.  While standardized Betas are typically in the range of
one to negative one, other values are possible and not abnormal.
Values much greater than one or much less than one are possible,
and while they may be an indication of multicolliniariy, they can occur
in cases where this is not a problem.  For a very nice description of
when this condition occurs, see Darlington, R. (1990) REGRESSION AND
LINEAR MODELS, published by McGraw-Hill, pg.s 50-51.  Basically, given
two independent variables both correlated to the dependent variable,
the relative size of their Beta parameters depends on how they are
correlated with each other.  For example (from Darlington), for two
vars, X1 and X2 which have r(Y-X1)=.5 and r(Y-X2)=.3, both will have
Beta values greater than one when r(X1-X2) < -0.6 (approx). When
r(X1-X2) > 0.85 (approx), the Beta value for X1 approaches positive
infinity, while Beta for X2 approaches negative infinity.  Hope this
helps.

Bob Zambarano                         | "Success is the greatest liar"
Statistical and Mathematical Services |
University of Texas@Austin  78712     |                 Nietzsche
rjz@utxvm.bitnet                      |
ccbe001@utxvm.cc.utexas.edu           |
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Organization: Connecticut's Business School
Newsgroups: bit.listserv.spssx-l
Date:         Wed, 29 Jul 1992 23:52:27 EST
Reply-To:     Murph Sewall <SEWALL@UCONNVM.BITNET>
From:         Murph Sewall <SEWALL@UCONNVM.BITNET>
Subject:      Re: Comparison of respondents to non-respondents or Population?
Comments: To: "Brian K. Pickerill" <00BKPICKERIL@BSUVAX1.BITNET>
              29 Jul 1992 15:44:00 EST from <00BKPICKERIL@BSUVAX 1>

On Wed, 29 Jul 1992 15:44:00 EST you said:
>The survey work that I do often involves surveying an entire population
>of students.  Therefore, there isn't any sampling, just a potential for
>response bias.   Generally, the question is wether the respondents are
>representative of the population, but I also get somewhat hung-up on the
>question of whether there is a difference between respondents and
>non-respondents. After all, comparing respondents to the entire
>population is comparing respondents to _themselves_ AND the
>non-respondents.

What question do you want answered?  If the question is does the sample
represent the population, then test against the population (the larger
the fraction of the population you actually get to respond, the more
likely it is to represent the population).  If the question is are
non-respondents systematically different from those who respond, then
test respondents against non-respondents.  In most study access to
statistics for the sample frame aren't available; the deriving
non-respondent statistics by backing out the sample from the frame isn't
feasible; so the question doesn't come up.

/s Murph Sewall <Sewall@UConnVM.UConn.Edu>
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Newsgroups: bit.listserv.spssx-l
Date:         Sat, 25 Jul 1992 17:28:52 EDT
Reply-To:     Kevin Hodge <CCUSEKH@GITVM1.BITNET>
From:         Kevin Hodge <CCUSEKH@GITVM1.BITNET>
Subject:      Re: SPSS-PC 4.01 on OS/2 ???

RFM@PSUVM.PSU.EDU writes:
>A week or two ago, there was a posting about SPSS-PC not running under
>OS/2. I lost track of thread/net for a few days, and never saw response -
>if any were made. Is it an issue? Will SPSS-PC run in a DOS session undr
>OS/2 2.0?
>Thanks,
>BobM

Using OS/2's migrate facility, I migrated SPSS-PC+ 4.01 to the Workplace
Shell.  To start it, I double-click on the icon.  I have not used
it much, but I did run the teststreams that SPSS provides and there
were NO problems.  I'm pretty certain that I did not have to modify/
tweak any of the settings that OS/2 chose.  If needed, I can check
on them.  Actually, I've yet to experience problems with any DOS
application under OS/2 2.0.

+---------------------------------+------------------------------------+
| Kevin Hodge, graduate student   |                                    |
| engineering psychology program  | BITNET:   ccusekh@gitvm1           |
| School of Psychology            | INTERNET: ccusekh@vm1.gatech.edu   |
| Georgia Institute of Technology | VOICE: (404) 853-0616              |
| Atlanta, GA 30332-0170          | FAX:   (404) 894-8905              |
+---------------------------------+------------------------------------+
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Organization: Connecticut's Business School
Newsgroups: bit.listserv.spssx-l
Date:         Thu, 23 Jul 1992 14:13:21 EST
Reply-To:     Murph Sewall <SEWALL@UCONNVM.BITNET>
From:         Murph Sewall <SEWALL@UCONNVM.BITNET>
Subject:      Re: ill-conditioned matrix
Comments: To: Stephen Salbod <SALBOD1@PACEVM.BITNET>

On Wed, 22 Jul 1992 13:11:28 EDT you said:
>    Problem:   PC analyses is being applied to the responses of 300
>               students to a 107 item scale. A warning is given that the
>               matrix is ill-conditioned!

Wasn't this answered last week (maybe the week before)?

At least one of the 107 items is VERY highly correlated with some linear
combination of the others.  That's not really surprising given the small
number of cases relative to the number of items.  Students often are
justified as subjects on the grounds that they are a relatively
homogeneous population (except for deliberately introduced treatment
differences).  It wouldn't surprise me that 300 students provide highly
similar answers to at least some combinations of 107 questions.  BMD's
factor routines take a stab at identifying the "culprit" (the variable
being operated on when the determinate approached zero).  It could be a
nice feature to add to SPSS, but in all likelihood once the identified
variable is removed, another would probably take its place (an process
that could iterate for 50 variables or so).

>    Question: Is there a solution to this problem?

More conceptualization; less thrashing around in a data haystack hoping
a needle will turn up.

If your colleague insists on the brute force approach; another 900 or so
observations (based on the oft use but conceptually dubious ten
observations per item rule of thumb) might help the statistical (though
not the conceptual) problem.

/s Murph Sewall <Sewall@UConnVM.UConn.Edu>
Newsgroups: bit.listserv.spssx-l
Subject: 'stacking procedures'
From: edowdy@vax1.umkc.edu
Date: Wed, 22 Jul 1992 20:03:40 GMT
Organization: University of Missouri - Kansas City
Keywords: stack
Summary: how do I stack several same procedures?

I have a question, hopefully someone more knowledgable than I in SPSS language:

	I want to 'stack' several of the same procedures (eg: mean, stdev, se)
for the same variable generated by each procedure. I then want to treat those
summary descriptive statistics as my raw data file to be read and analyzed by
another spss program file.

How do I do this?

Thanks in advance
Eric Dowdy
edowdy@vax1.umkc.edu
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X-Envelope-to: SPSSX-L%UALTAVM@ugw.utcs.utoronto.ca
X-VMS-To: NET"SPSSX-L@UALTAVM"
X-VMS-Cc: JSHIRK
Newsgroups: bit.listserv.spssx-l
Date:         Thu, 23 Jul 1992 17:21:00 -0400
Reply-To:     JSHIRK@TRENTU.CA
From:         JSHIRK@TRENTU.CA
Comments: To: SPSSX-L%UALTAVM@UTORUGW.BITNET

SET SPSSX-L CONCEAL
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