Parallel Analysis Syntax

[greg-169]

Hey folks,

I was wondering if anyone could help me. I am runing SPSS 16 on Mac OS X and
I am trying to get the parallel analysis syntax to run


I ahve tried to use:
Get file "*" to use the current data set but I get an error message that *
is in invalid file name

I have tried

Get file "file name.sav"/Users/Greg/desktop
get file /users/greg/desktop/"filename.sav"
get file "/users/greg/desktop/"filename.sav"

Everytime I get an error message that the file does not exist. Does anyone
know what I am doing wrong?

* Parallel Analysis Program For Raw Data and Data Permutations.

* This program conducts parallel analyses on data files in which
  the rows of the data matrix are cases/individuals and the
  columns are variables;  Data are read/entered into the program
  using the GET command (see the GET command below);  The GET
  command reads an SPSS systemfile, which can be either the
  current, active SPSS data file or a previously saved systemfile;
  A valid filename/location must be specified on the GET command;
  A subset of variables for the analyses can be specified by using
  the "/ VAR =" subcommand with the GET statement;  There can be
  no missing values.

* You must also specify:
  -- the # of parallel data sets for the analyses;
  -- the desired percentile of the distribution and random
     data eigenvalues;
  -- whether principal components analyses or principal axis/common
     factor analysis are to be conducted, and
  -- whether normally distributed random data generation or
     permutations of the raw data set are to be used in the
     parallel analyses.

* WARNING: Permutations of the raw data set are time consuming;
  Each parallel data set is based on column-wise random shufflings
  of the values in the raw data matrix using Castellan's (1992,
  BRMIC, 24, 72-77) algorithm; The distributions of the original
  raw variables are exactly preserved in the shuffled versions used
  in the parallel analyses; Permutations of the raw data set are
  thus highly accurate and most relevant, especially in cases where
  the raw data are not normally distributed or when they do not meet
  the assumption of multivariate normality (see Longman & Holden,
  1992, BRMIC, 24, 493, for a Fortran version); If you would
  like to go this route, it is perhaps best to (1) first run a
  normally distributed random data generation parallel analysis to
  familiarize yourself with the program and to get a ballpark
  reference point for the number of factors/components;
  (2) then run a permutations of the raw data parallel analysis
  using a small number of datasets (e.g., 10), just to see how long
  the program takes to run; then (3) run a permutations of the raw
  data parallel analysis using the number of parallel data sets that
  you would like use for your final analyses; 1000 datasets are
  usually sufficient, although more datasets should be used if
  there are close calls.


* These next commands generate artificial raw data
  (50 cases) that can be used for a trial-run of
  the program, instead of using your own raw data;
  Just select and run this whole file; However, make sure to
  delete these commands before attempting to run your own data.

* Start of artificial data commands.
set length=none printback = off  width = 120.
input program.
loop #a=1 to 500.
compute com1 = normal (10).
compute com2 = normal (10).
compute com3 = normal (10).
compute var1 = normal (10) + com1.
compute var2 = normal (10) + com1.
compute var3 = normal (10) + com1.
compute var4 = normal (10) + com2.
compute var5 = normal (10) + com2.
compute var6 = normal (10) + com2.
compute var7 = normal (10) + com3.
compute var8 = normal (10) + com3.
compute var9 = normal (10) + com3.
end case.
end loop.
end file.
end input program.
factor var = var1 to var9.
* End of artificial data commands.



set mxloops=9000 printback=off width=80  seed = 1953125.
matrix.

* Enter the name/location of the data file for analyses after "FILE =";
  If you specify "FILE = *", then the program will read the current,
  active SPSS data file;  You can alternatively enter the name/location
  of a previously saved SPSS systemfile instead of "*";
  you can use the "/ VAR =" subcommand after "/ missing=omit"
  subcommand to select variables for the analyses.
GET raw / FILE = * / missing=omit / VAR = var1 to var9.

* Enter the desired number of parallel data sets here.
compute ndatsets = 100.

* Enter the desired percentile here.
compute percent  = 95.

* Enter either
  1 for principal components analysis, or
  2 for principal axis/common factor analysis.
compute kind = 2 .

* Enter either
  1 for normally distributed random data generation parallel analysis, or
  2 for permutations of the raw data set.
compute randtype = 1.

****************** End of user specifications. ******************

compute ncases   = nrow(raw).
compute nvars    = ncol(raw).

* principal components analysis & random normal data generation.
do if (kind = 1 and randtype = 1).
compute nm1 = 1 / (ncases-1).
compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute realeval = eval(d * vcv * d).
compute evals = make(nvars,ndatsets,-9999).
loop #nds = 1 to ndatsets.
compute x = sqrt(2 * (ln(uniform(ncases,nvars)) * -1) ) &*
            cos(6.283185 * uniform(ncases,nvars) ).
compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute evals(:,#nds) = eval(d * vcv * d).
end loop.
end if.

* principal components analysis & raw data permutation.
do if (kind = 1 and randtype = 2).
compute nm1 = 1 / (ncases-1).
compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute realeval = eval(d * vcv * d).
compute evals = make(nvars,ndatsets,-9999).
loop #nds = 1 to ndatsets.
compute x = raw.
loop #c = 1 to nvars.
loop #r = 1 to (ncases -1).
compute k = trunc( (ncases - #r + 1) * uniform(1,1) + 1 )  + #r - 1.
compute d = x(#r,#c).
compute x(#r,#c) = x(k,#c).
compute x(k,#c) = d.
end loop.
end loop.
compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute evals(:,#nds) = eval(d * vcv * d).
end loop.
end if.

* PAF/common factor analysis & random normal data generation.
do if (kind = 2 and randtype = 1).
compute nm1 = 1 / (ncases-1).
compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute cr = (d * vcv * d).
compute smc = 1 - (1 &/ diag(inv(cr)) ).
call setdiag(cr,smc).
compute realeval = eval(cr).
compute evals = make(nvars,ndatsets,-9999).
compute nm1 = 1 / (ncases-1).
loop #nds = 1 to ndatsets.
compute x = sqrt(2 * (ln(uniform(ncases,nvars)) * -1) ) &*
            cos(6.283185 * uniform(ncases,nvars) ).
compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute r = d * vcv * d.
compute smc = 1 - (1 &/ diag(inv(r)) ).
call setdiag(r,smc).
compute evals(:,#nds) = eval(r).
end loop.
end if.

* PAF/common factor analysis & raw data permutation.
do if (kind = 2 and randtype = 2).
compute nm1 = 1 / (ncases-1).
compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute cr = (d * vcv * d).
compute smc = 1 - (1 &/ diag(inv(cr)) ).
call setdiag(cr,smc).
compute realeval = eval(cr).
compute evals = make(nvars,ndatsets,-9999).
compute nm1 = 1 / (ncases-1).
loop #nds = 1 to ndatsets.
compute x = raw.
loop #c = 1 to nvars.
loop #r = 1 to (ncases -1).
compute k = trunc( (ncases - #r + 1) * uniform(1,1) + 1 )  + #r - 1.
compute d = x(#r,#c).
compute x(#r,#c) = x(k,#c).
compute x(k,#c) = d.
end loop.
end loop.
compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
compute d = inv(mdiag(sqrt(diag(vcv)))).
compute r = d * vcv * d.
compute smc = 1 - (1 &/ diag(inv(r)) ).
call setdiag(r,smc).
compute evals(:,#nds) = eval(r).
end loop.
end if.

* identifying the eigenvalues corresponding to the desired percentile.
compute num = rnd((percent*ndatsets)/100).
compute results = { t(1:nvars), realeval, t(1:nvars), t(1:nvars) }.
loop #root = 1 to nvars.
compute ranks = rnkorder(evals(#root,:)).
loop #col = 1 to ndatsets.
do if (ranks(1,#col) = num).
compute results(#root,4) = evals(#root,#col).
break.
end if.
end loop.
end loop.
compute results(:,3) = rsum(evals) / ndatsets.

print /title="PARALLEL ANALYSIS:".
do if (kind = 1 and randtype = 1).
print /title="Principal Components & Random Normal Data Generation".
else if (kind = 1 and randtype = 2).
print /title="Principal Components & Raw Data Permutation".
else if (kind = 2 and randtype = 1).
print /title="PAF/Common Factor Analysis & Random Normal Data Generation".
else if (kind = 2 and randtype = 2).
print /title="PAF/Common Factor Analysis & Raw Data Permutation".
end if.
compute specifs = {ncases; nvars; ndatsets; percent}.
print specifs /title="Specifications for this Run:"
 /rlabels="Ncases" "Nvars" "Ndatsets" "Percent".
print results
 /title="Raw Data Eigenvalues, & Mean & Percentile Random Data Eigenvalues"
 /clabels="Root" "Raw Data" "Means" "Prcntyle"  /format "f12.6".

do if   (kind = 2).
print / space = 1.
print /title="Warning: Parallel analyses of adjusted correlation matrices".
print /title="eg, with SMCs on the diagonal, tend to indicate more factors".
print /title="than warranted (Buja, A., & Eyuboglu, N., 1992, Remarks on
parallel".
print /title="analysis. Multivariate Behavioral Research, 27, 509-540.).".
print /title="The eigenvalues for trivial, negligible factors in the real".
print /title="data commonly surpass corresponding random data eigenvalues".
print /title="for the same roots. The eigenvalues from parallel analyses".
print /title="can be used to determine the real data eigenvalues that are".
print /title="beyond chance, but additional procedures should then be used".
print /title="to trim trivial factors.".
print / space = 2.
print /title="Principal components eigenvalues are often used to determine".
print /title="the number of common factors. This is the default in most".
print /title="statistical software packages, and it is the primary practice".
print /title="in the literature. It is also the method used by many factor".
print /title="analysis experts, including Cattell, who often examined".
print /title="principal components eigenvalues in his scree plots to determine".
print /title="the number of common factors. But others believe this common".
print /title="practice is wrong. Principal components eigenvalues are based".
print /title="on all of the variance in correlation matrices, including both".
print /title="the variance that is shared among variables and the variances".
print /title="that are unique to the variables. In contrast, principal".
print /title="axis eigenvalues are based solely on the shared variance".
print /title="among the variables. The two procedures are qualitatively".
print /title="different. Some therefore claim that the eigenvalues from one".
print /title="extraction method should not be used to determine".
print /title="the number of factors for the other extraction method.".
print /title="The issue remains neglected and unsettled.".
end if.

compute root      = results(:,1).
compute rawdata = results(:,2).
compute percntyl = results(:,4).

save results /outfile=* / var=root rawdata means percntyl .

end matrix.

* plots the eigenvalues, by root, for the real/raw data and for the random data;
  This command works in SPSS 12, but not in all earlier versions.
TSPLOT VARIABLES= rawdata means percntyl /ID= root /NOLOG.

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[Richard Ristow]

At 02:34 PM 11/25/2008, greg wrote:

>I am running SPSS 16 on Mac OS X

If there's anything Mac-specific to your problem, I can't help; my
recent SPSS experience is all Windows. But carrying on, just thinking SPSS,

>I have tried to use:
>Get file "*"
>to use the current [active] data set but I get an error message that
>* is in invalid file name

Right. But to get the current active dataset, you write nothing at
all; it's already there. If you need a dataset that's open but
inactive, use DATASET ACTIVATE. (Your documentation says, "If you
specify "[GET] FILE = *", then the program will read the current,
active SPSS data file", but that's wrong.)

>I have tried
>
>Get file "file name.sav"/Users/Greg/desktop
>get file /users/greg/desktop/"filename.sav"
>get file "/users/greg/desktop/"filename.sav"
>
>Everytime I get an error message that the file does not exist.

Without detailing the problems (I will, if you like), the third form
is closest, with just an extra '"' before the filename. (So there are
three '"' characters in the statement, a giveaway for trouble.) Try

get file "/users/greg/desktop/filename.sav"

>  Does anyone
>know what I am doing wrong?
>
>* Parallel Analysis Program For Raw Data and Data Permutations.
>
>* This program conducts parallel analyses on data files in which
>   the rows of the data matrix are cases/individuals and the
>   columns are variables;  Data are read/entered into the program
>   using the GET command (see the GET command below);  The GET
>   command reads an SPSS systemfile, which can be either the
>   current, active SPSS data file or a previously saved systemfile;
>   A valid filename/location must be specified on the GET command;
>   A subset of variables for the analyses can be specified by using
>   the "/ VAR =" subcommand with the GET statement;  There can be
>   no missing values.
>
>* You must also specify:
>   -- the # of parallel data sets for the analyses;
>   -- the desired percentile of the distribution and random
>      data eigenvalues;
>   -- whether principal components analyses or principal axis/common
>      factor analysis are to be conducted, and
>   -- whether normally distributed random data generation or
>      permutations of the raw data set are to be used in the
>      parallel analyses.
>
>* WARNING: Permutations of the raw data set are time consuming;
>   Each parallel data set is based on column-wise random shufflings
>   of the values in the raw data matrix using Castellan's (1992,
>   BRMIC, 24, 72-77) algorithm; The distributions of the original
>   raw variables are exactly preserved in the shuffled versions used
>   in the parallel analyses; Permutations of the raw data set are
>   thus highly accurate and most relevant, especially in cases where
>   the raw data are not normally distributed or when they do not meet
>   the assumption of multivariate normality (see Longman & Holden,
>   1992, BRMIC, 24, 493, for a Fortran version); If you would
>   like to go this route, it is perhaps best to (1) first run a
>   normally distributed random data generation parallel analysis to
>   familiarize yourself with the program and to get a ballpark
>   reference point for the number of factors/components;
>   (2) then run a permutations of the raw data parallel analysis
>   using a small number of datasets (e.g., 10), just to see how long
>   the program takes to run; then (3) run a permutations of the raw
>   data parallel analysis using the number of parallel data sets that
>   you would like use for your final analyses; 1000 datasets are
>   usually sufficient, although more datasets should be used if
>   there are close calls.
>
>
>* These next commands generate artificial raw data
>   (50 cases) that can be used for a trial-run of
>   the program, instead of using your own raw data;
>   Just select and run this whole file; However, make sure to
>   delete these commands before attempting to run your own data.
>
>* Start of artificial data commands.
>set length=none printback = off  width = 120.
>input program.
>loop #a=1 to 500.
>compute com1 = normal (10).
>compute com2 = normal (10).
>compute com3 = normal (10).
>compute var1 = normal (10) + com1.
>compute var2 = normal (10) + com1.
>compute var3 = normal (10) + com1.
>compute var4 = normal (10) + com2.
>compute var5 = normal (10) + com2.
>compute var6 = normal (10) + com2.
>compute var7 = normal (10) + com3.
>compute var8 = normal (10) + com3.
>compute var9 = normal (10) + com3.
>end case.
>end loop.
>end file.
>end input program.
>factor var = var1 to var9.
>* End of artificial data commands.
>
>
>
>set mxloops=9000 printback=off width=80  seed = 1953125.
>matrix.
>
>* Enter the name/location of the data file for analyses after "FILE =";
>   If you specify "FILE = *", then the program will read the current,
>   active SPSS data file;  You can alternatively enter the name/location
>   of a previously saved SPSS systemfile instead of "*";
>   you can use the "/ VAR =" subcommand after "/ missing=omit"
>   subcommand to select variables for the analyses.
>GET raw / FILE = * / missing=omit / VAR = var1 to var9.
>
>* Enter the desired number of parallel data sets here.
>compute ndatsets = 100.
>
>* Enter the desired percentile here.
>compute percent  = 95.
>
>* Enter either
>   1 for principal components analysis, or
>   2 for principal axis/common factor analysis.
>compute kind = 2 .
>
>* Enter either
>   1 for normally distributed random data generation parallel analysis, or
>   2 for permutations of the raw data set.
>compute randtype = 1.
>
>****************** End of user specifications. ******************
>
>compute ncases   = nrow(raw).
>compute nvars    = ncol(raw).
>
>* principal components analysis & random normal data generation.
>do if (kind = 1 and randtype = 1).
>compute nm1 = 1 / (ncases-1).
>compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute realeval = eval(d * vcv * d).
>compute evals = make(nvars,ndatsets,-9999).
>loop #nds = 1 to ndatsets.
>compute x = sqrt(2 * (ln(uniform(ncases,nvars)) * -1) ) &*
>             cos(6.283185 * uniform(ncases,nvars) ).
>compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute evals(:,#nds) = eval(d * vcv * d).
>end loop.
>end if.
>
>* principal components analysis & raw data permutation.
>do if (kind = 1 and randtype = 2).
>compute nm1 = 1 / (ncases-1).
>compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute realeval = eval(d * vcv * d).
>compute evals = make(nvars,ndatsets,-9999).
>loop #nds = 1 to ndatsets.
>compute x = raw.
>loop #c = 1 to nvars.
>loop #r = 1 to (ncases -1).
>compute k = trunc( (ncases - #r + 1) * uniform(1,1) + 1 )  + #r - 1.
>compute d = x(#r,#c).
>compute x(#r,#c) = x(k,#c).
>compute x(k,#c) = d.
>end loop.
>end loop.
>compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute evals(:,#nds) = eval(d * vcv * d).
>end loop.
>end if.
>
>* PAF/common factor analysis & random normal data generation.
>do if (kind = 2 and randtype = 1).
>compute nm1 = 1 / (ncases-1).
>compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute cr = (d * vcv * d).
>compute smc = 1 - (1 &/ diag(inv(cr)) ).
>call setdiag(cr,smc).
>compute realeval = eval(cr).
>compute evals = make(nvars,ndatsets,-9999).
>compute nm1 = 1 / (ncases-1).
>loop #nds = 1 to ndatsets.
>compute x = sqrt(2 * (ln(uniform(ncases,nvars)) * -1) ) &*
>             cos(6.283185 * uniform(ncases,nvars) ).
>compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute r = d * vcv * d.
>compute smc = 1 - (1 &/ diag(inv(r)) ).
>call setdiag(r,smc).
>compute evals(:,#nds) = eval(r).
>end loop.
>end if.
>
>* PAF/common factor analysis & raw data permutation.
>do if (kind = 2 and randtype = 2).
>compute nm1 = 1 / (ncases-1).
>compute vcv = nm1 * (sscp(raw) - ((t(csum(raw))*csum(raw))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute cr = (d * vcv * d).
>compute smc = 1 - (1 &/ diag(inv(cr)) ).
>call setdiag(cr,smc).
>compute realeval = eval(cr).
>compute evals = make(nvars,ndatsets,-9999).
>compute nm1 = 1 / (ncases-1).
>loop #nds = 1 to ndatsets.
>compute x = raw.
>loop #c = 1 to nvars.
>loop #r = 1 to (ncases -1).
>compute k = trunc( (ncases - #r + 1) * uniform(1,1) + 1 )  + #r - 1.
>compute d = x(#r,#c).
>compute x(#r,#c) = x(k,#c).
>compute x(k,#c) = d.
>end loop.
>end loop.
>compute vcv = nm1 * (sscp(x) - ((t(csum(x))*csum(x))/ncases)).
>compute d = inv(mdiag(sqrt(diag(vcv)))).
>compute r = d * vcv * d.
>compute smc = 1 - (1 &/ diag(inv(r)) ).
>call setdiag(r,smc).
>compute evals(:,#nds) = eval(r).
>end loop.
>end if.
>
>* identifying the eigenvalues corresponding to the desired percentile.
>compute num = rnd((percent*ndatsets)/100).
>compute results = { t(1:nvars), realeval, t(1:nvars), t(1:nvars) }.
>loop #root = 1 to nvars.
>compute ranks = rnkorder(evals(#root,:)).
>loop #col = 1 to ndatsets.
>do if (ranks(1,#col) = num).
>compute results(#root,4) = evals(#root,#col).
>break.
>end if.
>end loop.
>end loop.
>compute results(:,3) = rsum(evals) / ndatsets.
>
>print /title="PARALLEL ANALYSIS:".
>do if (kind = 1 and randtype = 1).
>print /title="Principal Components & Random Normal Data Generation".
>else if (kind = 1 and randtype = 2).
>print /title="Principal Components & Raw Data Permutation".
>else if (kind = 2 and randtype = 1).
>print /title="PAF/Common Factor Analysis & Random Normal Data Generation".
>else if (kind = 2 and randtype = 2).
>print /title="PAF/Common Factor Analysis & Raw Data Permutation".
>end if.
>compute specifs = {ncases; nvars; ndatsets; percent}.
>print specifs /title="Specifications for this Run:"
>  /rlabels="Ncases" "Nvars" "Ndatsets" "Percent".
>print results
>  /title="Raw Data Eigenvalues, & Mean & Percentile Random Data Eigenvalues"
>  /clabels="Root" "Raw Data" "Means" "Prcntyle"  /format "f12.6".
>
>do if   (kind = 2).
>print / space = 1.
>print /title="Warning: Parallel analyses of adjusted correlation matrices".
>print /title="eg, with SMCs on the diagonal, tend to indicate more factors".
>print /title="than warranted (Buja, A., & Eyuboglu, N., 1992, Remarks on
>parallel".
>print /title="analysis. Multivariate Behavioral Research, 27, 509-540.).".
>print /title="The eigenvalues for trivial, negligible factors in the real".
>print /title="data commonly surpass corresponding random data eigenvalues".
>print /title="for the same roots. The eigenvalues from parallel analyses".
>print /title="can be used to determine the real data eigenvalues that are".
>print /title="beyond chance, but additional procedures should then be used".
>print /title="to trim trivial factors.".
>print / space = 2.
>print /title="Principal components eigenvalues are often used to determine".
>print /title="the number of common factors. This is the default in most".
>print /title="statistical software packages, and it is the primary practice".
>print /title="in the literature. It is also the method used by many factor".
>print /title="analysis experts, including Cattell, who often examined".
>print /title="principal components eigenvalues in his scree plots to
>determine".
>print /title="the number of common factors. But others believe this common".
>print /title="practice is wrong. Principal components eigenvalues are based".
>print /title="on all of the variance in correlation matrices, including both".
>print /title="the variance that is shared among variables and the variances".
>print /title="that are unique to the variables. In contrast, principal".
>print /title="axis eigenvalues are based solely on the shared variance".
>print /title="among the variables. The two procedures are qualitatively".
>print /title="different. Some therefore claim that the eigenvalues from one".
>print /title="extraction method should not be used to determine".
>print /title="the number of factors for the other extraction method.".
>print /title="The issue remains neglected and unsettled.".
>end if.
>
>compute root      = results(:,1).
>compute rawdata = results(:,2).
>compute percntyl = results(:,4).
>
>save results /outfile=* / var=root rawdata means percntyl .
>
>end matrix.
>
>* plots the eigenvalues, by root, for the real/raw data and for the
>random data;
>   This command works in SPSS 12, but not in all earlier versions.
>TSPLOT VARIABLES= rawdata means percntyl /ID= root /NOLOG.
>
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=====================
To manage your subscription to SPSSX-L, send a message to
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command. To leave the list, send the command
SIGNOFF SPSSX-L
For a list of commands to manage subscriptions, send the command
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