GLM repeated measures concern/question

[eyeman03]

Hola,

We have a potential concern with analyzing our data:

Quick Backgroud:  Research Design is 2x2 Mixed Model involving the effects of central blind spots on Visual Search.  The subjects are searching for a target (O) embedded among distracters (C’s).  The set sizes we have used are 1, 8, and 32 (1 stimuli on screen =1set size; 8 stimuli on screen= 8 set size and so forth).

Problem: The potential concern is the set sizes (predictor variables) are NON-LINEAR in spacing (unequal). Our plan is to use GLM Repeated Measures to analyze the data, but we’re wondering if this GLM model can be used with the set size non-linearity…………another words does the GLM model implicitly assume linear spacing with its analyses?

If yes, what are the potential solutions?

t/y

Dave

[Maguin, Eugene]

Dave,
I recall that the GLM documentation says that the default model for repeated measures is a polynomial. I think a first question I would have--especially since I know nothing about this area, is what shape of curve the data really do produce. So, are times for 8 stimuli 0.125 of those for 1 stimulus and those for 32 stimuli .03125 of those for 1 stimulus? Or, are they something else. Spss has a curve fit procedure, called that, I think, and that might be helpful here. Also, when you run GLM, you can shift the repeated measures contrast from polynomial to another alternative. You have three data points, 1, 8, 32, and those would define a cubic function. But you could have a power function of 2: 0, 3, 5.

By the way, you describe a 2 by 2 design. Just to be sure, 2 between factors and 2 within factors, right? I have assumed that stimuli number is a within factor, is that correct?

Gene Maguin

-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of eyeman03
Sent: Tuesday, December 27, 2011 5:30 PM
To: [hidden email]
Subject: GLM repeated measures concern/question

Hola,

We have a potential concern with analyzing our data:

*Quick Backgroud:*  Research Design is 2x2 Mixed Model involving the effects
of central blind spots on Visual Search.  The subjects are searching for a
target (O) embedded among distracters (Cs).  The set sizes we have used are
1, 8, and 32 (1 stimuli on screen =1set size; 8 stimuli on screen= 8 set
size and so forth).

*Problem*: The potential concern is the set sizes (predictor variables) are
NON-LINEAR in spacing (unequal). Our plan is to use GLM Repeated Measures to
analyze the data, but were wondering if this GLM model can be used with the
set size non-linearityanother words does the GLM model implicitly assume
linear spacing with its analyses?

If yes, what are the potential solutions?

t/y

Dave


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[Rich Ulrich]

Do you really  have reason to throw out the assumption of
equal spacing?

The set sizes (1,8,32) are obviously non-linear in spacing
when you consider simple counts, but the dimension that
matters is the dimension of impact on the measured outcome. 
Thus, "equal intervals" for a drug dosage will sometimes be
in log-units, rather than raw units - as a simple example.

It is my experience that experienced researchers pick
"unequal intervals" in time, in setting up repeated measures,
in such a fashion to approximate equal intervals in outcome. 
I've usually had "time" for the repeated measures I've tested.
And it is a mistake not to take advantage of the intelligent
design when doing the analyses.

It is my observation that nominally unequal numeric intervals,
whether they are time or design elements like yours, are
usually (similarly) constructed with the intent of producing
equal intervals of outcome/ response.  So you might try the
simple analysis, assuming equal intervals, and see if fits.

--
Rich Ulrich

> Date: Tue, 27 Dec 2011 14:29:30 -0800

> From: [hidden email]
> Subject: GLM repeated measures concern/question
> To: [hidden email]
>
> Hola,
>
> We have a potential concern with analyzing our data:
>
> *Quick Backgroud:* Research Design is 2x2 Mixed Model involving the effects
> of central blind spots on Visual Search. The subjects are searching for a
> target (O) embedded among distracters (C’s). The set sizes we have used are
> 1, 8, and 32 (1 stimuli on screen =1set size; 8 stimuli on screen= 8 set
> size and so forth).
>
> *Problem*: The potential concern is the set sizes (predictor variables) are
> NON-LINEAR in spacing (unequal). Our plan is to use GLM Repeated Measures to
> analyze the data, but we’re wondering if this GLM model can be used with the
> set size non-linearity…………another words does the GLM model implicitly assume
> linear spacing with its analyses?
>
> If yes, what are the potential solutions?

[snip]

[Kornbrot, Diana]

This study follows on well-established work of Sternberg & later Triesman, Townsend where the set size is numeric and MATTERS
It is now very well known, & should be in psych101, that reaction time is a linear function of set size and the slope matters because changing conditions may change intercept but not slope or change both slope and intercept. There are strong theoretical implications concerned with parallel and serial processing and the location of processing limitations.

It may well be that log transformation will lead to better fit, but this is NOT a good reason to drop the numerical information in set size.
Test whether log or raw set size is better linear predictor.
Thrwoing out the linear assumption is ESSENTIAL
Best
Diana


On 28/12/2011 20:01, "Rich Ulrich" <rich-ulrich@...> wrote:

Do you really  have reason to throw out the assumption of
equal spacing?

The set sizes (1,8,32) are obviously non-linear in spacing
when you consider simple counts, but the dimension that
matters is the dimension of impact on the measured outcome.  
Thus, "equal intervals" for a drug dosage will sometimes be
in log-units, rather than raw units - as a simple example.

It is my experience that experienced researchers pick
"unequal intervals" in time, in setting up repeated measures,
in such a fashion to approximate equal intervals in outcome.  
I've usually had "time" for the repeated measures I've tested.
And it is a mistake not to take advantage of the intelligent
design when doing the analyses.

It is my observation that nominally unequal numeric intervals,
whether they are time or design elements like yours, are
usually (similarly) constructed with the intent of producing
equal intervals of outcome/ response.  So you might try the
simple analysis, assuming equal intervals, and see if fits.

[Rich Ulrich]

Diana seems to know something about the subject at hand. 
But I have to offer an obvious suggestion - I think the word needed
was "monotonic" and not "linear", in "reaction time is a linear function
of set size...."   -- Surely the difference between sets=1 vs 2  is much
larger than the difference between sets= 31 vs. 32  -- which is what
is implied by "linear".  My intuition says, That is not reasonable.  My
experience says that going against that intuition results in non-linear
relations that introduce artifacts in the testing and complications in the
interpretations.

I would agree that "throwing out the linear assumption is ESSENTIAL" if
she were referring to the spacing of (1,8,32).  That is not reasonable.

But she says, "Test whether log or raw set size is better linear
predictor"  and I presume that "raw" refers to (1,8,32). 

As to logs:  The log-spacing of (1,8,32) can be taken from the
respective powers of 2:  (0,3,5).  That is prettly close to linear, so
it is not unreasonable to use it, if you do not have a program that
affords the luxury of using the slightly unequal log-spacing (0,3,5). 
It is also justified by the experimenters' (presumable) intention of
providing equal spacing in their design. 

(Of course, the log is not the only power transformation that might
be considered for spacing.)

--
Rich Ulrich

---

Date: Thu, 29 Dec 2011 14:23:09 +0000
From: [hidden email]
Subject: {!!! SPAM ???} Re: GLM repeated measures concern/question
To: [hidden email]

This study follows on well-established work of Sternberg & later Triesman, Townsend where the set size is numeric and MATTERS
It is now very well known, & should be in psych101, that reaction time is a linear function of set size and the slope matters because changing conditions may change intercept but not slope or change both slope and intercept. There are strong theoretical implications concerned with parallel and serial processing and the location of processing limitations.

It may well be that log transformation will lead to better fit, but this is NOT a good reason to drop the numerical information in set size.
Test whether log or raw set size is better linear predictor.
Thrwoing out the linear assumption is ESSENTIAL
Best
Diana


On 28/12/2011 20:01, "Rich Ulrich" <rich-ulrich@...> wrote:

Do you really  have reason to throw out the assumption of
equal spacing?

The set sizes (1,8,32) are obviously non-linear in spacing
when you consider simple counts, but the dimension that
matters is the dimension of impact on the measured outcome.  
Thus, "equal intervals" for a drug dosage will sometimes be
in log-units, rather than raw units - as a simple example.

It is my experience that experienced researchers pick
"unequal intervals" in time, in setting up repeated measures,
in such a fashion to approximate equal intervals in outcome.  
I've usually had "time" for the repeated measures I've tested.
And it is a mistake not to take advantage of the intelligent
design when doing the analyses.

It is my observation that nominally unequal numeric intervals,
whether they are time or design elements like yours, are
usually (similarly) constructed with the intent of producing
equal intervals of outcome/ response.  So you might try the
simple analysis, assuming equal intervals, and see if fits.

[Kornbrot, Diana]

I do indeed have CONTENT knowledge
The key descriptive parameters for any visual search task are the slope and intercept of the function of reaction time (DV) on set size (IV).
Typically, as in this case,researcher are comparing these parameters in different conditions. So the inferential problem is to know whether there are reliable differences in slope between tasks/conditions. For example condition 1 may have the target further out to the side than condition 2 whaere targets are more central.
In order to get the work published in a ‘respectable’ psych journal, as questioner well knows, he will need to get the slopes and intercepts and perform inferential tests. So what he needs to know is how to do this in SPSS.
My suggestions enable him to do just that, in what I have found the simplest way
(There IS a syntax option in GLM polynomial that allows one to specify the actual set size spacing
In this case: GLM RESPONSE BY STIMULUS /CONTRAST(STIMULUS) =POLYNOMIAL(1,8,32)
But suspect means plot will still be equally spaced)
__
SPSS command synatx referenc odf includse: POLYNOMIAL Polynomial contrasts. This setting is the default for within-subjects factors.
The first degree of freedom contains the linear effect across the levels of the
factor, the second degree of freedom contains the quadratic effect, and so on. In
a balanced design, polynomial contrasts are orthogonal. By default, the levels
are assumed to be equally spaced; you can specify unequal spacing by entering a
metric consisting of one integer for each level of the factor in parentheses after
the keyword POLYNOMIAL. (All metrics that are specified cannot be equal; thus,
(1, 1, . . . 1) is not valid.) An example is as follows:
GLM RESPONSE BY STIMULUS /CONTRAST(STIMULUS) =
POLYNOMIAL(1,2,4).
Suppose that factor STIMULUS has three levels. The specified contrast indicates
that the three levels of STIMULUS are actually in the proportion 1:2:4. The
default metric is always (1, 2, . . . k), where k levels are involved. Only the
relative differences between the terms of the metric matter. (1, 2, 4) is the same
metric as (2, 3, 5) or (20, 30, 50) because, in each instance, the difference between
the second and third numbers is twice the difference between the first and second

---

Your suggestions are interesting but they change the nature of the question. (NB taking logs give spacing of 0,3, 5 which STILL isn’t equally spaced.)
Sternberg, S. (1969). The discovery of processing stages: Extensions of Donders' method. Acta Psychologica, Amsterdam, 30, 276-315.
Sternberg, S. (2001). Separate modifiability, mental modules, and the use of pure and composite measures to reveal them. Acta Psychologica, 106(1-2), 147-246.
Townsend, J. T. (1972). Some results concerning the identifiability of parallel and serial processes. British Journal of Mathematical and Statistical Psychology, 25, 168-197.
Townsend, J. T. (1992). On the proper scales for reaction time. In H.-G. Geissler, S. W. Link & J. T. Townsend (Eds.), Cognition, information processing and psychophysics:  basic issues (Vol. Lawrence Erlbaum Associates). Hove and London.
Treisman, A. (1991). Search, similarity, and integration of features between and within dimensions. Journal of Experimental Psychology: Human Perception and Performance676, 17, 652-676.

Sternberg’s key Ms have more than 900citations, Treisman’s more than 500, Townsend’s more than 2000.  This is a well researched area & the key issues are KNOWN
Best
Diana





On 31/12/2011 22:14, "Rich Ulrich" <rich-ulrich@...> wrote:

Diana seems to know something about the subject at hand.  
But I have to offer an obvious suggestion - I think the word needed
was "monotonic" and not "linear", in "reaction time is a linear function
of set size...."   -- Surely the difference between sets=1 vs 2  is much
larger than the difference between sets= 31 vs. 32  -- which is what
is implied by "linear".  My intuition says, That is not reasonable.  My
experience says that going against that intuition results in non-linear
relations that introduce artifacts in the testing and complications in the
interpretations.

I would agree that "throwing out the linear assumption is ESSENTIAL" if
she were referring to the spacing of (1,8,32).  That is not reasonable.

But she says, "Test whether log or raw set size is better linear
predictor"  and I presume that "raw" refers to (1,8,32).  

As to logs:  The log-spacing of (1,8,32) can be taken from the
respective powers of 2:  (0,3,5).  That is prettly close to linear, so
it is not unreasonable to use it, if you do not have a program that
affords the luxury of using the slightly unequal log-spacing (0,3,5).  
It is also justified by the experimenters' (presumable) intention of
providing equal spacing in their design.  

(Of course, the log is not the only power transformation that might
be considered for spacing.)

---

Professor Diana Kornbrot
email:  d.e.kornbrot@...    
web:    http://web.me.com/kornbrot/KornbrotHome.html
Work
School of Psychology
 University of Hertfordshire
 College Lane, Hatfield, Hertfordshire AL10 9AB, UK
 voice:   [phone]
   fax:     [phone]
Home
 19 Elmhurst Avenue
 London N2 0LT, UK
    voice:   [phone]
    mobile: [phone]
   fax:      [phone]

[eyeman03]

First, thank you EVERYONE for your input.  It’s really appreciated.    

Diana,

Let me expand a little on the design.  We are studying the effects of two types of simulated central blind spots on adaptation.  The task is Visual Search with 3 set sizes (1, 8, 32)  

The research design is a mixed 2x2 ANOVA with adaptation as the within effect, and blind spot type as the between effect.  The statistical tool is GLM Repeated measures.          

As you know, our problem is the unequal spacing of the set sizes, and whether or not SPSS intrinsically recognizes the spacing as equal.   You answered that question; yes, it does.

We’ve come to learn we can use the CONTRAST option for set size.  However, If we add set size as a covariate (as you suggested), then SPSS will not recognize it as a factor (CONTRAST).

But if set size is added as a Between-Subject factor , we can now write syntax as a CONTRAST:  /CONTRAST(Set size)=Polynomial (1, 8, 32).

We ran two different syntaxes to compare their CONTRAST results. : /CONTRAST(Set size)=Polynomial (1, 8, 32) vs /CONTRAST(Set size)=Polynomial.  

With the former syntax, only the linear contrast was significant.   With the latter syntax, both the linear and quadratic contrast were highly significant.  

So the output analysis with stating vs not stating the set sizes made a significant difference.  
We wanted to ask you opinion if this syntax solution: CONTRAST POLYOMIALS (1, 8, 32) seems correct for our analysis

Dave

PS……I can send the Output data if anyone is interested.

[Amanda Brouwer]

I am trying to determine whether my data are missing at random using Little's MCAR test.

When I run the test via the missing values analysis, I also get a notification that "The EM algorithm failed to converge in 25 iterations".

When I increase the iterations (i.e., 50,75,100), the Chi-square value for Little's MCAR test also changes. In some cases from significant to non-significant.

I'm wondering if anyone could provide me with more information about the relationship between these and whether or not the failed convergence on the EM algorithm makes the Little's MCAR test invalid.

Thanks!

Amanda

Amanda Brouwer

Patient Advocacy & Research Lab
Pearse Hall, B53
University of Wisconsin - Milwaukee
P.O. Box 413
Milwaukee, WI 53201
[hidden email]

---

From: "eyeman03" <[hidden email]>
To: [hidden email]
Sent: Monday, January 9, 2012 8:14:08 AM
Subject: Re: GLM repeated measures concern/question

First, thank you* EVERYONE* for your input.  It’s really appreciated.

Diana,

Let me expand a little on the design.  We are studying the effects of two
types of simulated central blind spots on adaptation.  The task is Visual
Search with 3 set sizes (1, 8, 32)

The research design is a mixed 2x2 ANOVA with adaptation as the within
effect, and blind spot type as the between effect.  The statistical tool is
GLM Repeated measures.

As you know, our problem is the unequal spacing of the set sizes, and
whether or not SPSS intrinsically recognizes the spacing as equal.   You
answered that question; yes, it does.

We’ve come to learn we can use the CONTRAST option for set size.  However,
If we add set size as a covariate (as you suggested), then SPSS will not
recognize it as a factor (CONTRAST).

But if set size is added as a Between-Subject factor , we can now write
syntax as a CONTRAST:  /CONTRAST(Set size)=Polynomial (1, 8, 32).

We ran two different syntaxes to compare their CONTRAST results. :
*/CONTRAST(Set size)=Polynomial (1, 8, 32)* vs /CONTRAST(Set
size)=Polynomial.

With the *former syntax*, only the linear contrast was significant.   With
the latter syntax, both the linear and quadratic contrast were highly
significant.

So the output analysis with stating vs not stating the set sizes made a
significant difference.
We wanted to ask you opinion if this syntax solution: CONTRAST POLYOMIALS
(1, 8, 32) seems correct for our analysis

Dave

PS……I can send the Output data if anyone is interested.


--
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[Rich Ulrich]

If it never converged, then it never reached a stable answer. 

In general, that would say that the answer is not valid.

Also in general -- for results that are based on convergence after
iterations, there might be some clue in the progressive iterations
by single steps.  Big fluctuations suggest a problem with the problem.

--
Rich Ulrich

---

Date: Mon, 9 Jan 2012 20:14:24 -0600
From: [hidden email]
Subject: Little's MCAR Test and EM algorithms
To: [hidden email]

I am trying to determine whether my data are missing at random using Little's MCAR test.

When I run the test via the missing values analysis, I also get a notification that "The EM algorithm failed to converge in 25 iterations".

When I increase the iterations (i.e., 50,75,100), the Chi-square value for Little's MCAR test also changes. In some cases from significant to non-significant.

I'm wondering if anyone could provide me with more information about the relationship between these and whether or not the failed convergence on the EM algorithm makes the Little's MCAR test invalid.

Thanks!

[snip, sig; and previous post, not relevant]

[Garry Gelade]

Dave

I think your CONTRASTS Polynomial(1 8 32) is correct.  CONTRASTS Poylnomial without the set sizes will assume equal spacing of your IV (e.g. set sizes of 1,2,3 or 1, 11, 21).

One way to check would be to request SPSS to print the contrast coefficients (Lmatrix).  For testing the linear trend, the spacing of (difference between) successive contrast coefficients should be in the same ratio as the spacing of your set sizes, i.e. 7:24.

Regards
Garry Gelade
Business Analytic Ltd


-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of eyeman03
Sent: 09 January 2012 14:14
To: [hidden email]
Subject: Re: GLM repeated measures concern/question

First, thank you* EVERYONE* for your input.  It’s really appreciated.

Diana,

Let me expand a little on the design.  We are studying the effects of two
types of simulated central blind spots on adaptation.  The task is Visual
Search with 3 set sizes (1, 8, 32)

The research design is a mixed 2x2 ANOVA with adaptation as the within
effect, and blind spot type as the between effect.  The statistical tool is
GLM Repeated measures.

As you know, our problem is the unequal spacing of the set sizes, and
whether or not SPSS intrinsically recognizes the spacing as equal.   You
answered that question; yes, it does.

We’ve come to learn we can use the CONTRAST option for set size.  However,
If we add set size as a covariate (as you suggested), then SPSS will not
recognize it as a factor (CONTRAST).

But if set size is added as a Between-Subject factor , we can now write
syntax as a CONTRAST:  /CONTRAST(Set size)=Polynomial (1, 8, 32).

We ran two different syntaxes to compare their CONTRAST results. :
*/CONTRAST(Set size)=Polynomial (1, 8, 32)* vs /CONTRAST(Set
size)=Polynomial.

With the *former syntax*, only the linear contrast was significant.   With
the latter syntax, both the linear and quadratic contrast were highly
significant.

So the output analysis with stating vs not stating the set sizes made a
significant difference.
We wanted to ask you opinion if this syntax solution: CONTRAST POLYOMIALS
(1, 8, 32) seems correct for our analysis

Dave

PS……I can send the Output data if anyone is interested.


--
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[eyeman03]

Thanks Garry for your reply,

Sorry for my ignorance (or lack of SPSS knowledge), but how would I  " request SPSS to print the contrast coefficients (Lmatrix)"?

Dave

[David Marso]

GLM dependent varlist
             [BY factor list [WITH covariate list]]
 [/WSFACTOR=name levels[{DEVIATION[(refcat)]       }] name...
                        {SIMPLE [(refcat)]         }
                        {DIFFERENCE                }
                        {HELMERT                   }
                        {REPEATED                  }
                        {POLYNOMIAL [({1,2,3...})]**}
                        {             {metric  }    }
                        {SPECIAL (matrix)           }
 [/MEASURE=newname newname...]
 [/WSDESIGN=effect effect...]†
 [/RANDOM=factor factor...]
 [/REGWGT=varname]
 [/METHOD=SSTYPE({1  })]
                 {2  }
                 {3**}
                 {4  }
 [/INTERCEPT=[INCLUDE**] [EXCLUDE] ]
 [/MISSING=[INCLUDE] [EXCLUDE**]]
 [/CRITERIA=[EPS({1E-8**})][ALPHA({0.05**}) ]
                 {a     }         {a     }
 [/PRINT  = [DESCRIPTIVE] [HOMOGENEITY] [PARAMETER][ETASQ] [RSSCP]
            [GEF] [LOF] [OPOWER] [TEST [([SSCP] [LMATRIX] [MMATRIX])] ]
 [/PLOT=[SPREADLEVEL] [RESIDUALS]
        [PROFILE (factor factor*factor factor*factor*factor ...) ]
 [/TEST=effect VS {linear combination [DF(df)]}]
                  {value DF (df)              }
 [/LMATRIX={["label"] effect list effect list ...;...}]
           {["label"] effect list effect list ...    }
           {["label"] ALL list; ALL...               }
           {["label"] ALL list                       }
 [/CONTRAST (factor name)={DEVIATION[(refcat)]** ‡   } ]
                          {SIMPLE [(refcat)]         }
                          {DIFFERENCE                }
                          {HELMERT                   }
                          {REPEATED                  }
                          {POLYNOMIAL  [({1,2,3...})]}
                          {              {metric  }  }
                          {SPECIAL (matrix)          }
 [/MMATRIX= {["label"] depvar value depvar value ...;["label"]...} ]
            {["label"] depvar value depvar value ...             }
            {["label"] ALL list; ["label"] ...                   }
            {["label"] ALL list                                  }
 [/KMATRIX= {list of numbers    } ]
            {list of numbers;...}
 [/POSTHOC =effect effect...([SNK] [TUKEY] [BTUKEY][DUNCAN]
            [SCHEFFE] [DUNNETT(refcat)] [DUNNETTL(refcat)]
            [DUNNETTR(refcat)] [BONFERRONI] [LSD] [SIDAK]
            [GT2] [GABRIEL] [FREGW] [QREGW]  [T2] [T3] [GH][C]
            [WALLER ({100** })]]
                     {kratio}
                 [VS effect]
 [/EMMEANS=TABLES({OVERALL               }) ]
                  {factor                }
                  {factor*factor...      }
                  {wsfactor              }
                  {wsfactor*wsfactor ... }
                  {factor*...wsfactor*...}
                  WITH (covariate=MEAN covariate=MEAN)
                  COMPARE ADJ (LSD)
                              (BONFERRONI)
                              (SIDAK)
 [/SAVE=[tempvar [(list of names)]] [tempvar [(list of names)]]...]
 [/OUTFILE=[{COVB (file)}]
            {CORB (file)}
 [/DESIGN={[INTERCEPT...]    }]
          {[effect effect...]}
† WSDESIGN uses the same specification as DESIGN,
  with only within-subjects factors.
‡ DEVIATION is the default for between-subjects factors
  while POLYNOMIAL is the default for within-subjects factors.
** Default if subcommand or keyword is omitted.
Temporary variables (tempvar) are:
PRED, WPRED, RESID, WRESID, DRESID, ZRESID,
SRESID, SEPRED, COOK, LEVER

eyeman03 wrote

Thanks Garry for your reply,

Sorry for my ignorance (or lack of SPSS knowledge), but how would I  " request SPSS to print the contrast coefficients (Lmatrix)"?

Dave

[Garry Gelade]

Dave

On the GLM Univariate (UNIANOVA) screen, click Options then tick Contrast
Coefficient Matrix. For Generalized Linear Model, then you can find the same
option on the Statistics tab.  I can't see a menu option on the GLM Repeated
measures screens, but you can include the statement /PRINT LMATRIX in
syntax.

Garry


-----Original Message-----
From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
eyeman03
Sent: 11 January 2012 16:58
To: [hidden email]
Subject: Re: GLM repeated measures concern/question

Thanks Garry for your reply,

Sorry for my ignorance (or lack of SPSS knowledge), but how would I  "
*request SPSS to print the contrast coefficients (Lmatrix)"?*

Dave

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[David Marso]

I was highlighting the syntax options for the OP.


On Wed, Jan 11, 2012 at 1:33 PM, Garry Gelade [via SPSSX Discussion]
<[hidden email]> wrote:

> Dave
>
> On the GLM Univariate (UNIANOVA) screen, click Options then tick Contrast
> Coefficient Matrix. For Generalized Linear Model, then you can find the same
> option on the Statistics tab.  I can't see a menu option on the GLM Repeated
> measures screens, but you can include the statement /PRINT LMATRIX in
> syntax.
>
> Garry
>
>
> -----Original Message-----
> From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of
> eyeman03
> Sent: 11 January 2012 16:58
> To: [hidden email]
> Subject: Re: GLM repeated measures concern/question
>
> Thanks Garry for your reply,
>
> Sorry for my ignorance (or lack of SPSS knowledge), but how would I  "
> *request SPSS to print the contrast coefficients (Lmatrix)"?*
>
> Dave
>
> --
> View this message in context:
> http://spssx-discussion.1045642.n5.nabble.com/GLM-repeated-measures-concern-
> question-tp5104022p5137359.html
> Sent from the SPSSX Discussion mailing list archive at Nabble.com.
>
> =====================
> To manage your subscription to SPSSX-L, send a message to
> [hidden email] (not to SPSSX-L), with no body text except the
> 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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>
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