Hi
One might expect (hope) these procedures would have same results when GLMM has normal with identity link and some settings.
NOT SO.
Design. this is complex as want to test limits, between factors are very unbalanced subject variable has n = 87
Factor 1, between: betwennrepeat, 2 level
Factor 2, between: Nlevels, 4 levels
Factor 3, repeated: analysis, 5 levels
Nearest I can get is below with following syntax
GLM repeated
GLM raw_f lgt_f z_f FlogitVC_f FprobitVC_f BY bewennrepeat Nlevels
/WSFACTOR=analysis 5 Repeated
/CONTRAST(Nlevels)=Repeated
/METHOD=SSTYPE(3)
/EMMEANS=TABLES(OVERALL)
/EMMEANS=TABLES(bewennrepeat)
/EMMEANS=TABLES(Nlevels)
/EMMEANS=TABLES(analysis)
/EMMEANS=TABLES(bewennrepeat*Nlevels)
/EMMEANS=TABLES(bewennrepeat*analysis)
/EMMEANS=TABLES(Nlevels*analysis)
/EMMEANS=TABLES(bewennrepeat*Nlevels*analysis)
/PRINT=DESCRIPTIVE ETASQ HOMOGENEITY
/CRITERIA=ALPHA(.05)
/WSDESIGN=analysis
/DESIGN=bewennrepeat Nlevels bewennrepeat*Nlevels.
GLMM
*Generalized Linear Mixed Models.Key settings in bold
GENLINMIXED
/DATA_STRUCTURE SUBJECTS=id REPEATED_MEASURES=analysis COVARIANCE_TYPE=UNSTRUCTURED
/FIELDS TARGET=trans1 TRIALS=NONE OFFSET=NONE
/TARGET_OPTIONS DISTRIBUTION=NORMAL LINK=IDENTITY
/FIXED EFFECTS=analysis bewennrepeat Nlevels analysis*bewennrepeat analysis*Nlevels bewennrepeat*Nlevels analysis*bewennrepeat*Nlevels USE_INTERCEPT=TRUE
/BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=SATTERTHWAITE COVB=MODEL PCONVERGE=0.000001(ABSOLUTE) SCORING=0 SINGULAR=0.000000000001
/EMMEANS TABLES=analysis COMPARE=analysis CONTRAST=PAIRWISE
/EMMEANS TABLES=bewennrepeat CONTRAST=NONE
/EMMEANS TABLES=Nlevels COMPARE=Nlevels CONTRAST=PAIRWISE
/EMMEANS TABLES=analysis*bewennrepeat COMPARE=analysis CONTRAST=PAIRWISE
/EMMEANS TABLES=analysis*Nlevels COMPARE=analysis CONTRAST=PAIRWISE
/EMMEANS TABLES=bewennrepeat*Nlevels COMPARE=bewennrepeat CONTRAST=PAIRWISE
/EMMEANS TABLES=analysis*bewennrepeat*Nlevels COMPARE=analysis CONTRAST=PAIRWISE
/EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD.
Results.
For GLM repeated have only included Roy’s largest root,as this is nearest to GLMM
Note on df
For between factors there are 7 =(41 for Nlevels) + (21 for bewennrepeat) +1 for grand mean
For GGLMM: df2 = 80 for all F tests
For repeated Roy’s: between factors still have df2 = 80
Repeated factors have lower df,as the related measures df are also taken into account
Comparison
The results are identical for between factors,
BUT slightly different for repeated factor
WHY?, WHICH RESULTS are to be recommended?
best
Diana
________________________________________
Professor Diana Kornbrot Work University of Hertfordshire College Lane, Hatfield, Hertfordshire AL10 9AB, UK +44 (0) 170 728 4626 [hidden email] http://dianakornbrot.wordpress.com/ http://go.herts.ac.uk/Diana_Kornbrot skype: kornbrotme Home 19 Elmhurst Avenue London N2 0LT, UK +44 (0) 208 444 2081 
Isn’t one difference that the genlinmixed model uses an unstructured cov matrix while the glm model uses the standard repeated measures assumption of compound
symmetry? What happens if you impose a compound symmetry cov matrix on the genlinmixed model? Gene Maguin From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Kornbrot, Diana Hi One might expect (hope) these procedures would have same results when GLMM has normal with identity link and some settings. NOT SO. Design. this is complex as want to test limits, between factors are very unbalanced subject variable has n = 87 Factor 1, between: betwennrepeat, 2 level Factor 2, between: Nlevels, 4 levels Factor 3, repeated: analysis, 5 levels Nearest I can get is below with following syntax GLM repeated GLM raw_f lgt_f z_f FlogitVC_f FprobitVC_f BY bewennrepeat Nlevels /WSFACTOR=analysis 5 Repeated /CONTRAST(Nlevels)=Repeated /METHOD=SSTYPE(3) /EMMEANS=TABLES(OVERALL) /EMMEANS=TABLES(bewennrepeat) /EMMEANS=TABLES(Nlevels) /EMMEANS=TABLES(analysis) /EMMEANS=TABLES(bewennrepeat*Nlevels) /EMMEANS=TABLES(bewennrepeat*analysis) /EMMEANS=TABLES(Nlevels*analysis) /EMMEANS=TABLES(bewennrepeat*Nlevels*analysis) /PRINT=DESCRIPTIVE ETASQ HOMOGENEITY /CRITERIA=ALPHA(.05) /WSDESIGN=analysis /DESIGN=bewennrepeat Nlevels bewennrepeat*Nlevels. GLMM *Generalized Linear Mixed Models.Key settings in bold GENLINMIXED /DATA_STRUCTURE SUBJECTS=id REPEATED_MEASURES=analysis COVARIANCE_TYPE=UNSTRUCTURED /FIELDS TARGET=trans1 TRIALS=NONE OFFSET=NONE /TARGET_OPTIONS DISTRIBUTION=NORMAL LINK=IDENTITY /FIXED EFFECTS=analysis bewennrepeat Nlevels analysis*bewennrepeat analysis*Nlevels bewennrepeat*Nlevels analysis*bewennrepeat*Nlevels USE_INTERCEPT=TRUE /BUILD_OPTIONS TARGET_CATEGORY_ORDER=ASCENDING INPUTS_CATEGORY_ORDER=ASCENDING MAX_ITERATIONS=100 CONFIDENCE_LEVEL=95 DF_METHOD=SATTERTHWAITE COVB=MODEL PCONVERGE=0.000001(ABSOLUTE)
SCORING=0 SINGULAR=0.000000000001 /EMMEANS TABLES=analysis COMPARE=analysis CONTRAST=PAIRWISE /EMMEANS TABLES=bewennrepeat CONTRAST=NONE /EMMEANS TABLES=Nlevels COMPARE=Nlevels CONTRAST=PAIRWISE /EMMEANS TABLES=analysis*bewennrepeat COMPARE=analysis CONTRAST=PAIRWISE /EMMEANS TABLES=analysis*Nlevels COMPARE=analysis CONTRAST=PAIRWISE /EMMEANS TABLES=bewennrepeat*Nlevels COMPARE=bewennrepeat CONTRAST=PAIRWISE /EMMEANS TABLES=analysis*bewennrepeat*Nlevels COMPARE=analysis CONTRAST=PAIRWISE /EMMEANS_OPTIONS SCALE=ORIGINAL PADJUST=LSD. Results. For GLM repeated have only included Roy’s largest root,as this is nearest to GLMM Note on df For between factors there are 7 =(41 for Nlevels) + (21 for bewennrepeat) +1 for grand mean For GGLMM: df2 = 80 for all F tests For repeated Roy’s: between factors still have df2 = 80 Repeated factors have lower df,as the related measures df are also taken into account Comparison
The results are identical for between factors, BUT slightly different for repeated factor WHY?,
WHICH RESULTS are to be recommended? best Diana ________________________________________ ===================== To manage your subscription to SPSSXL, send a message to
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I was wondering about that too, Gene. Diana, if that change doesn't get you all the way there, I'd also try changing DF to RESIDUAL.

Bruce Weaver bweaver@lakeheadu.ca http://sites.google.com/a/lakeheadu.ca/bweaver/ "When all else fails, RTFM." NOTE: My Hotmail account is not monitored regularly. To send me an email, please use the address shown above. 
Compound symmetry is much less like multivariate than unstructured as it gives very large df for analysis (repeated) and its interactions
Residuals is definitely wrong it gives df as total N all dos which is MUCH too large.
Here is my problem
I want to recommend a SIMPLE CONSISTENT approach to psychologists that applies to both normal, identify AND binomial, logit analyses.
For normal, the repeated option is very well known and used in zillions of studies. As Thom says GLMM unstructured may well be slightly better, and is what I intend recommending.
BUT I would dearly love to understand why I am getting different answers
Another problem with GLMM is that it often gives up immediately as matrices are problematic  unlike General Estimating Equations, GEE that gives a prompt answer whatever.
GEE is different again and gives chisquare inferential test statistic.
Again I would dearly love to know why GEE gives chisquare not F
NB GEE also gives too large df2 for repeated predictor
When analyses give too large df2 the p value will be lower thus increasing chance of wrongly rejecting the nuke
All very puzzling  but definitely not going for overlarge df2
thanks for help
best
Diana
________________________________________
Professor Diana Kornbrot Work University of Hertfordshire College Lane, Hatfield, Hertfordshire AL10 9AB, UK +44 (0) 170 728 4626 [hidden email] http://dianakornbrot.wordpress.com/ http://go.herts.ac.uk/Diana_Kornbrot skype: kornbrotme Home 19 Elmhurst Avenue London N2 0LT, UK +44 (0) 208 444 2081 
In reply to this post by Kornbrot, Diana
Diana, I haven't had time to read your post carefully, but a quick glance at your syntax looks like you employed a mixed (within subjects and between subjects factors) ANOVA. As others mentioned, the tests generated that include the repeated measures factor assume Sphericity from an ANOVA. For a true comparison between a MANOVA and a linear mixed model, you should employ a MANOVA, which assumes an unstructured Sigma matrix. I would also suggest you use the MIXED procedure to start since you are assuming the DVs are conditionally MVN. What you are trying to accomplish has been done before. The linear mixed modeling procedure in SPSS, and other software for that matter, can accomplish just about everything a general linear modeling procedure can do and more (e.g., better handle unbalanced designs, utilize more restrictive covariance structures). Ryan On Wed, Mar 1, 2017 at 4:56 AM, Kornbrot, Diana <[hidden email]> wrote:

Diana,
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