Hi there! I am trying to figure out how I can analyze my data. Any help would
be greatly appreciated. The dataset includes 72 participants who answered a question with a dichotomous response (yes/no). There are two betweensubject variables (age: 2 conditions  young/old, and Variable X: conditions Up/Down,) and a within subject variable (Variable Y: 3 conditions, A, B, and C). An example of the dataset is attached as an image. <http://spssxdiscussion.1045642.n5.nabble.com/file/t341829/ExampleData.jpg> Example Data frame code for R Participant < c('A','B','C') Age < c('Young', 'Young', 'Old') VariableX < c('Up', 'Up', 'Down') ConditionA < c('Yes', 'No', 'Yes') ConditionB < c('No', 'Yes', 'No') ConditionC < c('Yes', 'Yes', 'No') ExampleData < dataframe(Participant, Age, VariableX, ConditionA, ConditionB, ConditionC) I am trying to figure out what to run in order to see if there is an interaction effect between age, Variable X and Variable Y on the binomial response of participants. I tried to run a GLMM (ominbus generalized mixed model with a binary logistic regression function) with the binary response (yes/no) across condition with predefined contrasts (Condition AConditionC, ConditionBConditionC) as a withinsubjects variables, and age (Young, old) and condition Y (Up, Down) as between subject variables in SPSS. I am not sure however if we did it correctly. I received no output data for the significance value. Any guidance on how to analyze and interpret the output would be greatly appreciated!* I am happy to analyze these data either in SPSS or in R.* Thanks!  Sent from: http://spssxdiscussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD 
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You'll have to restructure your data (with VARSTOCASES) so that there is one
row per observation of the outcome variable, like this: ID Old Up Cond Y 1 0 1 1 1 1 0 1 2 0 1 0 1 3 1 2 1 0 1 1 2 1 0 2 1 2 1 0 3 0 Notice that I have used Cond (short for condition) to describe the variable you called Y. And I have used Y to refer to the outcome variable (as is the convention). Variables Old, Up and Y are all "indicator variables" with 1=Yes and 0=No. HTH. Sarah Thomas wrote > Hi there! I am trying to figure out how I can analyze my data. Any help > would > be greatly appreciated. > > The dataset includes 72 participants who answered a question with a > dichotomous response (yes/no). There are two betweensubject variables > (age: > 2 conditions  young/old, and Variable X: conditions Up/Down,) and a > within > subject variable (Variable Y: 3 conditions, A, B, and C). An example of > the > dataset is attached as an image. > > <http://spssxdiscussion.1045642.n5.nabble.com/file/t341829/ExampleData.jpg> > > Example Data frame code for R > > Participant < c('A','B','C') > Age < c('Young', 'Young', 'Old') > VariableX < c('Up', 'Up', 'Down') > ConditionA < c('Yes', 'No', 'Yes') > ConditionB < c('No', 'Yes', 'No') > ConditionC < c('Yes', 'Yes', 'No') > ExampleData < dataframe(Participant, Age, VariableX, ConditionA, > ConditionB, ConditionC) > > I am trying to figure out what to run in order to see if there is an > interaction effect between age, Variable X and Variable Y on the binomial > response of participants. > > I tried to run a GLMM (ominbus generalized mixed model with a binary > logistic regression function) with the binary response (yes/no) across > condition with predefined contrasts (Condition AConditionC, > ConditionBConditionC) as a withinsubjects variables, and age (Young, > old) > and condition Y (Up, Down) as between subject variables in SPSS. I am not > sure however if we did it correctly. I received no output data for the > significance value. > > Any guidance on how to analyze and interpret the output would be greatly > appreciated!* I am happy to analyze these data either in SPSS or in R.* > > Thanks! > > > >  > Sent from: http://spssxdiscussion.1045642.n5.nabble.com/ > > ===================== > To manage your subscription to SPSSXL, send a message to > LISTSERV@.UGA > (not to SPSSXL), with no body text except the > command. To leave the list, send the command > SIGNOFF SPSSXL > For a list of commands to manage subscriptions, send the command > INFO REFCARD   Bruce Weaver [hidden email] 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.  Sent from: http://spssxdiscussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD

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. 
How was age originally measured? Did it come to you already coarsened?
Young = under 65 Old = 65 and older? Young = under 36 months Old = 36 months and older?  Art Kendall Social Research Consultants  Sent from: http://spssxdiscussion.1045642.n5.nabble.com/ ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD
Art Kendall
Social Research Consultants 
In reply to this post by Sarah Thomas
Hi Sarah
Am working on comparing packages for this problem, comparing SPSS and R
1st
As Bruce states get data in long form
ID Old Up Cond Y
1 0 1 1 1 1 0 1 2 0 1 0 1 3 1 2 1 0 1 1 2 1 0 2 1 2 1 0 3 0 Makes sure all variables are nominal for SPSS
Then I would be most grateful if you would send me long data in CSV or .sav
SPSS analysis
1.1.
Set preferences > output > display>output display pivots and charts. Makes output easier to read
1.2.
Set preferences > viewer> log > shown. This ensures you can share syntax with others
2.
Analysis > mixed models> generalised linear. You will get a lot of tabs
3
Data structure tab
3.1
Data structure > effects: id = subject; repeated measures = Up, Cond
3.2
Data structure > covariance = unstructured
4.
Fields and effect tab
4.1
Target = Y. It will then default to logistic because Y is nominal
4.2
Fixed: insert Old, Up, Cond and all 2 way and 3 way interactions
5 Build tab. This only affects df for F tests. KenwardRogers is currently flavour of the month, residual is conservative
6. Model options tab. Should see all main and interactions listed. Needed to get means and comparison
6.1 Tick all of them
6.2 Choses pairwise contrasts for all of them
7. RUN
SPSS output, wharf massive output, t is needed?
Model describes variables and AIC<BIC for model fit
Fixed gives the Fvalues, including the interaction  which you were requesting
Estimated means. Provided separately for all main and interaction effects with pretty pictures
Good luck!
R maybe typos in this but should be easy to correct
Make sure you have R studio
1. Launch R studio
1.1 Make sure you have all necessary packages, I suggest
library(haven # its about file loading
library(readxl)
#ensures you can read in data
library(tidyverse) #supposed to make output pretty
ibrary(lme4)
#has needed function glmer
library(emmeans) # to get means
library(dbplyr) #needed for graphs
library(rmarkdown) # for refines output, not needed
library(knitr) ) # for refines output, not needed
1.2 tenure lme4 and emmeans are ticked ob right under packages
2. Load your data from R studio file menu
2.1 import> spss> e.e. sarah.sav
browse to find it , then import
3. Tell R about your data
# Assign columns to contain categorical factor predictor variables
sarah[c(1,2,3,4,5 )] < lapply(Sarah[c(1,2,3,4,5)], factor)
#Assign contrasts
contrasts(sarah$Up) < contr.sum(2)
contrasts(sarah$Cond) < contr.sum(2)
contrasts(Sarah $Old) < contr.sum(2)
4. perform analysis, with results in object res
res < glmer(Y ~ OldUpCond + (UpCondID), data=Sarah, family=binomial(link="logit”)) #does analysis
# if correct you will get a friendly “>”. Now you need R to divulge its secrets
anova(res) # gets the F table
descr < means(res, ~ OldUpCond, data=sarah) # means and SEs
descr # outputs means and YEs
pairs (descr) #gets all pairwise comparisons
5. GET graphs  optional  may not work
emmpi(descr, ~ OldUpCond, data=Sarah)
6. Copu output to WORD and/or EXCEL
Good luck?
NOW WHAT?
SPSS and R may [probably will] give different answers
Probably differences are small
I would very much like to see them
Please send me your output
Help from SPSS community. Please
a. Who to trust? SPSS or R
What is caysbg differences?
b. Would much appreciate R code that duplicates SPSS as described above
best
Diana
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In reply to this post by Bruce Weaver
Thanks
Can be different? They always are
But ia ma veery specific in what I want to do
Here is SPSS syntax of 2 possibilities, with just 2 levels
Already know that most packages, e.g. SPSS and R, give different results for paired comparisons within 3 levels and paired comparisons with just 2 levels
Sop staying with 2 levels and being VERY specific
SPSS
GENLINMIXED
/DATA_STRUCTURE SUBJECTS=pno REPEATED_MEASURES=wiithin COVARIANCE_TYPE=UNSTRUCTURED /FIELDS TARGET=freqY TRIALS=FIELD(Nmax) OFFSET=NONE /TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT /FIXED EFFECTS=wiithin 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=wiithin COMPARE=wiithin CONTRAST=PAIRWISE /EMMEANS_OPTIONS SCALE=TRANSFORMED PADJUST=LSD. Has within factor in data structure with unstructured covariance
It looks simple but what is R code?
Have not been able to discover after many attempts
Its what I have always recommended
Or
*Generalized Linear Mixed Models.
GENLINMIXED/DATA_STRUCTURE SUBJECTS=pno /FIELDS TARGET=freqY TRIALS=FIELD(Nmax) OFFSET=NONE /TARGET_OPTIONS DISTRIBUTION=BINOMIAL LINK=LOGIT /FIXED EFFECTS=wiithin USE_INTERCEPT=TRUE /RANDOM EFFECTS=wiithin USE_INTERCEPT=FALSE SUBJECTS=pno COVARIANCE_TYPE=UNSTRUCTURED SOLUTION=FALSE /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=wiithin COMPARE=wiithin CONTRAST=PAIRWISE /EMMEANS_OPTIONS SCALE=TRANSFORMED PADJUST=LSD. Has within factor as random effect with unstructured covariance
It gives very similar, but not identical to following in R
R
lgt12s < glmer(cbind(freq,Nmaxfreq) ~ wiithin + (wiithinpno), data=kex12,family=binomial(link="logit"))
anova(lgt12s)
lgt12ms<emmeans(lgt12s, ~wiithin,data =kex12)
lgt12ms
pairs(lgt12ms)
summary(lgt12s)
Here is output for 3 models
IMHO, SPSS within as random should be identical to within as repeated in data structure
Please can anyone explain WHY they are not the same
There must be SOMEONE out there who understands both R and SPSS who can explain what is happening
I note that within in data structure has lower AIC, BIC than within in random
So please, please how can one do 1st SPSS model in R?
HELP
Have also copied in Mark and Thomas, who I know to be R experts
Best
Diana
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