Great suggestions - i have attempted to work through them. 1. I have included all two-way interactions into the model in addition to the three-way. Unfortunately, two of the main effects and one of the two ways still yield a zero f-value. 2a. This did not alter the model results, still yielding zero f-values 2b. This looked quite promising. Upon first inspection this fixed the zero f-value issue. However in an attempt to replicate the instability of my earlier models, this one was also shown to be unstable. The syntax worked in this case: PROC GLIMMIX DATA=SASUSER.onscreenimitation_extras METHOD=laplace IC=Q;
class PID Task Age_Group Condition;
model TaskPerformance = Task Age_Group Condition Age_Group*Task*Condition / s dist=multinomial link=cumlogit;
random task / subject=PID(Condition Age_Group) type=un;
covtest / wald;
run; But when age_group is moved before task in the model statement it fails, syntax: PROC GLIMMIX DATA=SASUSER.onscreenimitation_extras METHOD=laplace IC=Q;
class PID Task Age_Group Condition;
model TaskPerformance = Age_Group Task Condition Age_Group*Task*Condition / s dist=multinomial link=cumlogit;
random task / subject=PID(Condition Age_Group) type=un;
covtest / wald;
run; 3. I am still working through this suggestion - but attempted stepping down rather than stepping up through the model. I removed the three-way interaction (and included all 2-way interactions) and the model completely stabilises. Unfortunately our a priori hypotheses, study design and underlying theory necessitates a three-way interaction. Interestingly, when using a continuous Age measure, rather than a categorical age measure, the entire model including the three way interaction is stable. Suggesting the problem may arise from the Age_group variable. 4. This method will not run. I receive the error "quanew optimization could not be completed". 5. Yes I refer to using the same variables in different orders in the class and/or model statement (changes to either or both lines result in the same issues). I do suspect that it is a symptom of a dysfunctional model given that the Age_Group variable yields instability whilst the Continuous_Age variable yields stability in identical models. Thank you for all of your suggestions thus far, it seems we are getting closer to a solution!
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