I am working on a Multivariate multilevel model where The 3 outcome variables RCBPre_Rating RCTPre_Rating RCSPre_Rating are continuous but changes in all three need to be considered simultanously as participants can indicate more or less of each RC type and this combination explains what they believe the complex root causes of an Ableism scenario are (RC1 is bias, RC2 is a lack of training, and RC3 is systemic barriers). The data set is double stacked where items (vignettes) are nested within Participants. There are item-level variables of ableism type (AT, 4 categories) (OB, 4 levels), and 2 item-level covariates of Trial oder. There are participant-level variables of teacher type (TT, 3 categories), and years of experience (YOE), and the participant-level covariates such as other demographic variables ( INLevel INDisSchool INLOE PerDis FamDis EthnicitySel Race Gender Ableismselfassess). I have tried two different ways to get the model to run, one using mutilple model lines in the proc mixed statement, and one using one model line with all three outcome variables, neither way is working! please help me! Syntax for each way is shown below. First way: proc mixed data=RQ2 method=ml; class ResponseID Vignette AT OB Teachertype INLevel INdisschool INLOE PerDis FamDis EthnicitySel Race Gender ADCTO Vtrialorder; /* Model statement for RC1 */ model RCBPre_Rating = OB|AT Teachertype|YOE Teachertype|YOE INLevel INDisSchool INLOE PerDis FamDis EthnicitySel Race Gender Ableismselfassess ADCTO Vtrialorder / solution ddfm=bw; /* Model statement for RC2 */ model RCTPre_Rating = OB|AT Teachertype|YOE Teachertype|YOE INLevel INDisSchool INLOE PerDis FamDis EthnicitySel Race Gender Ableismselfassess ADCTO Vtrialorder / solution ddfm=bw; /* Model statement for RC3 */ model RCSPre_Rating = OB|AT Teachertype|YOE Teachertype|YOE INLevel INDisSchool INLOE PerDis FamDis EthnicitySel Race Gender Ableismselfassess ADCTO Vtrialorder / solution ddfm=bw; /* Random effects */ random intercept / subject=ResponseID type=un; /* Random intercept for participants */ random intercept / subject=vignette type=un; /* Random intercept for vignettes */ /* Random slopes for participant-level effects */ random ADCTO / subject=vignette; Vtrialorder / subject=vignette; random OB / subject=vignette; random AT / subject=vignette; /* Allow covariance structure to account for multivariate response */ repeated / subject=ResponseID*vignette type=un group=ResponseID; run; Second way: proc mixed data=RQ2 covtest NOCLPRINT Method=REML; class ResponseID Vignette AT OB Teachertype INLevel INdisschool INLOE PerDis FamDis EthnicitySel Race Gender ADCTO Vtrialorder; /* Model statement for RC1 */ model RCBPre_Rating RCTPre_Rating RCSPre_Rating = OB|AT Teachertype|YOE Teachertype|YOE INLevel INDisSchool INLOE PerDis FamDis EthnicitySel Race Gender Ableismselfassess ADCTO Vtrialorder / solution DDFM=Satterthwaite; /* Random effects */ random intercept / subject=ResponseID type=un; /* Random intercept for participants */ random intercept / subject=Vignette type=un; /* Random intercept for vignettes */ /* Random slopes for participant-level effects */ random ADCTO / subject=ResponseID; random Vtrialorder / subject=ResponseID; random OB / subject=Vignette; random AT / subject=Vignette; /* Allow covariance structure to account for multivariate response */ repeated / subject=ResponseID*vignette type=un group=ResponseID; parms / ols; run;
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