I am running a multilevel ordinal logistic regression model using PROC GLIMMIX. The outcome variable is number of school suspensions (none, 1, 2 or more), with children (level 1) nested in schools (level 2). There are multiple categorical predictor variables in the model as well.    The code I have used is as follows:   proc glimmix data=have method=quad ; class suspension_cat school_ID child_sex child_esl child_atsi mh_dx different_school siblings patage_cat matage_cat mat_maritalstat parent_highestED seifa_quintile school_aria; model suspension_cat = child_sex child_esl child_atsi mh_dx different_school siblings patage_cat matage_cat mat_maritalstat parent_highestED seifa_quintile school_aria / CL DIST= multi LINK= clogit SOLUTION ODDSRATIO (DIFF=last label); random intercept / subject=school_ID type=vc ; run;     The odds ratio estimates I obtain from the above code look like this: Odds Ratio Estimates Comparison Estimate DF 95% Confidence Limits male vs female 9.07 14256 6.48 12.69 ESL vs non-ESL 0.99 14256 0.63 1.57 ATSI vs non ATSI 2.27 14256 1.64 3.14 mh_dx vs no_mh_dx 10.73 14256 7.18 16.03 different school vs same school 1.41 14256 1.10 1.79 siblings 4 or more vs 1 3.15 14256 2.15 4.61 siblings 3 vs 1 2.04 14256 1.42 2.92 siblings 2 vs 1 1.14 14256 0.83 1.59 siblings none vs 1 1.84 14256 1.24 2.72 patage_cat under 20 vs 30 to 39 1.38 14256 0.77 2.48 patage_cat 20 to 29 vs 30 to 39 1.41 14256 1.04 1.92 patage_cat 40 and over vs 30 to 39 1.01 14256 0.61 1.65 matage_cat under 20 vs 30 to 39 1.64 14256 0.99 2.71 matage_cat 20 to 29 vs 30 to 39 1.31 14256 0.94 1.82 matage_cat 40 and over vs 30 to 39 1.39 14256 0.63 3.09 mat_maritalstat unmarried vs married 1.62 14256 1.18 2.21 mat_maritalstat divorced/widowed vs married 1.63 14256 0.78 3.42 parent_highestED high school vs university 1.47 14256 1.00 2.15 parent_highestED vocational vs university 1.46 14256 1.01 2.11 ses_quintile 1 vs 5 2.65 14256 1.58 4.45 ses_quintile 2 vs 5 2.07 14256 1.22 3.50 ses_quintile 3 vs 5 1.50 14256 0.87 2.58 ses_quintile 4 vs 5 1.02 14256 0.56 1.83 school_aria remote vs metro 1.59 14256 0.99 2.55 school_aria regional vs metro 1.29 14256 0.87 1.91   What I need is to have separate odds ratios for '1 suspension' and '2 or more suspensions' (i.e. all categories of suspension other than the reference category). I know that when running a multinomial logistic regression you can use the 'group= ' option to obtain odds ratios for each level of the DV, and this is what I'm needing for the ordinal logistic regression.   I believe I need to write an 'estimate' statement with the /exp option, but I haven't been able to work out exactly how to write this. Can someone please provide me with some sample syntax? ... View more

I have a data file containing educational test results for students in year levels 3, 5, 7, and 9. Each year level also has a value for "assessed year", based on the calendar year in which the student sat the test (values for calendar year vary by id). However I have many missing values for "assessed year". For each id, each value of assessed year should be 2 years apart. This is what the data should look like:   ID YearLevel AssessedYear 1 3 2007 1 5 2009 1 7 2011 1 9 2013 2 3 2009 2 5 2011 2 7 2013 2 9 2015 3 3 2011 3 5 2013 3 7 2015 3 9 2017 4 3 2003 4 5 2005 4 7 2007 4 9 2009 5 3 2003 5 5 2005 5 7 2007 5 9 2009   But this is what I have: ID YearLevel AssessedYear 1 3 . 1 5 2009 1 7 . 1 9 2013 2 3 2009 2 5 2011 2 7 2013 2 9 . 3 3 2011 3 5 . 3 7 . 3 9 . 4 3 . 4 5 2005 4 7 2007 4 9 2009 5 3 . 5 5 . 5 7 . 5 9 2009   I want to fill in the missing cells by calculating +/- 2 years from the non-missing values of assessed year.   Any suggestions will be greatly appreciated, this has been keeping me occupied for some time now!   Thanks ... View more