version 13.1
clear
// Data set for Example 16.1
// mlbook2, data set with pupils having missings on ses or IQ_verb or (langPOST and aritPOST) excluded.
input schoolnr pupilNR_new langPOST aritPOST ses IQ_verb IQ_perf Minority denomina sch_ses sch_iqv sch_min
1.00 1.00 . 12.00 -17.73 -1.37 -3.75 1.00 1.00 -14.035 -1.4039 0.630
// .. Skip a couple of thousand observations
end
// Rename outcome variables for -reshape-:
rename langPOST score1
rename aritPOST score2
// Convert data into long format:
reshape long score, i(pupilNR_new) j(outcome)
// Create dummy variables that identify outcome variable:
quietly tab outcome, gen(outcome)
// Results in Table 16.1:
mixed score outcome1 outcome2, nocons ///
|| schoolnr: outcome1 outcome2, nocons cov(un) ///
|| pupilNR_new: , nocons cov(un) residuals(un, t(outcome))
// Results in Table 16.2:
mixed score outcome1 outcome2 ///
outcome#c.IQ_verb outcome#c.ses ///
outcome#c.sch_iqv outcome#c.sch_ses ///
outcome#c.IQ_verb#c.ses outcome#c.sch_iqv#c.sch_ses, nocons ///
|| schoolnr: outcome1 outcome2, nocons cov(un) ///
|| pupilNR_new:, nocons cov(un) residuals(un, t(outcome))
Reference
Snijders, Tom, and Roel Boskers. 2012. Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modeling, 2nd ed. Sage.
