Thanks in advance!

Edit: I was thinking that it would probably help if I provide an example for others to replicate. Perhaps this is just a silly mistake I did. I am using the example dataset for the realcomImpute command in Stata ("prac2full.dta") which can be downloaded here (http://missingdata.lshtm.ac.uk/examplea ... eStata.zip). I generate a random level-2 variable which I divide into quartiles and delete 10% completely at random. I was using the default imputation settings for the below example but from what I recall, changing them does not seem to make a difference.

Code: Select all

```
gen r=invnormal(uniform())
bys school: egen randomlevel2variable=mean(r)
gen quartile=.
quietly sum randomlevel2variable, d
replace quartile=1 if randomlevel2variable<r(p25)
replace quartile=2 if randomlevel2variable<r(p50) & quartile==.
replace quartile=3 if randomlevel2variable<r(p75) & quartile==.
replace quartile=4 if quartile==.
gen s=uniform()
bys school: egen mmissing=mean(s)
quietly sum mmissing, d
replace quartile=. if mmissing<r(p10)
drop r s randomlevel2variable mmissing
sort school
realcomImpute nlitpre o.quartile nlitpost fsmn gend using prac2fullMIInput.dat, replace numresponses(2) level2id(school) cons(cons)
*** after imputation ****
realcomImputeLoad
mi convert flong, clear
tab quartile _mi_m
```

_mi_m

quartile 0 1 2 3 4 5 6 ...

1 1,100 1,566 1,566 1,566 1,566 1,566 1,566 ....

2 1,147 1,147 1,147 1,147 1,147 1,147 1,147 ...

3 1,114 1,114 1,114 1,114 1,114 1,114 1,114 ...

4 1,046 1,046 1,046 1,046 1,046 1,046 1,046 ...

Total 4,407 4,873 4,873 4,873 4,873 4,873 4,873 ...

(Sorry about the formatting of this table but the rows correspond to the quartiles and the columns are the imputations)