iematch - issue with long numeric strings #252
Labels
minor bug
Bug unlikely to lead to incorrect analysis
resolved but not yet published
Issue is fixed, but not yet published on SSC
Hi DIME Analytics,
I’ve tried using iematch, but when I run the command it continuously runs and never completes. I’ve let it run for several minutes. I don’t have an error code to provide because the command never completes or breaks. I’m testing it on a subset of data for which I have 40 observations, 10 in the base group and 30 in the target group. I’m trying to execute a 1-1 match. I would not think the command would take several minutes to run on 40 observations.
Set seed 1956
iematch if pair==1 & grade==2, grpdummy(srm_treatment) matchvar(orf) idvar(student_id) seedok replace
I assume this is a user-issue, but I wanted to verify that there was not some other issue with the command.
Sean
Hi Sean,
Thanks for letting us know. When I developed this I had to account for many infinite loop issues, but since the release I have not had anyone report another case. Are you able to share a deidentified version of the data that I can test myself on? If there is an error I’d like to fix it as others might have had the same issue without reporting it.
I do not see any error in the information you have provided so far.
Best,
Kristoffer
[External]
Hi Kristoffer,
Thanks for the reply. Attached is a de-identified dataset using a subset of the data. I’ve included the first two pairs of matched schools. The full dataset has 25 matched school pairs. For treatment schools, we assessed 10 students, but for control schools we assessed 30 students. Students from Grade 2 and Grade 4 were assessed. The goal is to match at the student-level within the matched schools, find a match control student for each treatment student. This has to be done for students in Grade 2 and Grade 4.
A few notes on the dataset that I’ve attached. The student ID variables are randomly generated by our data collection app, so the numbers have no meaning outside of the dataset. I’ve recoded the school codes with numbers from a random number generator. The original school codes are tied to EMIS codes in the country where the study is happening. The variable for the match is orf, which is oral reading fluency. Let me know if you have any questions about the attached dataset.
Sean
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