A simple do file should work.
display caseno age if age<0 or age>120 & age<. // may want to print
missing ages
display caseno gender if gender~=a | gender ~=b // a and b are the
unique values (could be strings so you'd want to fix that up)
diplay caseno if age<NNN and mother==1 // mother is an indicator
etc.
An interesting question is whether you want to correct these - e.g.
convert them to missing or an error code (I first typed coed - but
that's NOT what I meant!)
In a study earlier this summer I did just this. Initially I printed all
the missing value cases, but the data came from medical records and
about half of 2000 cases were missing, so I simply didn't print, but
gave a count for each variable.
Some of the variables had many possible legal values (e.g., which of 30
drugs were being taken), so the checking became very complicated -
especially when the dosage and schedule were being checked.
Svend Juul has a nice chapter on this in his book.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Sergiy
Radyakin
Sent: Tuesday, October 07, 2008 2:02 PM
To: [email protected]
Subject: st: Data consistency heuristics
Hello All,
this is more or less general question, not related to Stata itself,
but to data processing. I wonder if anyone could point me to a good
source of heuristics/rules on checking the data for
consistency/plausibility. I am looking for something like:
* age of a person must be within the range 0-120
* gender must have no more than 2 unique values
* person younger than NNN years may not be a mother
* if a person is reporting not working, wage must be missing/zero
* if a person is attending primary school, occupation may not be
"manager"
* if a person is attending university, [s]he may not report being
illiterate
etc
Note that these are more or less flexible rules and there might be
exceptions. But if it is valid for 99% of cases - it's what I am
looking for.
The context topics include economics
(employment/earnings/wages/sector/hours of work etc), education(years
of educ/enrollment/completion), family structure and composition, and
other related topics commonly found in family, household or labor
force surveys.
I believe a significant amount of such checks is being done by data
collectors before releasing the data to public, and I wouldn't want to
reinvent the wheel here.
Thank you, Sergiy Radyakin
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