hi all--
i am currently encountering a data management/display issue. i welcome
any suggestions on dealing with this.
thank you,
--matthew
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matthew m. gushta -- research associate
computer & statistical sciences center
american institutes for research
[email protected] -- 202.403.5079
**************************************************
PROBLEM
i have a dataset containing student test scores. within this data are
district, school, and teacher variables. i will be running a mixed model
incorporating all of these variables, unfortunately, the teacher
variable is a manually-entered string variable. this means that within
school X, there might be teachers A, B, and C, however, due to
variations in data entry, teachers may appear different who in fact are
not.
in order to QC this and recode teacher values where appropriate, i would
like to basically crosstab school and teacher variables, so that only
unique teacher values appear within each school. you can see that each
school is presented in a separate table and teacher "grant" appears
twice in school 2766 (see the syntax and sample output below).
...given 2105 districts and 5262 teachers, this output is quite
cumbersome.
is there a simpler, more compressed format for such output? i.e., a
single table?
**************************************************
SYNTAX
bysort schirn: tab teacher
**************************************************
OUTPUT
--------------------------------------------------
-> schirn = 2758
TEACHER | Freq. Percent Cum.
--------------+-----------------------------------
HANTHORX | 14 31.11 31.11
MILLER | 15 33.33 64.44
SMITH | 16 35.56 100.00
--------------+-----------------------------------
Total | 45 100.00
--------------------------------------------------
-> schirn = 2766
TEACHER | Freq. Percent Cum.
--------------+-----------------------------------
CAMPBELL | 24 7.50 7.50
DOLORESCO | 23 7.19 14.69
FLEMING RACHE | 25 7.81 22.50
GRANT | 1 0.31 22.81
HAAS | 25 7.81 30.63
HARRISON | 25 7.81 38.44
JONES | 25 7.81 46.25
L SMITH | 25 7.81 54.06
LABUS | 25 7.81 61.88
OWENS | 25 7.81 69.69
SMIALEK | 22 6.88 76.56
SONYA GRANT | 25 7.81 84.38
STAUFFER | 25 7.81 92.19
WELLING | 25 7.81 100.00
--------------+-----------------------------------
Total | 320 100.00
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