Can someone help me with creating a composite measure of prejudice from
four individual variables in my data set which measure prejudice.
the variables are:
deasyblk: perception of blacks as easy to get along with
dwelfblk: perception of blacks as likely to be on welfare
dintlblk: perception of blacks as intelligent
drichblk: perception of blacks as rich or poor
the variables are distributed as follows:
. tab deasyblk
easy to get along |
w/blacks | Freq. Percent Cum.
---------------------+-----------------------------------
easy to get along w/ | 915 10.26 10.26
2 | 1052 11.80 22.06
3 | 1379 15.47 37.53
neither | 2722 30.53 68.06
5 | 1143 12.82 80.88
6 | 638 7.16 88.03
hard to get along w/ | 547 6.14 94.17
don't know... | 418 4.69 98.86
missing | 102 1.14 100.00
---------------------+-----------------------------------
Total | 8916 100.00
What I want to do is combine these four variables into one measure of
prejudice, which will become a dependent variable in some of my models.
The only way I could think to do it was to create a new variable prejblk
with numerical values 1 through 7 that equal the sums of the respective
1 through 7's
from my four variables...
gen prejblk=.
replace prejblk=1 if drichblk==1|dwelfblk==1|deasyblk==1|dintlblk==1
replace prejblk=2 if drichblk==2|dwelfblk==2|deasyblk==2|dintlblk==2
etc.
