Dear Statalisters:
I have data from a cross-sectional survey with 4,000 observations. The
sample may be divided into two groups: group 1 (n=3,700) and group 2
(n=300). I wish to compare group 1 with group 2 on important demographic
characteristics (e.g., age, gender, race/ethnicity, income). Several of the
variables I wish to compare between group 1 and group 2 are categorical.
For example, income is measured using 6 categories.
There are missing data for several of the demographic variables. For
example, out of 4,000 possible observations, I have 3,450 observations on
income. I would like to use multiple imputation (as implemented by
the -hotdeck- procedure) to impute values for missing data so that I can
compare group 1 with group 2.
Typically, the model test for the following logistic regression
xi: logistic group i.income
would yield the same result (p-value) as
tab group income
However, I am working with survey data. I have noted that
xi: svylogit group i.income
does not yield the same result (p-value) as
svytab group income
My questions are as follows:
1) Why do svylogit and svytab yield different results?
2) What does this difference mean? In other words, can I still interpret
the model test for svylogit as a valid test for whether income differs
between the two groups (or is associated with group membership)?
3) Assuming that I can interpret the model test for svylogit as a test for
whether income varies between the two groups, could I use -hotdeck- as
follows to impute the missing data and interpret the overall F test as a
test for whether income differs between the two samples:
hotdeck income, by(sex race) imp(10) command(xi: logistic sample i.income
[pw = weight], cluster(psu)) parms( Iincome_2 Iincome_3 Iincome_4
Iincome_5 Iincome_6) keep(_all) store
4) Is there any other way that one could hot deck a 2-by-k (in the above
example, 2-by-6) chi-square test using Stata?
Thank you very much.
Best Regards,
Jim Shaw
Research Associate
College of Pharmacy
The University of Arizona
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