--- On Wed, 18/11/09, Hoogendoorn, Adriaan wrote:
> I use Stata's facilities for Multiple Imputation to solve
> my missing data problem.
> I'm motivated to do so, since I "think" that the missing
> data pattern is not Missing Completely At Random (MCAR).
> I'd like to sustain my "thought" by testing MCAR and read
> in the literature about a test for this purpose.
> Do you know of a package to do Little's MCAR test?
I don't know this particular test, but one thing you can do
very easily is test whether the probability of having a
missing value on one of the explanatory variables is
associated with the explained variable. The logic is that
missing values only influence the results if the
probability of missingness is associated with the dependent
variable. A simple proof can be found in footnote 1 of
Allison (2002).
You obiviously cannot test whether the probability of
missingness on the dependent variable depends on its own
unobserved value, but you can perform this test for the
explanatory variables, as is shown in the example below for
a continuous dependent variable (wage) and a dichotomous
dependent variable (collgrad).
*------------ begin example --------------
sysuse nlsw88, clear
gen byte miss = missing(union, tenure)
ttest wage, by(miss)
tab miss collgrad, row chi2
*------------- end example ---------------
Hope this helps,
Maarten
Allison, Paul D. Missing Data. Quantitative Applications
in the Social Sciences, nr. 136. Thousand Oaks: Sage.
--------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany
http://www.maartenbuis.nl
--------------------------
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