MAR means that missingness can be predicted by variables you observe but
not by variables you don't observe. so unless you observe some more
variables, you can't really test MAR. its sort of the same as the
assumption of no omitted variable bias in regression analysis; you can't
test it unless you have that omitted variable. without it, you make
arguments. assuming MAR however means that you're using all the info
you have at your disposal.
however, if any observed variables predict missingness, then your data
are not MCAR.
Amelia II does include some features that allow you to go beyond MAR, by
including more info in your imputation model than just that in the data,
such as priors on cell values and such.
Gary
---
http://gking.harvard.edu
On 11/6/2008 9:10 PM, Carlos Rodriguez wrote:
Hello,
What would be evidence that the MAR assumption holds? Could I for
example, run a regression in which the dependent variable is a dummy
for "missing obs." and the independent variables are the other
covariates in the analysisi model? Or would it be enought to show
relatively strong bivariate correlations between missingness and
control variables?
I guess too strong correlation between missing observations for a
variable and a dummy for time period or county will pose a problem for
multiple imputation because it may be a sign of truncation or
systematic missingnes...then MAR would not hold, right?
Are there any formal tests for showing that the data are not missing
at completly random (MCRA) but at MAR?
THANKS TO ALL and have a nice day,
Carlos
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