Hello,
I'm new to using mixed effects modeling, and I'm running a mixed effects logistic regression in STATA as my data were collected at different schools. Everything looked good when I ran the model, but after using multiple imputation to handle missing data, the random effects portion of the output looked strange (i.e., much larger standard error and no confidence interval). Here is the code and portion of the output:
mi estimate: xtmelogit y i.x1 x2 x3 x4 x5 || school:
.
.
.
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
school: Identity |
sd(_cons) | .000012 916.0454 0 .
------------------------------------------------------------------------------
Any ideas as to why this might be happening?
I'm new to using mixed effects modeling, and I'm running a mixed effects logistic regression in STATA as my data were collected at different schools. Everything looked good when I ran the model, but after using multiple imputation to handle missing data, the random effects portion of the output looked strange (i.e., much larger standard error and no confidence interval). Here is the code and portion of the output:
mi estimate: xtmelogit y i.x1 x2 x3 x4 x5 || school:
.
.
.
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
school: Identity |
sd(_cons) | .000012 916.0454 0 .
------------------------------------------------------------------------------
Any ideas as to why this might be happening?