mi estimate
Posted: Fri Jul 11, 2014 3:14 pm
Dear all,
I am currently trying to analyse data with plausible values as dependent variables (PISA) with runmlwin and Stata's mi estimate integrating your suggestions in this thread: http://www.cmm.bristol.ac.uk/forum/view ... ?f=3&t=918
As the mi estimate command requires original variables before imputation and PISA as well as TIMMS only provide already imputed plausible values, I created a variable that only has missing values:
So that my estimation command becomes e.g.:
I would like to ask you three things regarding this approach:
1. Do you see any major issues with the first step of creating pv? I am afraid, I might overlook a problem here, but from my understanding this variable does not exist as the values are missing by design?
2. I had some problems with identifying how the requested weights will be included in MLwiN by weightvar (raw or PWIGLS Step A): If I drop the nopause statement and look in MLwiN, it seems they are used as raw weights, but if I simply include a small mistake in the specification, the error messages imply standardised weights, e.g.:
Standardised weights have been requested for level 2, this requires non-equal standardised weights for all levels below 2. Please define weights for these levels
3. Do you think one can trust the resulting p-values using the cmdok command, as runmlwin is not specifically supported by mi estimate? I also understand that the combination of p-values and fit indices such as deviance is quite problematic with multiple imputation, but I am somewhat unsure whether to build the model simply using the plausible values separately so that I can use lrtest?
I am using the 64-bit version of MLwiN and I would really appreciate any ideas regarding this.
Best regards and thank you for your consideration!
David
I am currently trying to analyse data with plausible values as dependent variables (PISA) with runmlwin and Stata's mi estimate integrating your suggestions in this thread: http://www.cmm.bristol.ac.uk/forum/view ... ?f=3&t=918
As the mi estimate command requires original variables before imputation and PISA as well as TIMMS only provide already imputed plausible values, I created a variable that only has missing values:
Code: Select all
egen pv = .
mi import wide, imputed(pv = pv1math pv2math pv3math pv4math pv5math)
Code: Select all
mi estimate, cmdok noisily: runmlwin pv cons ///
escs immig ictattpos intmat instmot, ///
level3(country: cons) level2(school: cons, weightvar(w_fschwt)) level1(student: cons, weightvar(w_fstuwt)) nopauseI would like to ask you three things regarding this approach:
1. Do you see any major issues with the first step of creating pv? I am afraid, I might overlook a problem here, but from my understanding this variable does not exist as the values are missing by design?
2. I had some problems with identifying how the requested weights will be included in MLwiN by weightvar (raw or PWIGLS Step A): If I drop the nopause statement and look in MLwiN, it seems they are used as raw weights, but if I simply include a small mistake in the specification, the error messages imply standardised weights, e.g.:
Standardised weights have been requested for level 2, this requires non-equal standardised weights for all levels below 2. Please define weights for these levels
3. Do you think one can trust the resulting p-values using the cmdok command, as runmlwin is not specifically supported by mi estimate? I also understand that the combination of p-values and fit indices such as deviance is quite problematic with multiple imputation, but I am somewhat unsure whether to build the model simply using the plausible values separately so that I can use lrtest?
I am using the 64-bit version of MLwiN and I would really appreciate any ideas regarding this.
Best regards and thank you for your consideration!
David