Missing data: FIML and (multilevel) multiple imputation

Welcome to the forum for MLwiN users. Feel free to post your question about MLwiN software here. The Centre for Multilevel Modelling take no responsibility for the accuracy of these posts, we are unable to monitor them closely. Do go ahead and post your question and thank you in advance if you find the time to post any answers!

Remember to check out our extensive software FAQs which may answer your question: http://www.bristol.ac.uk/cmm/software/s ... port-faqs/
Post Reply
stephan87
Posts: 9
Joined: Sun May 11, 2014 8:06 am

Missing data: FIML and (multilevel) multiple imputation

Post by stephan87 »

Hi experts

I have >15% missing data in my continuous multilevel model; missing at independent variables at all levels but not the dependent variable on level 1. Paul Allison writes that Full-Information Maximum Likelihood has advantages compared with multiple imputation for single-level models. MLwiN apparently uses FIML (Hox, J.J. 2009 Multilevel Analysis: Techniques and Applications p138) (Twisk, Jos W.R 2003, Applied Longitudinal Data Analysis for Epidimeology, p.262) (what I gather from MLwiN manuals, forum etc) but is also not listed among 'FIML-software' in e.g. Allison 2012 (http://www.statisticalhorizons.com/wp-c ... llison.pdf). I understand that missing at cluster level results in the whole cluster being discarded. However, it does not seem to me that MLwiN uses all information available within cluster 2 (level 1) since only a listwise deletion could explain that I end up with only using 230,000 of 309,000 cases. Besides, I 'feel' MLwiN is so much quicker than MPlus (which definitely uses FIML) and results in less missing. I'm now spending 3+ days to impute level 1 and level 2 variables by level 4 clusters (countries) with Realcom Impute to see if anything improves, but I wonder how biased my model would be without the complexity of imputation in an already complex study. Note that I am forced to have 5 imputed sets if using MI.
In short, is IGLS equivalent to FIML and can MAR at level 1 be 'ignored'? Or am I missing the point (pun intended).
Thanks for any help.
EDIT: Spelling
Harvey Goldstein
Posts: 49
Joined: Sun Sep 06, 2009 5:30 pm

Re: Missing data: FIML and (multilevel) multiple imputation

Post by Harvey Goldstein »

The IGLS estimates are indeed maximum likelihood ones.
Re missing data the mlwin default is listwise deletion of all level 1 records where any model variable has a missing value.
If you wish to do an efficient multiple imputation on 2-level data you have two possibilities now. Either REALCOM which has been around for a few years or the more recent STATJR software which is faster. Go to the CMM website for details about getting these and the relevant references to the procedures involved.
Harvey Goldstein
Post Reply