Multilevel Missing techniques

halladje — Mon, 17 Jun 2019 15:33:37 GMT

Hi All,

I am trying to test out different strategies for dealing with missing data in a multi-level dataset (3 levels). Does anyone know whow I can perform multilevel missing data strategies including:

(1) Full information maximum likelihood

(2) Joint Modelling

(3) Multilevel imputation by fully conditional specification

Thanks in advance!

Jillian halladje@mcmaster.ca

Re: Multilevel Missing techniques

DarthPathos — Thu, 04 Jul 2019 11:32:10 GMT

hi Jillian

Take a look at this paper - it may give you some ideas, and talks about a macro MMI_IMPUTE.

Having said that, I've poked around here and found a thread asking a very similar question; the author of the paper, who as of 2017 worked for SAS, posted "...I no longer recommend using MMI_IMPUTE. MMI_IMPUTE uses an imputation algorithm called PAN, which was developed a while back by Joseph Schafer. Unfortunately, PAN is a bit outdated, and is less flexible than newer algorithms (e.g., the algorithm can't incorporate random effects between incomplete variables; all incomplete variables are required to be normally distributed). I recommend that you instead use a standalone software package called Blimp. Blimp was written by Brian Keller and Craig Enders at UCLA. Unlike MMI_IMPUTE, Blimp can handle random effects between incomplete variables and can also handle some non-normal incomplete variables (e.g., binary variables). The software and associated documentation are available at http://www.appliedmissingdata.com/multilevel-imputation.html. Blimp is free, and the website contains scripts for using it from SAS."

I don't know Blimp at all, but as Missing Data / Imputation is something I'm interested in, I will definitely be taking a look. Please post back if you have any further questions; I'm also happy to contact you via email if that's easier.

Chris

topic Multilevel Missing techniques in Statistical Procedures

Multilevel Missing techniques

Re: Multilevel Missing techniques