How to run multilevel models with missing data using Stat-JR and MLwiN?
Posted: Wed Dec 27, 2023 6:53 am
Hi, I'm a Stat-JR user who wants to run multilevel models with missing data using MLwiN. I have a three-level dataset with students nested within schools nested within countries, and I have some missing values in the outcome and predictor variables. I want to use multiple imputation to handle the missing data and then fit a random intercept model with MLwiN. I have read the Stat-JR documentation and the multiple imputation template, but I'm still confused about how to do this. Can anyone help me with the following questions?
• How do I specify the imputation model and the analysis model in Stat-JR? Do I need to use the same variables and levels for both models?
• How do I choose the number of imputations and iterations for the imputation process? What are the criteria or rules of thumb for this?
• How do I export the imputed datasets from Stat-JR to MLwiN? Do I need to use the realcomimpute command or the mi: prefix in MLwiN?
• How do I combine the results from the imputed datasets using Rubin's rules? Can Stat-JR or MLwiN do this automatically or do I need to do it manually?
I would appreciate any guidance or advice on how to run multilevel models with missing data using Stat-JR and MLwiN. Thank you in advance.
• How do I specify the imputation model and the analysis model in Stat-JR? Do I need to use the same variables and levels for both models?
• How do I choose the number of imputations and iterations for the imputation process? What are the criteria or rules of thumb for this?
• How do I export the imputed datasets from Stat-JR to MLwiN? Do I need to use the realcomimpute command or the mi: prefix in MLwiN?
• How do I combine the results from the imputed datasets using Rubin's rules? Can Stat-JR or MLwiN do this automatically or do I need to do it manually?
I would appreciate any guidance or advice on how to run multilevel models with missing data using Stat-JR and MLwiN. Thank you in advance.