14 September 2020 3 10K Report

I am running multilevel models in R (two-level and three level models) for my thesis. However, I have two problems:

1. Missing data

Literature advises use of Multiple Imputation (MI) or Full-information maximum likelihood (FIML). I do not know how to carry out these processes in r or stata taking into account multilevel modeling. I am looking for practical videos or articles that can help me run either of these processes. I would like to have something running from imputation to analysis process.

2. Many variables

I have many explanatory variables i.e. dummies (e.g gender), discrete and continuous variables. I am looking for a procedure to choose the variables for the regressions. I read some article that said PCA can only work for continuous variables. So this process should take into account multilevel modeling. Are there recommendations of practical videos or articles

Lastly, which one should be conducted first, sorting out missing data or choosing the variables (PCA or other process)

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