Missing data

Using FIML in R for Multilevel Data

Using FIML in R with Multilevel Data (Part 3) A recurring question that I get asked is how to use full information maximum likelihood (FIML) when performing a multiple regression analysis BUT this time, accounting for nesting or clustered data structure. For this example, I use the the leadership dataset in the mitml package (Grund et al., 2021). We’ll also use lavaan (Roseel, 2012) to estimate the two-level model. The chapter of Grund et al.

Using FIML and MI in R

Using FIML in R (Part 2) A recurring question that I get asked is how to handle missing data when researchers are interested in performing a multiple regression analysis. There are so many excellent articles, books, and websites that discuss the theory and rationale behind what can be done. Often, what is recommended is to either use full information likelihood (FIML) or multiple imputation (MI). Many excellent articles explain in detail how these work.

Missing Data (Rough) Notes

Create some missing data Impute missing data Selecting the imputation method manually Analyze (imputed results) Pool results (using Rubin’s rules) Creating nicer output Example Others: Extracting datasets Using Full Information Maximum Likelihood library(mice) #for imputation library(summarytools) #for freq library(dplyr) #other data management dat <- rio::import("http://faculty.missouri.edu/huangf/data/kbbcarVALUE.xls") summary(dat) ## Price Mileage Make Model ## Min. : 8639 Min. : 266 Length:804 Length:804 ## 1st Qu.:14273 1st Qu.:14624 Class :character Class :character ## Median :18025 Median :20914 Mode :character Mode :character ## Mean :21343 Mean :19832 ## 3rd Qu.