This tutorial demonstrates how to use full information maximum likelihood (FIML) estimation to deal with missing data in a regression model using lavaan.
Import Data In this post I use FIML to deal with missing data in a multiple regression framework. First, I import the data from a text file named ‘employee.dat’. You can download a zip file of the data from Applied Missing Data website. I also have a github page for these examples here.
FIML for Missing Data in Lavaan Full information maximum likelihood (FIML) is a modern statistical technique for handling missing data. If you are not familiar with FIML, I would recommend the book entitled Applied Missing Data Analysis by Craig Enders. The book is both thorough and accessible, and a good place to start for those not familiar with the ins and outs of modern missing data techniques.
The purpose of the FIML in Lavaan series of posts and the related git repository is to take some of the examples related to FIML estimation within a regression framework from the Applied Missing Data website, and translate them into code for the R package lavaan.
This is the third tutorial in a series that demonstrates how to us full information maximum likelihood (FIML) estimation using the R package lavaan. In this post, I demonstrate two methods of using auxiliary variable in a regression model with FIML. I am using data and examples from Craig Ender’s website Applied Missing Data. The purpose of these posts is to make the examples on Craig’s website, which uses Mplus, available to those who prefer to use lavaan.