Missing Data

FIML in Lavaan: Regression Analysis with Auxiliary Variables

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

FIML in Lavaan: Regression Analysis

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 in Lavaan: Regression Analysis with Auxiliary Variables

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.