Posts

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

CONTINUE READING

When I have to prepare data for Mplus, I use the MplusAutomation package in R. Its great! I import the SPSS data file into R with the foreign package. Then I use the prepareMplusData() function to create a .dat file for use in Mplus. This function also creates basic Mplus code that can pasted into Mplus or a text file used to prepare Mplus code files. MplusAutomation has many other great features and I highly recommend it for those who use Mplus and R.

CONTINUE READING

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.

CONTINUE READING

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.

CONTINUE READING

If you are a visitor to this site who is not taking one of my courses, you can use the site as you would any other site you find on the internet. If you are currently, or have previously enrolled in one of my courses, and want to find information relevant to that course, use the Courses tab to find your course, click on it to see the related posts.

CONTINUE READING

The first step in analyzing data is often getting the data into your chosen software. In this tutorial, I demonstrate how to manually enter data into SPSS and R as well as how to import existing date from two common formats. Manually Entering Data Let’s assume you had the following data on five students grades you wanted to enter into SPSS and R. You have 5 cases, in this example students, and two variables, the student’s name and their grade.

CONTINUE READING

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.

CONTINUE READING