SSI 2018 New Course Spotlight: "Missing Data Analysis using Mplus" with Keenan Pituch
SSI 2018 New Course Spotlight: "Missing Data Analysis using Mplus" with Keenan Pituch
Instructor Biography
Dr. Keenan Pituch earned a Ph.D. in Research Design and Statistics from Florida State University in 1997. He is currently an Associate Professor in Quantitative Methods in the Department of Educational Psychology at The University of Texas at Austin. His methodological research interests include multivariate analysis, multilevel modeling, missing data analysis, intensive longitudinal modeling, and mediation analysis.
What is the main goal of this course?
The main goal of the course is to help participants apply proper missing data treatments when they analyze data, as well as understand why specialized missing data treatments are sometimes necessary.
What makes you excited about teaching this course?
The concepts underlying missing data treatments are quite interesting in themselves. However, I really look forward to helping participants use, what are considered to be, the best missing data treatments, as nearly all empirical research involves missing data.
How much background knowledge or experience in this subject is required to be able to follow the course material?
An individual with a good working knowledge of multiple regression can certainly benefit from the workshop. Those with an understanding of logistic regression and factor analysis should be able to get more out of the course, as I will show how missing data treatments are applied to such analysis models. I assume that participants have no understanding of modern missing data treatments and no previous exposure to Mplus.
What skills and knowledge can participants expect to acquire by the end of the course?
Participants will learn why and under what circumstances traditional methods of handling missing data work well and not so well. They should also come out of the workshop with a conceptual understanding of why more modern missing data treatments are generally better. At the end of the workshop, they should know how to run Mplus to apply these treatments to various analysis models (i.e., regression, ANCOVA, factor analysis). Mplus is particularly useful for missing data analysis because it can apply modern missing data treatments to many research situations and is easy to use.
You’ve taught "Hierarchical Linear Modeling" for several years at the SSI. How was that experience? What made you interested in teaching something brand new, "Missing Data analysis Using Mplus"?
I have really enjoyed teaching the HLM workshop for the past 10 years. In 2016, I began teaching a semester-long Missing Data Analysis course in the Educational Psychology Department. I do not believe that SSI has provided such a workshop in the past. Given the prevalence of missing data and the advances in software that make it relatively easy, in many cases, to apply such treatments, it just seems like the right time to present such a workshop.
The Department of Statistics and Data Sciences at The University of Texas at Austin is proud to host the 11th annual UT Summer Statistics Institute (SSI) May 21 - 24, 2018. Registration is now open!
