Missing data in research can lead to biased results if not properly considered.

During this webinar, presenter Isaac Washburn, Ph.D., will address the dangers that come from ignoring missing data issues. He’ll introduce the concepts of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) and he’ll discuss how past methods of dealing with missing data are inadequate for dealing with the bias created by most forms of missing data.

Dr. Washburn will then focus on the two modern approaches to the problem of missing data: multiple imputation and maximum likelihood. He will walk through several examples using Mplus, Stata, and SPSS, showing how multiple imputation works and highlighting maximum likelihood techniques.

Approved for 1.5 CFLE contact hours of continuing education credit.