Modern Missing Data Analysis

Length: 3 Days
Instructor: Craig Enders
Lecture Recordings: Lifetime Access
Software Demonstrations: Mplus, R, and Blimp
Evergreen Content: Materials Continually Updated

Class Overview Video

Student: $594
Professional: $774

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Category: Asynchronous, Workshops

Missing data are a ubiquitous feature of nearly all research applications, arising through participant non-response, attrition, and sometimes even by design. Failure to account appropriately for missing values when conducting statistical analyses can result in badly biased estimates and incorrect inferences about the relationships under study. Modern Missing Data Analysis is a three-day workshop focused on three optimal approaches for addressing missing data that can be applied across a variety of research settings: maximum likelihood, Bayesian estimation, and multiple imputation. Participants will learn details about the classic versions of these methods as well as state-of-the-art extensions that have been developed in the last five years. These procedures are advantageous because they use all available data and make realistic assumptions about the cause of missingness; estimates and significance tests are therefore valid in a broader range of situations than historical methods such as deleting incomplete data records. The purpose of this course is to provide participants with foundational knowledge about maximum likelihood, Bayesian estimation, and multiple imputation. Equally important, participants will learn how to select an appropriate missing data handling method and apply it to complex, real-world data sets. To this end, presentations will include a mix of theoretical information, practical tips, and computer applications.


2:06 Class Content / 12:41 Materials Provided / 14:47 Learning Objectives

Instructor

Craig K. Enders, PhD

Craig Enders is Professor and Area Chair of Quantitative Psychology in the Department of Psychology at the University of California, Los Angeles. His primary research focus is on analytic issues related to missing data analyses, and he leads the research team responsible for developing the Blimp statistical software application. Read More

Workshop Details

Reviews

Craig is easily one of the best statistics professors I have ever had. He has taken an incredibly complex topic that I have previously struggled to understand and apply to my own data and made it possible for me to feel confident in using and interpreting the analyses on my own

Dr. Enders did a great job of explaining concepts in multiple ways when prompted to do so by student questions, and creating an atmosphere where students felt comfortable asking questions -- Dr. Enders welcomed both basic questions and those that were beyond the scope of the course but related to a student's own research interests.

I felt the slides were quite easy to understand and appreciated that there were figures which helped to illustrate conceptual understanding underlying each concept.

I benefited so much from this class, both in learning the material, and in the professor's example for effective pedagogy.

Prof Enders is a great lecturer. He puts complicated stats concepts in terms that are easy to understand, knows when to pause to let students process, and knows when to go over something multiple times/in multiple ways to maximize student understanding.

I really appreciated this class because it taught me practical skills of how to do statistics in my own research, but also helped me to concretely understand important concepts.

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