The learning objectives of this webinar are:

• Provide a conceptual overview of missing data theory and assumptions.
• Briefly discuss "traditional" missing data techniques that are widely available in software packages, and demonstrate their shortcomings.
• Provide researchers with a conceptual overview of two "modern missing data handling methods", multiple imputation and maximum likelihood estimation

Presenter

• Craig Enders, PhD, is an Associate Professor in the Quantitative Psychology concentration in the Department of Psychology at Arizona State University, where he teaches graduate-level courses in missing data analyses, multilevel modeling, and longitudinal modeling. The majority of his research focuses on analytic issues related to missing data analyses and multilevel modeling. His book, Applied Missing Data Analysis, was published with Guilford Press in 2010.