Category: Research Methods and Statistics
Keywords: Data Analysis | Full Information Maximum Likelihood | Multiple Imputation
Presentation Type: AMASS
Level of Familiarity: Basic
There have been substantial methodological advances in the area of missing data analyses during the last 25 years. Methodologists currently regard maximum likelihood estimation (ML) and multiple imputation (MI) as two state of the art missing data handling procedures. These two procedures are advantageous because they use all available data, thereby mitigating the loss of power from missing data. Moreover, these techniques make less strict assumptions about the cause of missing data, thereby providing accurate estimates and significance tests in a wider ranger of situations than traditional missing data handling techniques. The purpose of this course is to familiarize participants with ML and MI and to demonstrate the use of these techniques using software packages. The goal of this workshop is to provide participants with the skills necessary to understand and implement ML and MI. To this end, the course will provide a mixture of theoretical information and computer applications. The workshop content will be accessible to researchers with a foundation in multiple regression.
Earn 4 continuing education credits
Recommended Reading: Enders, C. K. (2016). Multiple imputation as a flexible tool for missing data handling in clinical research. Behavior Research and Therapy, Advanced online publication. doi:10.1016/j.brat.2016.11.008
Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549-576.
Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147â€177
Thursday, November 16
1:00 PM – 5:00 PM
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