Missing data causes a lot of problems in data analysis. Unfortunately, some of the “solutions” for missing data cause more problems than they solve.
In this overview, you’ll learn the big picture of the concepts, challenges, and solutions of missing data.
- The goals of a good missing data approach
- The three missing data mechanisms and why they’re so important
- Common situations where missing data arises and their specific challenges
- Related data issues that share some of the same challenges and solutions
- The four main approaches to dealing with missing data and when each one does and doesn’t work
About the Instructor
Karen Grace-Martin helps statistics practitioners gain an intuitive understanding of how statistics is applied to real data in research studies.
She has guided and trained researchers through their statistical analysis for over 15 years as a statistical consultant at Cornell University and through The Analysis Factor. She has master’s degrees in both applied statistics and social psychology and is an expert in SPSS and SAS.
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