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Missing data: analysis and design
Monday 29 July, 2pm-5pm (break at 3.15pm for 10 mins)
Workshop will be led by John W. Graham, Pennsylvania State University:
In this half-day workshop, I will draw heavily on material from his recently published book, Missing Data: Analysis and Design, and other recent publications. He will present a sketch of missing data theory, including descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. I discuss attrition and nonignorable missingness, emphasizing the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. I also present a description of a planned missing data design known as two method measurement (TMM). The TMM design offers researchers a cost-effective strategy for maximizing power and construct validity when the best measures of key constructs are very costly. I discuss recent advances in the use of the TMM design in the context of assessing blood pressure, body fat, and physical activity. The workshop will consist of lectures, computer demonstrations, and open discussion.
Graham, J. W. (2012). Missing Data: Analysis and Design. New York: Springer.
Graham, J. W. (2009). Missing data analysis: making it work in the real world. Annual Review of Psychology, 60, 549-576.
Page last modified on 26 jun 13 14:56