[gov3009-l] Blackwell on "Multiple Overimputation: A Unified Approach to Measurement Error and Missing Data"

Hi all, We hope you can join us for the Applied Statistics Workshop this Wednesday, September 15th, when I will have the dubious honor of hosting myself, Matthew Blackwell, and presenting joint work with James Honaker and Gary King. Details of the talk and an abstract are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "Multiple Overimputation: A Unified Approach to Measurement Error and Missing Data" Matthew Blackwell (with James Honaker and Gary King) Governemnt September 15th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: Social scientists typically devote considerable effort to reducing measurement error during data collection only to ignore the issue during data analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative that generalizes the popular multiple imputation (MI) framework so that it treats missing data problems as a special case of extreme measurement error and corrects for both. Like MI, the proposed "multiple overimputation" (MO) framework is a simple two-step procedure. First, multiple (~5) completed copies of the dataset are created where cells measured without error are held constant, those missing are imputed from the distribution of predicted values, and cells (or entire variables) with measurement error are "overimputed," that is imputed from a predictive distribution with observation-level priors defined by the mismeasured values and available external information, if any. In the second step, analysts can then run whatever statistical method they would have run on each of the overimputed datasets as if there had been no missingness or measurement error; the results are then combined via a simple procedure. We also offer open source software that implements all the methods described herein. You can find a copy of the paper here: http://gking.harvard.edu/files/abs/measure-abs.shtml Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/