A Unified Approach to Measurement Error and Missing Data: Overview and Applications

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And 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 without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion article with technical details and extensions. 1 Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA Corresponding Author: Gary King, Harvard University, Institute for Quantitative Social Science, 1737 Cambridge Street, Cambridge, MA 02138, USA. Email: [email protected] Sociological Methods & Research 2017, Vol. 46(3) 303-341 a The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0049124115585360 journals.sagepub.com/home/smr