REGRESSION WITH MISSING YS: AN IMPROVED STRATEGY FOR ANALYZING MULTIPLY IMPUTED DATA

Authors


  • I thank Paul Allison, Doug Downey, Jerry Reiter, and Donald Rubin for helpful feedback on an earlier draft. Direct correspondence to Paul T. von Hippel, Department of Sociology, Ohio State University, 300 Bricker Hall, 190 N. Oval Mall, Columbus OH 43210; e-mail von-hippel.1@osu.edu

Abstract

When fitting a generalized linear model—such as linear regression, logistic regression, or hierarchical linear modeling—analysts often wonder how to handle missing values of the dependent variable Y. If missing values have been filled in using multiple imputation, the usual advice is to use the imputed Y values in analysis. We show, however, that using imputed Ys can add needless noise to the estimates. Better estimates can usually be obtained using a modified strategy that we call multiple imputation, then deletion (MID). Under MID, all cases are used for imputation but, following imputation, cases with imputed Y values are excluded from the analysis. When there is something wrong with the imputed Y values, MID protects the estimates from the problematic imputations. And when the imputed Y values are acceptable, MID usually offers somewhat more efficient estimates than an ordinary MI strategy.

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