Multiple Imputation for Combined-Survey Estimation with Incomplete Regressors in One but Not Both Surveys

Published In: Sociological Methods Research, v. 42, no. 4, Nov. 2013, p. 483-530

Posted on on November 01, 2013

by Michael S. Rendall, Bonnie Ghosh-Dastidar, Margaret M. Weden, Elizabeth Baker, Zafar Nazarov

Read More

Access further information on this document at Sage Publications

This article was published outside of RAND. The full text of the article can be found at the link above.

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has more regressors, but typically fewer observations, than the other. This adaptation is achieved through (1) larger numbers of imputations to compensate for the higher fraction of missing values, (2) model-fit statistics to check the assumption that the two surveys sample from a common universe, and (3) specifying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the missing at random assumption needed for unbiased MI. Large efficiency gains and substantial reduction in omitted variable bias are demonstrated in an application to sociodemographic differences in the risk of child obesity estimated from two nationally representative cohort surveys.

This report is part of the RAND Corporation external publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.