Multiple imputation using chained equations for missing data in TIMSS: a case study

Abstract

In this paper, we document a study that involved applying a multiple imputation technique with chained equations to data drawn from the 2007 iteration of the TIMSS database. More precisely, we imputed missing variables contained in the student background datafile for Tunisia (one of the TIMSS 2007 participating countries), by using Van Buuren, Boshuizen, and Knook’s (SM 18:681-694,1999) chained equations approach. We imputed the data in a way that was congenial with the analysis model. We also carried out different diagnostics in order to determine if the imputations were reasonable. Our analysis of multiply imputed data confirmed that the power of multiple imputation lies in obtaining smaller standard errors and narrower confidence intervals in addition to allowing one to work with the entire dataset.