Multiple imputation using chained equations for missing data in TIMSS: a case study
By Donia Smaali Bouhlila, Fethi Sellaouti. Large-scale Assessments in Education, 2013
61 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/425c2f574c8b1a01ad582f830d44324215bb9781
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Citations in the corpus
- The acquisition of problem solving competence: evidence from 41 countries that math and science education matters (2014, methodology): “By applying Rubin’s combination rules (Bouhilila and Sellaouti 2013; Enders 2010), analyses were replicated with each of the five plausible values and then combined.”