Multiple imputation of multiple multi-item scales when a full imputation model is infeasible

Model - Based Multiple Imputation

managing Multiple imputation can improve data quality.

When Are Inferences from Multiple Imputation Valid?

Multiple imputation, as described by Rubin, has seen a wide variety of applications. Counterexamples, presented by Fay (1991), and new methods, such as those of J.N.K. Rao and J. Shao, that can asymptotically disagree with the multiple imputation approach, have raised questions about the validity of multiple imputation. This paper identifies critical restrictions on the practical application of...

Multiple Imputation with Mplus

Local Multiple Imputation

Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonpar...

Multiple imputation for item scores when test data are factorially complex.

Multiple imputation under a two-way model with error is a simple and effective method that has been used to handle missing item scores in unidimensional test and questionnaire data. Extensions of this method to multidimensional data are proposed. A simulation study is used to investigate whether these extensions produce biased estimates of important statistics in multidimensional data, and to c...