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 compare them with lower benchmark listwise deletion, two-way with error and multivariate normal imputation. The new methods produce smaller bias in several psychometrically interesting statistics than the existing methods of two-way with error and multivariate normal imputation. One of these new methods clearly is preferable for handling missing item scores in multidimensional test data.
van Ginkel
Joost R
JR
Tilburg University, The Netherlands. j.vanginkel@vumc.nl
van der Ark
L Andries
LA
Sijtsma
Klaas
K
D003627:Data Interpretation, Statistical; D005163:Factor Analysis, Statistical; D006801:Humans; D008960:Models, Psychological; D011584:Psychology / Q000379:methods* / Q000706:statistics & numerical data*