title: Multiple Imputation of Item Scores in Test and Questionnaire Data, and Influence on Psychometric Results.
abstract: The performance of five simple multiple imputation methods for dealing with missing data were compared. In addition, random imputation and multivariate normal imputation were used as lower and upper benchmark, respectively. Test data were simulated and item scores were deleted such that they were either missing completely at random, missing at random, or not missing at random. Cronbach's alpha, Loevinger's scalability coefficient H, and the item cluster solution from Mokken scale analysis of the complete data were compared with the corresponding results based on the data including imputed scores. The multiple-imputation methods, two-way with normally distributed errors, corrected item-mean substitution with normally distributed errors, and response function, produced discrepancies in Cronbach's coefficient alpha, Loevinger's coefficient H, and the cluster solution from Mokken scale analysis, that were smaller than the discrepancies in upper benchmark multivariate normal imputation.
authors.affiliation: a Tilburg University , The Netherlands.
authors.forename: Joost R
authors.initials: JR
authors.lastname: van Ginkel
authors.affiliation: a Tilburg University , The Netherlands.
authors.forename: L Andries
authors.initials: LA
authors.lastname: van der Ark
authors.affiliation: a Tilburg University , The Netherlands.
authors.forename: Klaas
authors.initials: K
authors.lastname: Sijtsma