Imputation of incomplete questionnaire items should preserve the structure among items and the correlations between scales. This paper explores the use of fully conditional specification (FCS) to impute missing data in questionnaire items. FCS is particularly attractive for items because it does not require (1) a specification of the number of factors or classes, (2) a specification of which item belongs to which scale, and (3) assumptions about conditional independence among items. Imputation models can be specified using standard features of the R package MICE 1.16. A limited simulation shows that MICE outperforms two-way imputation with respect to Cronbach’s α and the correlations between scales. We conclude that FCS is a promising alternative for imputing incomplete questionnaire items.
Missing Data: A Unified Taxonomy Guided by Conditional Independence
