Exploring Incomplete Rating Designs With Mokken Scale Analysis

Recent research has explored the use of models adapted from Mokken scale analysis as a nonparametric approach to evaluating rating quality in educational performance assessments. A potential limiting factor to the widespread use of these techniques is the requirement for complete data, as practical constraints in operational assessment systems often limit the use of complete rating designs. In order to address this challenge, this study explores the use of missing data imputation techniques and their impact on Mokken-based rating quality indicators related to rater monotonicity, rater scalability, and invariant rater ordering. Simulated data and real data from a rater-mediated writing assessment were modified to reflect varying levels of missingness, and four imputation techniques were used to impute missing ratings. Overall, the results indicated that simple imputation techniques based on rater and student means result in generally accurate recovery of rater monotonicity indices and rater scalability coefficients. However, discrepancies between violations of invariant rater ordering in the original and imputed data are somewhat unpredictable across imputation methods. Implications for research and practice are discussed.