Calibrating Multidimensional Assessments With Structural Missingness: An Application of a Multiple-Group Higher-Order IRT Model

Abstract

Educational Constructs are becoming increasingly complex and are often conceptualized at both a general level and a subdomain level. It is often desirable to report scores from both levels simultaneously. However, to measure such complex constructs, a very large item bank that is hard for a student to complete in any reasonable timeframe is needed. Furthermore, most current score reporting practices either only report subdomain scores, or the general domain score is calculated post hoc. We propose that a multiple group HO-IRT model with structural missingness can be used to simultaneously report general and subdomain scores while controlling assessment length. Although the model itself is not new, we consider a novel application scenario using a NEAT design with both a representative and non-representative anchor test. While a representative anchor test is recommended in literature, it is sometimes unrealistic in practice when the multidimensional construct shifts over time. Hence, exploring the parameter recovery of multiple group HO-IRT in the presence of non-representative anchor test is especially interesting and important. We show, through Monte Carlo simulation, that the RMSE of IRT estimates retrieved under a non-representative anchor item set with a moderate correlation between the higher- and lower-order factors, is comparable to the RMSE of IRT estimates retrieved under a representative anchor item set. Missing data were addressed using a full-information maximum likelihood approach to parameter estimation.