An Application of the Response Vector for Mastery Method of Standard Setting in Comparison with the Extended-Angoff and Cluster Analysis Methods

This paper presents a real-data application of a recently developed method of standard setting, referred to as the response vector for mastery (RVM) method, and compares its performance with the popular Angoff method (an extended version) and cluster analysis method (CAM). The RVM enables the derivation of cut-scores on the bounded D-scale (ranging from 0 to 1), under the D-scoring method of measurement, and the logit scale of item response theory (IRT). Using a 25-item multiple-choice test in the field of educational measurement, the study demonstrates that the RVM method offers key advantages over Angoff and CAM methods, including reduced judgmental burden for panelists, greater classification accuracy and consistency, and flexibility in computing cut-scores at the domain (content area) level. Given its novel structure and promising empirical performance, the RVM method holds strong potential for researchers and practitioners seeking practical, defensible, and scalable approaches to standard setting in educational assessment contexts.