Scoring and classifying examinees using measurement decision theory
DOI
https://doi.org/10.7275/vksg-rh07
Abstract
This paper describes and evaluates the use of measurement decision theory (MDT) to classify examinees based on their item response patterns. The model has a simple framework that starts with the conditional probabilities of examinees in each category or mastery state responding correctly to each item. The presented evaluation investigates: (1) the classification accuracy of tests scored using decision theory; (2) the effectiveness of different sequential testing procedures; and (3) the number of items needed to make a classification. A large percentage of examinees can be classified accurately with very few items using decision theory. A Java Applet for self instruction and software for generating, calibrating and scoring MDT data are provided. Accessed 13,741 times on https://pareonline.net from April 11, 2009 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Rudner, Lawrence M.
(2019)
"Scoring and classifying examinees using measurement decision theory,"
Practical Assessment, Research, and Evaluation: Vol. 14, Article 8.
DOI: https://doi.org/10.7275/vksg-rh07
Available at:
https://scholarworks.umass.edu/pare/vol14/iss1/8