ERIC Number: EJ933661
Record Type: Journal
Publication Date: 2009-Apr
Pages: 12
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-1531-7714
EISSN: N/A
Scoring and Classifying Examinees Using Measurement Decision Theory
Rudner, Lawrence M.
Practical Assessment, Research & Evaluation, v14 n8 Apr 2009
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. (Contains 1 figure, 6 tables, 2 notes, and 1 footnote.)
Descriptors: Classification, Scoring, Item Response Theory, Measurement, Models, Bayesian Statistics, Maximum Likelihood Statistics, Probability, Tests, Accuracy, Test Items
Dr. Lawrence M. Rudner. e-mail: editor@pareonline.net; Web site: http://pareonline.net
Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A