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ERIC Number: EJ1092443
Record Type: Journal
Publication Date: 2016
Pages: 22
Abstractor: As Provided
ISSN: ISSN-0022-0655
Does Maximizing Information at the Cut Score Always Maximize Classification Accuracy and Consistency?
Wyse, Adam E.; Babcock, Ben
Journal of Educational Measurement, v53 n1 p23-44 Spr 2016
A common suggestion made in the psychometric literature for fixed-length classification tests is that one should design tests so that they have maximum information at the cut score. Designing tests in this way is believed to maximize the classification accuracy and consistency of the assessment. This article uses simulated examples to illustrate that one can obtain higher classification accuracy and consistency by designing tests that have maximum test information at locations other than at the cut score. We show that the location where one should maximize the test information is dependent on the length of the test, the mean of the ability distribution in comparison to the cut score, and, to a lesser degree, whether or not one wants to optimize classification accuracy or consistency. Analyses also suggested that the differences in classification performance between designing tests optimally versus maximizing information at the cut score tended to be greatest when tests were short and the mean of ability distribution was further away from the cut score. Larger differences were also found in the simulated examples that used the 3PL model compared to the examples that used the Rasch model.
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A