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Classification Accuracy and Consistency under Item Response Theory Models Using the Package classify

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  • Wheadon, Chris

Abstract

The R package classify presents a number of useful functions which can be used to estimate the classification accuracy and consistency of assessments. Classification accuracy refers to the probability that an examinee’s achieved grade classification on an assessment reflects their true grade. Classification consistency refers to the probability that an examinee will be classified into the same grade classification under repeated administrations of an assessment. Understanding the classification accuracy and consistency of assessments is important where key decisions are being taken on the basis of grades or classifications. The study of classification accuracy can help to improve the design of assessments and aid public understanding and confidence in those assessments.

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  • Wheadon, Chris, 2014. "Classification Accuracy and Consistency under Item Response Theory Models Using the Package classify," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(i10).
    • Handle: RePEc:jss:jstsof:v:056:i10
    DOI: http://hdl.handle.net/10.18637/jss.v056.i10
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    References listed on IDEAS

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    1. Karen M. Douglas & Robert J. Mislevy, 2010. "Estimating Classification Accuracy for Complex Decision Rules Based on Multiple Scores," Journal of Educational and Behavioral Statistics, , vol. 35(3), pages 280-306, June.
    2. Curtis, S. McKay, 2010. "BUGS Code for Item Response Theory," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(c01).
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