IRT-based classification analysis of an english language reading proficiency subtest

Date
2022
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Source Title
Language Testing
Print ISSN
0265-5322
Electronic ISSN
1477-0946
Publisher
SAGE
Volume
39
Issue
4
Pages
541 - 566
Language
English
Type
Article
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Abstract

Language proficiency testing serves an important function of classifying examinees into different categories of ability. However, misclassification is to some extent inevitable and may have important consequences for stakeholders. Recent research suggests that classification efficacy may be enhanced substantially using computerized adaptive testing (CAT). Using real data simulations, the current study investigated the classification performance of CAT on the reading section of an English language proficiency test and made comparisons with the paper based version of the same test. Classification analysis was carried out to estimate classification accuracy (CA) and classification consistency (CC) by applying different locations and numbers of cutoff points. The results showed that classification was suitable when a single cutoff score was used, particularly for high- and low-ability test takers. Classification performance declined significantly when multiple cutoff points were simultaneously employed. Content analysis also raised important questions about construct coverage in CAT. The results highlight the potential for CAT to serve classification purposes and outline avenues for further research.

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Keywords
classification accuracy, classification consistency, computerized adaptive testing, language proficiency, Rudner approach
Citation
Published Version (Please cite this version)