ArticleEducational and psychological measurement2022
On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests.
Article in Educational and psychological measurement, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Iterative Item Selection of Neighborhood Clusters: A Nonparametric and Non-IRT Method for Generating Miniature Computer Adaptive Questionnaires.Educational and psychological measurement · 2024Article
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2 authors.
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Abstract
Large-scale assessments often use a computer adaptive test (CAT) for selection of items and for scoring respondents. Such tests often assume a parametric form for the relationship between item responses and the underlying construct. Although semi- and nonparametric response functions could be used, there is scant research on their performance in a CAT. In this work, we compare parametric response functions versus those estimated using kernel smoothing and a logistic function of a monotonic polynomial. Monotonic polynomial items can be used with traditional CAT item selection algorithms that use analytical derivatives. We compared these approaches in CAT simulations with a variety of item selection algorithms. Our simulations also varied the features of the calibration and item pool: sample size, the presence of missing data, and the percentage of nonstandard items. In general, the results support the use of semi- and nonparametric item response functions in a CAT.
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