On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests.

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

SUBMITTER: Falk CF 

PROVIDER: S-EPMC8721622 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature