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

Autor/inn/en	Falk, Carl F.; Feuerstahler, Leah M.
Titel	On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests
Quelle	In: Educational and Psychological Measurement, 82 (2022) 1, S.57-75 (19 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Falk, Carl F.) ORCID (Feuerstahler, Leah M.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211014261
Schlagwörter	Item Response Theory; Adaptive Testing; Computer Assisted Testing; Nonparametric Statistics; Mathematics; Comparative Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Mathematik
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01