On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests.
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,...
| Publicado en: | Educational & Psychological Measurement Vol. 82; no. 1; pp. 57 - 76 |
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| Autores principales: | , |
| Formato: | Artículo |
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Sage Publications Inc.
Feb2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=154431105&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 154431105 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00131644 EPM jtl: Educational & Psychological Measurement issn: 00131644 maglogo: Y pubinfo: dt: Feb2022 vid: 82 iid: 1 pid: 344 pub: Sage Publications Inc. artinfo: ui: 154431105 10.1177/00131644211014261 ppf: 57 ppct: 19 formats: tig: atl: On the Performance of Semi- and Nonparametric Item Response Functions in Computer Adaptive Tests. aug: au: Falk, Carl F. Feuerstahler, Leah M. affil: McGill University, Montreal, Quebec, Canada Fordham University, Bronx, NY, USA su: Computer adaptive testing Nonparametric statistics Sample size (Statistics) Calibration Research funding Descriptive statistics Algorithms sug: subj: Computer adaptive testing Nonparametric statistics Sample size (Statistics) Calibration Research funding Descriptive statistics Algorithms keyword: computer adaptive test large-scale testing monotonic polynomial nonparametric IRT computer adaptive test large-scale testing monotonic polynomial nonparametric IRT ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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