Kernel Smoothing Item Response Theory in R: A Didactic

Autor/inn/en	Effatpanah, Farshad; Baghaei, Purya
Titel	Kernel Smoothing Item Response Theory in R: A Didactic
Quelle	In: Practical Assessment, Research & Evaluation, 28 (2023), Artikel 7 (28 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	ORCID (Effatpanah, Farshad) ORCID (Baghaei, Purya)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
Schlagwörter	Item Response Theory; Feedback (Response); Mathematical Models; Item Analysis; Psychological Testing; Educational Assessment; Test Anxiety; Children; Measures (Individuals); Factor Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Mathematical model; Mathematisches Modell; Itemanalyse; Psychological test; psychological tests; Psychological examination; Psychologischer Test; Education; assessment; Bewertungssystem; Examination phobia; Testangst; Prüfungsangst; Child; Kind; Kinder; Messdaten; Faktorenanalyse
Abstract	Item response theory (IRT) refers to a family of mathematical models which describe the relationship between latent continuous variables (attributes or characteristics) and their manifestations (dichotomous/polytomous observed outcomes or responses) with regard to a set of item characteristics. Researchers typically use parametric IRT (PIRT) models to measure educational and psychological latent variables. However, PIRT models are based on a set of strong assumptions that often are not satisfied. For this reason, non-parametric IRT (NIRT) models can be more desirable. An exploratory NIRT approach is kernel smoothing IRT (KS-IRT; Ramsay, 1991) which estimates option characteristic curves by non-parametric kernel smoothing technique. This approach only gives graphical representations of item characteristics in a measure and provides preliminary feedback about the performance of items and measures. Although KS-IRT is not a new approach, its application is far from widespread, and it has limited applications in psychological and educational testing. The purpose of the present paper is to give a reader-friendly introduction to the KS-IRT, and then use the KernSmoothIRT package (Mazza et al., 2014, 2022) in R to straightforwardly demonstrate the application of the approach using data of Children's Test Anxiety scale. (As Provided).
Anmerkungen	Center for Educational Assessment. 813 North Pleasant Street, Amherst, MA 01002. e-mail: pare@umass.edu; Tel: 413-577-2180; Web site: https://scholarworks.umass.edu/pare
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01