Identifying and Classifying Aberrant Response Patterns through Functional Data Analysis

Autor/inn/en	Doval, Eduardo; Delicado, Pedro
Titel	Identifying and Classifying Aberrant Response Patterns through Functional Data Analysis
Quelle	In: Journal of Educational and Behavioral Statistics, 45 (2020) 6, S.719-749 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Doval, Eduardo) ORCID (Delicado, Pedro)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998620911941
Schlagwörter	Response Style (Tests); Data Analysis; Identification; Classification; Science Tests; Grade 12; Nonparametric Statistics + Suchen Sie Ihr Suchwort? Antwortverhalten; Auswertung; Identifikation; Identifizierung; Classification system; Klassifikation; Klassifikationssystem; School year 12; 12. Schuljahr; Schuljahr 12
Abstract	We propose new methods for identifying and classifying aberrant response patterns (ARPs) by means of functional data analysis. These methods take the person response function (PRF) of an individual and compare it with the pattern that would correspond to a generic individual of the same ability according to the item-person response surface. ARPs correspond to atypical difference functions. The ARP classification is done with functional data clustering applied to the PRFs identified as ARP. We apply these methods to two sets of simulated data (the first is used to illustrate the ARP identification methods and the second demonstrates classification of the response patterns flagged as ARP) and a real data set (a Grade 12 science assessment test, SAT, with 32 items answered by 600 examinees). For comparative purposes, ARPs are also identified with three nonparametric person-fit indices (Ht, Modified Caution Index, and ZU3). Our results indicate that the ARP detection ability of one of our proposed methods is comparable to that of person-fit indices. Moreover, the proposed classification methods enable ARP associated with either spuriously low or spuriously high scores to be distinguished. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01