Identifying and Classifying Aberrant Response Patterns Through Functional Data Analysis
Doval Dieguez, Eduardo (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut)
Delicado, Pedro (Universitat Autònoma de Barcelona. Departament de Psicobiologia i de Metodologia de Ciències de la Salut)

Publicació:	Elsevier 2020
Descripció:	31 pàg.
Resum:	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.
Ajuts:	Ministerio de Economía y Competitividad MTM2017-88142-P
Drets:	Tots els drets reservats.
Llengua:	Anglès
Document:	Article ; recerca ; Versió acceptada per publicar
Matèria:	Functional clustering ; Functional data analysis ; Item-person response surface ; Outlier detection ; Person response function ; Person-fit
Publicat a:	Journal of Educational and Behavioral Statistics, Vol. 45 Núm. 6 (2020) , p. 719-749, ISSN 1935-1054

DOI: 10.3102/1076998620911941

43 p, 23.3 MB

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