Performance of Nonparametric Person-Fit Statistics with Unfolding versus Dominance Response Models

Autor/inn/en	Reimers, Jennifer; Turner, Ronna C.; Tendeiro, Jorge N.; Lo, Wen-Juo; Keiffer, Elizabeth
Titel	Performance of Nonparametric Person-Fit Statistics with Unfolding versus Dominance Response Models
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 21 (2023) 4, S.232-253 (22 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Reimers, Jennifer)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2023.2165891
Schlagwörter	Goodness of Fit; Nonparametric Statistics; Error of Measurement; Comparative Analysis; Evaluation Methods; Identification; Models; Measurement Techniques; Behavior Patterns; Personality Measures + Suchen Sie Ihr Suchwort? Messfehler; Identifikation; Identifizierung; Analogiemodell; Messtechnik
Abstract	Person-fit analyses are commonly used to detect aberrant responding in self-report data. Nonparametric person fit statistics do not require fitting a parametric test theory model and have performed well compared to other person-fit statistics. However, detection of aberrant responding has primarily focused on dominance response data, thus the effectiveness of person-fit statistics in detecting different aberrant behaviors in ideal point data is unclear. This study compares the performance of nonparametric person-fit statistics in unfolding and dominance model contexts. Results for dominance data indicate that increases in detection rates depend, among other factors, on type of aberrant responding and person-fit statistic used. The detection of aberrant responses in ideal point data was ineffective using four nonparametric person-fit statistics, with slightly higher type I error and power less than 0.25. Additional research is needed to identify or develop nonparametric or parametric person-fit statistics effective for aberrant behavior exhibited in ideal point data. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01