Identifying Response Styles Using Person Fit Analysis and Response-Styles Models

Autor/inn/en	Wind, Stefanie A.; Ge, Yuan
Titel	Identifying Response Styles Using Person Fit Analysis and Response-Styles Models
Quelle	In: Measurement: Interdisciplinary Research and Perspectives, 21 (2023) 3, S.147-166 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Wind, Stefanie A.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1536-6367
DOI	10.1080/15366367.2022.2104565
Schlagwörter	Goodness of Fit; Responses; Likert Scales; Models; Evaluation Methods; Item Analysis; Difficulty Level; Simulation + Suchen Sie Ihr Suchwort? Likert-Skala; Analogiemodell; Itemanalyse; Schwierigkeitsgrad; Simulation program; Simulationsprogramm
Abstract	In selected-response assessments such as attitude surveys with Likert-type rating scales, examinees often select from rating scale categories to reflect their locations on a construct. Researchers have observed that some examinees exhibit "response styles," which are systematic patterns of responses in which examinees are more likely to select certain response categories, regardless of their locations on the construct (Baumgartner & Steenkamp, 2001; Paulhus, 1991; Roberts, 2016; Van Vaerenbergh & Thomas, 2013). To identify and minimize construct-irrelevant impacts of response styles, researchers have proposed tools such as the Partial Credit Model -- Response Style (PCMRS; Tutz et al., 2018) as an extension of the Partial credit model (PCM; Masters, 1982) to model the tendency for examinees to exhibit response styles. The PCMRS directly models response styles as a person-specific gamma parameter and corrects estimates of item difficulty for the presence of response styles. In this study, the authors describe details about the PCMRS model parameters and explore the correspondence between indicators of response styles from the PCMRS and person fit statistics that reflect the Rasch measurement framework to identify examinees who exhibit midpoint and extreme response styles. Findings suggest that researchers and practitioners who aim to identify response styles as a type of construct-irrelevant variance can do so using measurement models such as the PCM. (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01