Measuring Response Style Stability across Constructs with Item Response Trees

Autor/in	Ames, Allison J.
Titel	Measuring Response Style Stability across Constructs with Item Response Trees
Quelle	In: Educational and Psychological Measurement, 82 (2022) 2, S.281-306 (26 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Ames, Allison J.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/00131644211020103
Schlagwörter	Response Style (Tests); Individual Differences; Scores; Test Items; Test Bias; Reaction Time; Item Response Theory; Social Media; Climate; Drug Therapy; Marijuana; Likert Scales; Comparative Analysis; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Antwortverhalten; Individueller Unterschied; Test content; Testaufgabe; Testkritik; Reaktionsvermögen; Item-Response-Theorie; Soziale Medien; Klima; Likert-Skala; Monte-Carlo-Methode
Abstract	Individual response style behaviors, unrelated to the latent trait of interest, may influence responses to ordinal survey items. Response style can introduce bias in the total score with respect to the trait of interest, threatening valid interpretation of scores. Despite claims of response style stability across scales, there has been little research into stability across multiple scales from the beneficial perspective of item response trees. This study examines an extension of the IRTree methodology to include mixed item formats, providing an empirical example of responses to three scales measuring perceptions of social media, climate change, and medical marijuana use. Results show extreme and midpoint response styles were not stable across scales within a single administration and 5-point Likert-type items elicited higher levels of extreme response style than the 4-point items. Latent trait of interest estimation varied, particularly at the lower end of the score distribution, across response style models, demonstrating as appropriate response style model is important for adequate trait estimation using Bayesian Markov chain Monte Carlo estimation. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01