Generalized IRT Models for Extreme Response Style

Autor/inn/en	Jin, Kuan-Yu; Wang, Wen-Chung
Titel	Generalized IRT Models for Extreme Response Style
Quelle	In: Educational and Psychological Measurement, 74 (2014) 1, S.116-138 (23 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164413498876
Schlagwörter	Item Response Theory; Research Methodology; Bayesian Statistics; Response Style (Tests); Likert Scales; Rating Scales; Maximum Likelihood Statistics; Computation; Computer Simulation; Equations (Mathematics); Conflict; Interpersonal Relationship; Student Surveys; Citizenship Education + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Research method; Forschungsmethode; Antwortverhalten; Likert-Skala; Rating-Skala; Computergrafik; Computersimulation; Equations; Mathematics; Gleichungslehre; Konflikt; Interpersonal relation; Interpersonal relations; Interpersonelle Beziehung; Zwischenmenschliche Beziehung; Schülerbefragung; Citizenship; Education; Politische Bildung; Politische Erziehung; Staatsbürgerliche Erziehung
Abstract	Extreme response style (ERS) is a systematic tendency for a person to endorse extreme options (e.g., strongly disagree, strongly agree) on Likert-type or rating-scale items. In this study, we develop a new class of item response theory (IRT) models to account for ERS so that the target latent trait is free from the response style and the tendency of ERS is quantified. Parameters of these new models can be estimated with marginal maximum likelihood estimation methods or Bayesian methods. In this study, we use the freeware program WinBUGS, which implements Bayesian methods. In a series of simulations, we find that the parameters are recovered fairly well; ignoring ERS by fitting standard IRT models resulted in biased estimates, and fitting the new models to data without ERS did little harm. Two empirical examples are provided to illustrate the implications and applications of the new models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10