Higher-Order Item Response Models for Hierarchical Latent Traits

Autor/inn/en	Huang, Hung-Yu; Wang, Wen-Chung; Chen, Po-Hsi; Su, Chi-Ming
Titel	Higher-Order Item Response Models for Hierarchical Latent Traits
Quelle	In: Applied Psychological Measurement, 37 (2013) 8, S.619-637 (19 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621613488819
Schlagwörter	Item Response Theory; Models; Vertical Organization; Bayesian Statistics; Markov Processes; Monte Carlo Methods; Computer Software; Computation; Achievement Tests; Personality Assessment; Junior High School Students; Internet; Addictive Behavior; Psychological Patterns; College Students; Factor Analysis; Foreign Countries; Taiwan + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Analogiemodell; Markowscher Prozess; Monte-Carlo-Methode; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Persönlichkeitsanalyse; Junior High Schools; Student; Students; Sekundarstufe I; Schüler; Schülerin; Collegestudent; Faktorenanalyse; Ausland
Abstract	Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10