Mixture Rasch Models with Joint Maximum Likelihood Estimation

Autor/in	Willse, John T.
Titel	Mixture Rasch Models with Joint Maximum Likelihood Estimation
Quelle	In: Educational and Psychological Measurement, 71 (2011) 1, S.5-19 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164410387335
Schlagwörter	Item Response Theory; Maximum Likelihood Statistics; Computation; College Seniors; Student Surveys; Models + Suchen Sie Ihr Suchwort? Item-Response-Theorie; College; Colleges; Senior; Hochschule; Fachhochschule; Schülerbefragung; Analogiemodell
Abstract	This research provides a demonstration of the utility of mixture Rasch models. Specifically, a model capable of estimating a mixture partial credit model using joint maximum likelihood is presented. Like the partial credit model, the mixture partial credit model has the beneficial feature of being appropriate for analysis of assessment data containing any combination of dichotomous and polytomous item types. Mixture Rasch models are able to provide information regarding latent classes (subpopulations without manifest grouping variables) and separate item parameter estimates for each of these latent classes. In this research, the step parameters were constrained to be equal across items, making the model a mixture rating scale model. An analysis with simulated data provides a clear example demonstration followed by a real-world analysis and interpretation of student survey data. (Contains 1 figure and 4 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10