Different Approaches to Covariate Inclusion in the Mixture Rasch Model

Autor/inn/en	Li, Tongyun; Jiao, Hong; Macready, George B.
Titel	Different Approaches to Covariate Inclusion in the Mixture Rasch Model
Quelle	In: Educational and Psychological Measurement, 76 (2016) 5, S.848-872 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164415610380
Schlagwörter	Item Response Theory; Psychometrics; Test Construction; Monte Carlo Methods; Models; Bayesian Statistics; Research Problems; Error of Measurement; Statistical Bias; Regression (Statistics); Effect Size; Markov Processes + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Psychometry; Psychometrie; Testaufbau; Monte-Carlo-Methode; Analogiemodell; Forschungskritik; Messfehler; Regression; Regressionsanalyse; Markowscher Prozess
Abstract	The present study investigates different approaches to adding covariates and the impact in fitting mixture item response theory models. Mixture item response theory models serve as an important methodology for tackling several psychometric issues in test development, including the detection of latent differential item functioning. A Monte Carlo simulation study is conducted in which data generated according to a two-class mixture Rasch model with both dichotomous and continuous covariates are fitted to several mixture Rasch models with misspecified covariates to examine the effects of covariate inclusion on model parameter estimation. In addition, both complete response data and incomplete response data with different types of missingness are considered in the present study in order to simulate practical assessment settings. Parameter estimation is carried out within a Bayesian framework vis-à-vis Markov chain Monte Carlo algorithms. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01