Fitting the Reduced RUM with Mplus: A Tutorial

Autor/inn/en	Chiu, Chia-Yi; Köhn, Hans-Friedrich; Wu, Huey-Min
Titel	Fitting the Reduced RUM with Mplus: A Tutorial
Quelle	In: International Journal of Testing, 16 (2016) 4, S.331-351 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1530-5058
DOI	10.1080/15305058.2016.1148038
Schlagwörter	Leitfaden; Educational Diagnosis; Classification; Models; Educational Assessment; Psychometrics; Computation; Computer Software; Mathematics; Multivariate Analysis; Tutorial Programs; Maximum Likelihood Statistics; Equations (Mathematics); Item Response Theory; Bayesian Statistics + Suchen Sie Ihr Suchwort? Pedagogical diagnostics; Pädagogische Diagnostik; Classification system; Klassifikation; Klassifikationssystem; Analogiemodell; Education; assessment; Bewertungssystem; Psychometry; Psychometrie; Mathematik; Multivariate Analyse; Tutorial programmes; Förderprogramm; Lernprogramm; Tutorensystem; Equations; Mathematics; Gleichungslehre; Item-Response-Theorie
Abstract	The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own code, are still rather limited. One option is to use a commercial software package that offers an implementation of the expectation maximization (EM) algorithm for fitting (constrained) latent class models like Latent GOLD or Mplus. But using a latent class analysis routine as a vehicle for fitting the Reduced RUM requires that it be re-expressed as a logit model, with constraints imposed on the parameters of the logistic function. This tutorial demonstrates how to implement marginal maximum likelihood estimation using the EM algorithm in Mplus for fitting the Reduced RUM. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01