A Multicomponent Latent Trait Model for Diagnosis

Autor/inn/en	Embretson, Susan E.; Yang, Xiangdong
Titel	A Multicomponent Latent Trait Model for Diagnosis
Quelle	In: Psychometrika, 78 (2013) 1, S.14-36 (23 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-012-9296-y
Schlagwörter	Mathematics Achievement; Achievement Tests; Item Response Theory; Measurement; Educational Diagnosis; Diagnostic Tests; Educational Testing; Psychometrics; Test Items; Simulation; Measurement Techniques; Comparative Analysis; Data Analysis + Suchen Sie Ihr Suchwort? Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Item-Response-Theorie; Messverfahren; Pedagogical diagnostics; Pädagogische Diagnostik; Diagnostic test; Diagnostischer Test; Psychometry; Psychometrie; Test content; Testaufgabe; Simulation program; Simulationsprogramm; Messtechnik; Auswertung
Abstract	This paper presents a noncompensatory latent trait model, the multicomponent latent trait model for diagnosis (MLTM-D), for cognitive diagnosis. In MLTM-D, a hierarchical relationship between components and attributes is specified to be applicable to permit diagnosis at two levels. MLTM-D is a generalization of the multicomponent latent trait model (MLTM; Whitely in "Psychometrika," 45:479-494, 1980; Embretson in "Psychometrika," 49:175-186, 1984) to be applicable to measures of broad traits, such as achievement tests, in which component structure varies "between" items. Conditions for model identification are described and marginal maximum likelihood estimators are presented, along with simulation data to demonstrate parameter recovery. To illustrate how MLTM-D can be used for diagnosis, an application to a large-scale test of mathematics achievement is presented. An advantage of MLTM-D for diagnosis is that it may be more applicable to large-scale assessments with more heterogeneous items than are latent class models. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10