A Comparison of Two Models for Cognitive Diagnosis. Research Report. ETS RR-04-02

Autor/inn/en	Yan, Duanli; Almond, Russell; Mislevy, Robert
Titel	A Comparison of Two Models for Cognitive Diagnosis. Research Report. ETS RR-04-02
Quelle	In: ETS Research Report Series, (2004), (41 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Comparative Analysis; Models; Bayesian Statistics; Statistical Inference; Networks; Item Response Theory; Measurement Techniques; Psychometrics; Cognitive Tests; Subtraction; Mathematics Tests; Problem Solving; Middle School Students + Suchen Sie Ihr Suchwort? Analogiemodell; Inferential statistics; Schließende Statistik; Item-Response-Theorie; Messtechnik; Psychometry; Psychometrie; Kognitiver Fähigkeitstest; Subtraktion; Problemlösen; Middle school; Middle schools; Student; Students; Mittelschule; Mittelstufenschule; Schüler; Schülerin
Abstract	Diagnostic score reports linking assessment outcomes to instructional interventions are one of the most requested features of assessment products. There is a body of interesting work done in the last 20 years including Tatsuoka's rule space method (Tatsuoka, 1983), Haertal and Wiley's binary skills model (Haertal, 1984; Haertal & Wiley, 1993), and Mislevy, Almond, Yan, and Steinberg's Bayesian inference network (Mislevy, Almond, Yan, & Steinberg, 1999). Recent research has resulted in major breakthroughs for the use of a parametric IRT model for performing skills diagnoses. Hartz, Roussos, and Stout (2002) have developed an identifiable and flexible model called the fusion model. This paper compares the fusion model and the Bayesian inference network in the design and analysis of an assessment, including the Q-matrix, and then compares other related models, including rule space, item response theory (IRT), and general multivariate latent class models. The paper also attempts to characterize the kinds of problems for which each type of measurement model is well-suited. A general Bayesian psychometric framework provides common language, making it easier to appreciate the differences. In addition, this paper explores some of the strengths and weaknesses of each approach based on a comparative analysis of a cognitive assessment, the mixed number subtraction data set. In this case, both the fusion model and Bayesian network approaches yield similar performance characteristics and also seem to pick up on different characteristics. (As Provided).
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Update	2020/1/01