Bayesian Analysis Methods for Two-Level Diagnosis Classification Models

Autor/in	Yamaguchi, Kazuhiro
Titel	Bayesian Analysis Methods for Two-Level Diagnosis Classification Models
Quelle	In: Journal of Educational and Behavioral Statistics, 48 (2023) 6, S.773-809 (37 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Yamaguchi, Kazuhiro)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/10769986231173594
Schlagwörter	Bayesian Statistics; Classification; Statistical Inference; Sampling; Markov Processes; Monte Carlo Methods; Diagnostic Tests; Models; Achievement Tests; Elementary Secondary Education; Foreign Countries; Science Achievement; Mathematics Achievement; Mathematics Tests; International Assessment; Science Tests; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Inferential statistics; Schließende Statistik; Markowscher Prozess; Monte-Carlo-Methode; Diagnostic test; Diagnostischer Test; Analogiemodell; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz
Abstract	Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB) inference and Gibbs sampling Markov chain Monte Carlo methods were developed for parameter estimation of the two-level DCMs. The results of a parameter recovery simulation study show that both techniques appropriately recovered the true parameters; Gibbs sampling in particular was slightly more accurate than VB, whereas VB performed estimation much faster than Gibbs sampling. The two-level DCMs with the proposed Bayesian estimation methods were further applied to fourth-grade data obtained from the Trends in International Mathematics and Science Study 2007 and indicated that mathematical activities in the classroom could be organized into four latent classes, with each latent class connected to different attribute mastery patterns. This information can be employed in educational intervention to focus on specific latent classes and elucidate attribute patterns. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01