Literaturnachweis - Detailanzeige
| Autor/inn/en | Zhan, Peida; Jiao, Hong; Man, Kaiwen; Wang, Lijun |
|---|---|
| Titel | Using JAGS for Bayesian Cognitive Diagnosis Modeling: A Tutorial |
| Quelle | In: Journal of Educational and Behavioral Statistics, 44 (2019) 4, S.473-503 (31 Seiten)Infoseite zur Zeitschrift
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| Zusatzinformation | ORCID (Zhan, Peida) |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1076-9986 |
| DOI | 10.3102/1076998619826040 |
| Schlagwörter | Bayesian Statistics; Computer Software; Models; Test Items; Cognitive Measurement; Longitudinal Studies; Markov Processes; Monte Carlo Methods |
| Abstract | In this article, we systematically introduce the just another Gibbs sampler (JAGS) software program to fit common Bayesian cognitive diagnosis models (CDMs) including the deterministic inputs, noisy "and" gate model; the deterministic inputs, noisy "or" gate model; the linear logistic model; the reduced reparameterized unified model; and the log-linear CDM (LCDM). Further, we introduce the unstructured latent structural model and the higher order latent structural model. We also show how to extend these models to consider polytomous attributes, the testlet effect, and longitudinal diagnosis. Finally, we present an empirical example as a tutorial to illustrate how to use JAGS codes in R. (As Provided). |
| Anmerkungen | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2020/1/01 |
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