Subjective Priors for Item Response Models: Application of Elicitation by Design

Autor/inn/en	Ames, Allison; Smith, Elizabeth
Titel	Subjective Priors for Item Response Models: Application of Elicitation by Design
Quelle	In: Journal of Educational Measurement, 55 (2018) 3, S.373-402 (30 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0022-0655
DOI	10.1111/jedm.12184
Schlagwörter	Item Response Theory; Bayesian Statistics; Ethics; Specialists; Markov Processes; Monte Carlo Methods; Check Lists; Comparative Analysis; Difficulty Level; Guessing (Tests); Item Analysis + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Ethik; Markowscher Prozess; Monte-Carlo-Methode; Checkliste; Schwierigkeitsgrad; Erraten; Itemanalyse
Abstract	Bayesian methods incorporate model parameter information prior to data collection. Eliciting information from content experts is an option, but has seen little implementation in Bayesian item response theory (IRT) modeling. This study aims to use ethical reasoning content experts to elicit prior information and incorporate this information into Markov Chain Monte Carlo (MCMC) estimation. A six-step elicitation approach is followed, with relevant details at each stage for two IRT items parameters: difficulty and guessing. Results indicate that using content experts is the preferred approach, rather than noninformative priors, for both parameter types. The use of a noninformative prior for small samples provided dramatically different results when compared to results from content expert--elicited priors. The WAMBS (When to worry and how to Avoid the Misuse of Bayesian Statistics) checklist is used to aid in comparisons. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01