Literaturnachweis - Detailanzeige
| Autor/in | Monroe, Scott |
|---|---|
| Titel | Estimation of Expected Fisher Information for IRT Models |
| Quelle | In: Journal of Educational and Behavioral Statistics, 44 (2019) 4, S.431-447 (17 Seiten)Infoseite zur Zeitschrift
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| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1076-9986 |
| DOI | 10.3102/1076998619838240 |
| Schlagwörter | Item Response Theory; Error of Measurement; Scoring; Inferences; Monte Carlo Methods; Models; Comparative Analysis; Simulation; Maximum Likelihood Statistics; Probability; Accuracy |
| Abstract | In item response theory (IRT) modeling, the Fisher information matrix is used for numerous inferential procedures such as estimating parameter standard errors, constructing test statistics, and facilitating test scoring. In principal, these procedures may be carried out using either the expected information or the observed information. However, in practice, the expected information is not typically used, as it often requires a large amount of computation. In the present research, two methods to approximate the expected information by Monte Carlo are proposed. The first method is suitable for less complex IRT models such as unidimensional models. The second method is generally applicable but is designed for use with more complex models such as high-dimensional IRT models. The proposed methods are compared to existing methods using real data sets and a simulation study. The comparisons are based on simple structure multidimensional IRT models with two-parameter logistic item models. (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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