Consequences of Ignoring Guessing when Estimating the Latent Density in Item Response Theory

Autor/in	Woods, Carol M.
Titel	Consequences of Ignoring Guessing when Estimating the Latent Density in Item Response Theory
Quelle	In: Applied Psychological Measurement, 32 (2008) 5, S.371-384 (14 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621607307691
Schlagwörter	Simulation; Item Response Theory; Monte Carlo Methods; Comparative Analysis; Goodness of Fit; Guessing (Tests); Item Analysis; Evaluation Methods + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Item-Response-Theorie; Monte-Carlo-Methode; Erraten; Itemanalyse
Abstract	In Ramsay-curve item response theory (RC-IRT), the latent variable distribution is estimated simultaneously with the item parameters. In extant Monte Carlo evaluations of RC-IRT, the item response function (IRF) used to fit the data is the same one used to generate the data. The present simulation study examines RC-IRT when the IRF is imperfectly matched to the data. In particular, guessing is ignored: The two-parameter logistic IRF is fitted to data generated from the three-parameter logistic IRF. The empirical histogram method (EHM) implemented in the BILOG-MG program is also applied for comparison. Results indicate that apparent nonnormality in a density estimate from either RC-IRT or the EHM can be entirely due to misspecification of the IRF, and it may be difficult to tell which IRF is best when the latent density is estimated. It is recommended that practitioners first identify the best IRF with the latent density fixed at normal and then subsequently examine the normality assumption about the latent density. (Contains 2 figures, 4 tables, and 2 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10