Signal Detection Models with Random Participant and Item Effects

Autor/inn/en	Rouder, Jeffrey N.; Lu, Jun; Sun, Dongchu; Speckman, Paul; Morey, Richard; Naveh-Benjamin, Moshe
Titel	Signal Detection Models with Random Participant and Item Effects
Quelle	In: Psychometrika, 72 (2007) 4, S.621-642 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-3123
DOI	10.1007/s11336-005-1350-6
Schlagwörter	Simulation; Recognition (Psychology); Computation; Mnemonics; Teaching Methods; Models; Theories; Memory; Data Analysis; Bayesian Statistics + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Recognition; Wiedererkennen; Mnemotechnik; Teaching method; Lehrmethode; Unterrichtsmethode; Analogiemodell; Theory; Theorie; Gedächtnis; Auswertung
Abstract	The theory of signal detection is convenient for measuring mnemonic ability in recognition memory paradigms. In these paradigms, randomly selected participants are asked to study randomly selected items. In practice, researchers aggregate data across items or participants or both. The signal detection model is nonlinear; consequently, analysis with aggregated data is not consistent. In fact, mnemonic ability is underestimated, even in the large-sample limit. We present two hierarchical Bayesian models that simultaneously account for participant and item variability. We show how these models provide for accurate estimation of participants' mnemonic ability as well as the memorability of items. The model is benchmarked with a simulation study and applied to a novel data set. (Author).
Anmerkungen	Springer. 233 Spring Street, New York, NY 10013. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-348-4505; e-mail: service-ny@springer.com; Web site: http://www.springerlink.com
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10