Generalized linear mixed modeling of signal detection theory

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dc.contributor.authorRabe, Maximilian Michael
dc.contributor.supervisorLindsay, D. Stephen
dc.contributor.supervisorMasson, Michael E. J.
dc.date.accessioned2018-04-10T14:48:19Z
dc.date.copyright2018en_US
dc.date.issued2018-04-10
dc.degree.departmentDepartment of Psychologyen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractSignal Detection Theory (SDT; Green & Swets, 1966) is a well-established technique to analyze accuracy data in a number of experimental paradigms in psychology, most notably memory and perception, by separating a response bias/criterion from the theoretically bias-free discriminability/sensitivity. As SDT has traditionally been applied, the researcher may be confronted with loss in statistical power and erroneous inferences. A generalized linear mixed-effects modeling (GLMM) approach is presented and advantages with regard to power and precision are demonstrated with an example analysis. Using this approach, a correlation of response bias and sensitivity was detected in the dataset, especially prevalent at the item level, though a correlation between these measures is usually not found to be reported in the memory literature. Directions for future extensions of the method as well as a brief discussion of the correlation between response bias and sensitivity are enclosed.en_US
dc.description.embargo2019-03-22
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/9208
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectmethodologyen_US
dc.subjectcognitive psychologyen_US
dc.subjectrecognition memoryen_US
dc.subjectsignal detection theoryen_US
dc.subjectgeneralized linear mixed modelsen_US
dc.subjectstatisticsen_US
dc.titleGeneralized linear mixed modeling of signal detection theoryen_US
dc.typeThesisen_US

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