Generalized linear mixed modeling of signal detection theory
Date
2018-04-10
Authors
Rabe, Maximilian Michael
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Abstract
Signal 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.
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Keywords
methodology, cognitive psychology, recognition memory, signal detection theory, generalized linear mixed models, statistics