Abstract
The discriminability measure is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate , but these corrections distort the estimate. In three simulation studies, we show that distortion in estimation can arise from other properties of the experimental design (number of trials, sample size, sample variance, task difficulty) that, when combined with application of the correction method, make distortion in any specific experiment design complex and can mislead statistical inference in the worst cases (Type I and Type II errors). To address this problem, we propose that researchers simulate estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates distortion, providing researchers the means to identify distortion and take steps to minimize its impact.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call