Assessing the distortions introduced when calculating d': A simulation approach.

Abstract

The discriminability measure $d^{\text{'}}$ is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate $d^{\text{'}}$ involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate $d^{\text{'}}$ , but these corrections distort the estimate. In three simulation studies, we show that distortion in $d^{\text{'}}$ 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 $d^{\text{'}}$ 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 $d^{\text{'}}$ estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates $d^{\text{'}}$ distortion, providing researchers the means to identify distortion and take steps to minimize its impact.