Abstract：
The discriminability measure d \' is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate d \' involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate d \' , but these corrections distort the estimate. In three simulation studies, we show that distortion in d \' 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 \' 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 \' estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates d \' distortion, providing researchers the means to identify distortion and take steps to minimize its impact.
摘要：
判别性度量d\'在心理学中被广泛用于独立于反应偏差来估计灵敏度。估计d'的常规方法涉及从命中率和误报率的变换。当性能完美时，必须应用校正方法来计算d'，但是这些修正扭曲了估计。在三个模拟研究中，我们表明，d'估计中的失真可能来自实验设计的其他属性(试验次数，样本量,样本方差，任务难度)，当结合校正方法的应用时，使任何特定实验设计中的d\'失真复杂，并可能在最坏的情况下（I型和II型错误）误导统计推断。为了解决这个问题,我们建议研究人员模拟d'估计来探索设计选择的影响，给定预期或观察到的数据。介绍了一种RShiny应用程序，用于估计d'失真，为研究人员提供识别失真并采取措施将其影响降至最低的手段。
