PDF(Pubmed)
Abstract：
An overwhelming majority of articles in psychology compare means, often between multiple groups. However, sometimes we do not know the exact group membership, but only a probability to be in one of the groups. Such information may come from classifiers trained on other datasets, prevalence of group memberships for some parts of the sample, multi-level situations where the group membership is only known as a ratio in an upper level, or expert ratings (e.g., whether a person has a pathological condition or not). We present a simple method that allows to compare group means in the absence of exact knowledge about group membership and investigate the loss of information depending on the probability values theoretically and in a large-scale simulation.
摘要：
绝大多数心理学文章比较手段，通常在多个群体之间。然而,有时我们不知道确切的组成员身份，但只是其中一个群体的可能性。这些信息可能来自在其他数据集上训练的分类器，样本某些部分的组成员身份的患病率，多级别的情况下，组成员资格只被称为上层的比率，或专家评级(例如，一个人是否有病理状况)。我们提出了一种简单的方法，该方法可以在缺乏有关组成员身份的确切知识的情况下比较组均值，并根据理论上和大规模模拟中的概率值调查信息丢失。
