In psychology, we often want to know whether or not an effect exists. The traditional way of answering this question is to use frequentist statistics. However, a significance test against a null hypothesis of no effect cannot distinguish between two states of affairs: evidence of absence of an effect and the absence of evidence for or against an effect. Bayes factors can make this distinction; however, uptake of Bayes factors in psychology has so far been low for two reasons. First, they require researchers to specify the range of effect sizes their theory predicts. Researchers are often unsure about how to do this, leading to the use of inappropriate default values which may give misleading results. Second, many implementations of Bayes factors have a substantial technical learning curve. We present a case study and simulations demonstrating a simple method for generating a range of plausible effect sizes, that is, a model of Hypothesis 1, for treatment effects where there is a binary-dependent variable. We illustrate this using mainly the estimates from frequentist logistic mixed-effects models (because of their widespread adoption) but also using Bayesian model comparison with Bayesian hierarchical models (which have increased flexibility). Bayes factors calculated using these estimates provide intuitively reasonable results across a range of real effect sizes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

Logistic（混合效应）模型的贝叶斯因子。


在心理学中，我们经常想知道是否存在效果。回答这个问题的传统方法是使用频率统计。但是，针对无效零假设的显著性检验无法区分两种事态：不存在效应的证据和不存在支持或反对效应的证据。贝叶斯因子可以进行这种区分;然而，到目前为止，贝叶斯因子在心理学中的采用率一直很低，原因有两个。首先，它们要求研究人员指定他们的理论预测的效应大小范围。研究人员通常不确定如何执行此操作，从而导致使用不适当的默认值，这可能会产生误导性结果。其次，贝叶斯因子的许多实现具有相当大的技术学习曲线。我们提供了一个案例研究和模拟，展示了一种简单的方法来生成一系列合理的效应大小，即假设 1 的模型，用于存在二进制因变量的治疗效应。我们主要使用频率主义逻辑混合效应模型的估计值（因为它们被广泛采用）来说明这一点，但也使用贝叶斯模型与贝叶斯分层模型（具有更高的灵活性）的比较来说明这一点。使用这些估计值计算的贝叶斯因子可在一系列实际效应大小中提供直观合理的结果。（PsycInfo 数据库记录 （c） 2024 APA，保留所有权利）。