Bayes factors for logistic (mixed-effect) models.

IF 7.6 1区 心理学 Q1 PSYCHOLOGY, MULTIDISCIPLINARY
Catriona Silvey, Zoltan Dienes, Elizabeth Wonnacott
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引用次数: 0

Abstract

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).

在心理学中,我们经常想知道某种效应是否存在。回答这个问题的传统方法是使用频数统计。然而,针对 "无效应 "零假设的显著性检验无法区分两种情况:没有效应的证据和没有支持或反对效应的证据。贝叶斯因子可以做出这种区分;然而,由于两个原因,贝叶斯因子在心理学中的应用至今还很低。首先,贝叶斯因子要求研究人员明确指出其理论所预测的效应大小范围。研究人员往往不知道如何做到这一点,从而导致使用不恰当的默认值,这可能会产生误导性结果。其次,贝叶斯因子的许多实现方法都有很大的技术学习曲线。我们介绍了一个案例研究和模拟实验,展示了一种简单的方法来生成一系列可信的效应大小,即假设 1 模型,用于二元变量依赖的治疗效果。我们主要使用频数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层次模型(具有更大的灵活性)进行比较。使用这些估计值计算出的贝叶斯系数在实际效应大小范围内提供了直观合理的结果。(PsycInfo 数据库记录 (c) 2024 APA,保留所有权利)。
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来源期刊
Psychological methods
Psychological methods PSYCHOLOGY, MULTIDISCIPLINARY-
CiteScore
13.10
自引率
7.10%
发文量
159
期刊介绍: Psychological Methods is devoted to the development and dissemination of methods for collecting, analyzing, understanding, and interpreting psychological data. Its purpose is the dissemination of innovations in research design, measurement, methodology, and quantitative and qualitative analysis to the psychological community; its further purpose is to promote effective communication about related substantive and methodological issues. The audience is expected to be diverse and to include those who develop new procedures, those who are responsible for undergraduate and graduate training in design, measurement, and statistics, as well as those who employ those procedures in research.
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