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引用次数: 0
摘要
因子设计适合采用贝叶斯方法进行各种分析。事实上的标准是采用蒙特卡罗积分的贝叶斯方差分析(ANOVA)。另一种现成的方法是采用拉普拉斯近似的贝叶斯方差分析以及针对个体效应的贝叶斯 t 检验。本模拟研究比较了这三种方法在 2 × 2 混合设计中得出的贝叶斯因子的顺序和度量一致性。模拟结果表明,在某些情况下,特别是当效应大小较小且研究样本量较小时,三种方法之间存在明显的差异。研究结果进一步复制并扩展了之前的观察结果,即在同一分析的不同运行中,使用蒙特卡洛积分的方差分析存在很大差异。这些观察结果表明了当前贝叶斯方差分析实施的重要局限性。研究人员在解释相应的分析时应注意这些局限性,最好采用多种方法来确定趋同的结果。(PsycInfo Database Record (c) 2024 APA, 版权所有)。
Consistency of Bayes factor estimates in Bayesian analysis of variance.
Factorial designs lend themselves to a variety of analyses with Bayesian methodology. The de facto standard is Bayesian analysis of variance (ANOVA) with Monte Carlo integration. Alternative, and readily available methods, are Bayesian ANOVA with Laplace approximation as well as Bayesian t tests for individual effects. This simulation study compared the three approaches regarding ordinal and metric agreement of the resulting Bayes factors for a 2 × 2 mixed design. Simulation results indicate remarkable disagreement of the three methods in certain cases, particularly when effect sizes are small and studies include small sample sizes. Findings further replicate and extend previous observations of substantial variability of ANOVAs with Monte Carlo integration across different runs of one and the same analysis. These observations showcase important limitations of current implementations of Bayesian ANOVA. Researchers should be mindful of these limitations when interpreting corresponding analyses, ideally applying multiple approaches to establish converging results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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.