Klugkist Irene, Volker Thom Benjamin
Utrecht University.
Psychol Methods. 2023 Sep 7. doi: 10.1037/met0000602.
To establish a theory one needs cleverly designed and well-executed studies with appropriate and correctly interpreted statistical analyses. Equally important, one also needs replications of such studies and a way to combine the results of several replications into an accumulated state of knowledge. An approach that provides an appropriate and powerful analysis for studies targeting prespecified theories is the use of Bayesian informative hypothesis testing. An additional advantage of the use of this Bayesian approach is that combining the results from multiple studies is straightforward. In this article, we discuss the behavior of Bayes factors in the context of evaluating informative hypotheses with multiple studies. By using simple models and (partly) analytical solutions, we introduce and evaluate Bayesian evidence synthesis (BES) and compare its results to Bayesian sequential updating. By doing so, we clarify how different replications or updating questions can be evaluated. In addition, we illustrate BES with two simulations, in which multiple studies are generated to resemble conceptual replications. The studies in these simulations are too heterogeneous to be aggregated with conventional research synthesis methods. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
要建立一种理论，需要精心设计并执行良好的研究，并进行恰当且解释正确的统计分析。同样重要的是，还需要对这类研究进行重复验证，并有一种方法将多次重复验证的结果整合为一种累积的知识状态。一种针对预先设定的理论的研究提供恰当且有力分析的方法是使用贝叶斯信息性假设检验。使用这种贝叶斯方法的另一个优点是，整合多项研究的结果很简单。在本文中，我们讨论了在多项研究评估信息性假设的背景下贝叶斯因子的表现。通过使用简单模型和（部分）解析解，我们引入并评估了贝叶斯证据合成（BES），并将其结果与贝叶斯序贯更新进行比较。通过这样做，我们阐明了如何评估不同的重复验证或更新问题。此外，我们用两个模拟示例说明了BES，在这两个模拟中生成了多项研究以类似于概念性重复验证。这些模拟中的研究差异太大，无法用传统的研究合成方法进行汇总。（《心理学文摘数据库记录》（c）2023美国心理学会，保留所有权利）
