Statistical interactions between two continuous variables in linear regression are common in psychological science. As a follow-up analysis of how the moderator impacts the predictor-outcome relationship, researchers often use the pick-a-point simple slopes method. The simple slopes method requires researchers to make two decisions: (a) which moderator values should be used for plotting and testing simple slopes, and (b) which predictor should be considered the moderator. These decisions are meant to be driven by theory, but in practice researchers may use arbitrary conventions or theoretical reasons may not exist. Even when done thoughtfully, simple slopes analysis omits important information about the interaction. Consequently, it is problematic that the simple slopes approach is the primary basis for interpreting interactions. A more nuanced alternative is to utilize the Johnson-Neyman technique in conjunction with a regression plane depicting the interaction effect in three-dimensional space. This approach does not involve picking points but rather shows the slopes at all possible values of the predictor variables and gives both predictors equal weight instead of selecting a de facto moderator. Because this approach is complex and user-friendly implementation tools are lacking, we present a tutorial explaining the Johnson-Neyman technique and how to visualize interactions in 3-D space along with a new open-source tool that completes these procedures. We discuss how this approach facilitates interpretation and communication as well as its implications for replication efforts, transparency, and clinical applications. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

中文翻译：

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简单的斜坡跟踪测试会让我们误入歧途吗？交互可视化和报告方面的进步。

线性回归中两个连续变量之间的统计相互作用在心理科学中很常见。作为对调节器如何影响预测器-结果关系的后续分析，研究人员经常使用选取点的简单斜率方法。简单斜率方法要求研究人员做出两个决定：(a) 应使用哪些调节值来绘制和测试简单斜率，以及 (b) 应将哪个预测因子视为调节因子。这些决定是由理论驱动的，但在实践中研究人员可能会使用任意约定或理论原因可能不存在。即使经过深思熟虑，简单的斜率分析也会忽略有关相互作用的重要信息。因此，简单斜率方法是解释相互作用的主要基础是有问题的。一个更微妙的替代方法是将 Johnson-Neyman 技术与描述三维空间中交互作用的回归平面结合使用。这种方法不涉及选择点，而是显示预测变量所有可能值的斜率，并赋予两个预测变量相等的权重，而不是选择事实上的调节器。由于这种方法很复杂并且缺乏用户友好的实现工具，我们提供了一个教程，解释了 Johnson-Neyman 技术以及如何在 3-D 空间中可视化交互以及完成这些过程的新开源工具。我们讨论了这种方法如何促进解释和交流，以及它对复制工作、透明度和临床应用的影响。（PsycInfo 数据库记录 (c) 2020 APA，