Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models.
Moderation analysis in two-instance repeated measures designs: Probing methods and multiple moderator models.
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DOI：
10.3758/s13428-018-1088-6
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发表时间：
2019-03
影响因子：
5.4
通讯作者：
Montoya AK
中科院分区：
文献类型：
--
作者：
Montoya AK
适度假设出现在心理科学的各个领域，但在两次重复测量设计中检验和探索适度的方法是不完整的。本文首先简要概述参与者之间的设计中的测试和探测交互。接下来，我回顾了贾德、麦克莱兰和史密斯(心理学方法1；366-378)和贾德、肯尼和麦克莱兰(心理学方法6；115-134)中概述的使用线性回归对重复测量因素和单个参与者之间的主持人之间的相互作用进行估计和推理的方法。我从两个方面对这些方法进行了扩展：首先，本文展示了如何使用挑点法和Johnson-Neyman程序来探索两个实例重复测量设计中的交互作用。其次，我推广了Judd等人描述的模型。到多主持人模型，包括相加和相乘缓和。文中还提供了使用已发布的数据集的工作示例，以演示本文中描述的方法。此外，我还演示了如何使用Mplus和MEMORE(重复测量的调解和调节；http://akmontoya.com)，上提供了一种可用于SPSS和SA的简单易用的工具)，以估计和探测当焦点预测器是参与者内部因素时的交互作用，从而减轻研究人员的计算负担。我描述了一些可供选择的分析方法，包括结构方程模型和多水平模型。结论涉及到本文所述方法的一些扩展，以及可能取得丰硕成果的进一步研究领域。
Moderation hypotheses appear in every area of psychological science, but the methods for testing and probing moderation in two-instance repeated measures designs are incomplete. This article begins with a short overview of testing and probing interactions in between-participant designs. Next I review the methods outlined in Judd, McClelland, and Smith (Psychological Methods 1; 366–378,) and Judd, Kenny, and McClelland (Psychological Methods 6; 115–134,) for estimating and conducting inference on an interaction between a repeated measures factor and a single between-participant moderator using linear regression. I extend these methods in two ways: First, the article shows how to probe interactions in a two-instance repeated measures design using both the pick-a-point approach and the Johnson–Neyman procedure. Second, I extend the models described by Judd et al. to multiple-moderator models, including additive and multiplicative moderation. Worked examples with a published dataset are included, to demonstrate the methods described throughout the article. Additionally, I demonstrate how to use Mplus and MEMORE (Mediation and Moderation for Repeated Measures; available at http://akmontoya.com), an easy-to-use tool available for SPSS and SAS, to estimate and probe interactions when the focal predictor is a within-participant factor, reducing the computational burden for researchers. I describe some alternative methods of analysis, including structural equation models and multilevel models. The conclusion touches on some extensions of the methods described in the article and potentially fruitful areas of further research.
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作者：
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