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Detecting Interaction Effects in Moderated Multiple Regression With Continuous Variables Power and Sample Size Considerations

机译：使用连续变量功效和样本大小考虑因素，检测适度多元回归中的相互作用效应

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摘要

In view of the long-recognized difficulties in detecting interactions among continuous variables in moderated multiple regression analysis, this article aims to address the problem by providing feasible solutions to power calculation and sample size determination for significance test of moderating effects. The proposed approach incorporates the essential factors of strength of moderator effect, magnitude of error variation, and distributional property of predictor and moderator variables into a unified framework. Accordingly, careful consideration across different plausible and practical configurations of the prescribed factors is an important aspect of power and sample size computations in planning moderated multiple regression research. The performance of the suggested procedure and an alternative simplified method is illustrated with detailed numerical studies. The simulation results demonstrate that an acceptable degree of accuracy can be obtained using the recommended method in assessing moderated relationships.
机译：鉴于在缓和多元回归分析中检测连续变量之间的交互的长期公认的困难，本文旨在通过为功效计算和样本量确定提供适度的解决方案以缓解影响的显着性，从而解决该问题。所提出的方法将主持人效果的强度，误差变化的大小以及预测变量和主持人变量的分布特性的基本要素合并到一个统一的框架中。因此，在计划中度的多元回归研究中，仔细考虑规定因素的不同合理配置和实际配置是计算功效和样本数量的重要方面。详细的数值研究说明了建议的程序和替代的简化方法的性能。仿真结果表明，使用推荐的方法评估缓和的关系可以获得可接受的准确性。

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