manymome:用于计算许多 (但不是所有) 模型中的间接效应,条件效应和条件间接效应,标准化或非标准化,以及它们的引导置信区间的R包
Shu Fai Cheung1, Sing-Hang Cheung2
1Department of Psychology, Faculty of Social Sciences, University of Macau, Avenida da Universidade, Taipa, Macao SAR, China. sfcheung@um.edu.mo.
Behavior research methods
|October 5, 2023
概括
研究人员现在可以轻松计算行为研究中的标准化间接效应,使用新的R包,manymome. 该工具简化了用于调解和调节模型的复杂统计分析.
科学领域:
- 行为科学 行为科学
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
背景情况:
- 调解,适度和适度调解在行为研究中很普遍.
- 现有的间接效应工具对于标准化的有条件间接效应缺乏用户友好性.
- 目前的一些方法仅限于特定的模型类型.
研究的目的:
- 介绍 manymome,一个用于估计和形成各种效应的置信区间的 R 包.
- 为标准化和非标准化间接,有条件和有条件间接效应提供一个用户友好的工具.
- 为了使研究人员能够在影响估计之前专注于模型拟合.
主要方法:
- 开发了许多mome R 包.
- 采用两步方法:参数估计 (SEM通过岩或回归通过lm),然后进行效果计算.
- 在使用结构方程建模时支持缺失数据处理.
主要成果:
- 许多人可以估计和形成间接,有条件和有条件间接效应的置信区间.
- 该包包含标准化和非标准化效果.
- 预测因素,独立变量,调节者或调解者的数量没有固有的限制.
结论:
- manymome为行为研究中的复杂效应估计提供了灵活和可访问的解决方案.
- 该包简化了这个过程,允许研究人员优先考虑模型开发.
- 它解决了统计软件对标准化有条件间接效应的缺口.
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