在营养和肥胖研究中分离连续变量:需要切断的做法
Osvaldo F Morera1, Mosi I Dane'el2, Brandt A Smith3
1Department of Psychology, University of Texas at El Paso, 500 W. University Ave., El Paso, TX, 79968, USA. omorera@utep.edu.
Nutrition & diabetes
|November 8, 2023
概括
在营养研究中对连续变量进行二分化会扭曲研究结果并掩盖重要影响. 建议使用多重回归连续分析变量以获得准确的结果.
科学领域:
- 营养科学 营养科学
- 肥胖问题研究研究
- 统计方法 统计方法
背景情况:
- 营养和肥胖研究人员经常将连续变量进行二分化进行分析.
- 这种做法可能会导致关于群体差异的不准确结论.
研究的目的:
- 描述营养研究中对连续独立变量进行二分化和分离的后果.
- 为了比较使用连续变量与二分化/离散变量的分析方法.
主要方法:
- 分析了两项与营养相关的横截面研究.
- 研究1研究了健康素养和营养知识对营养标签准确性的影响 (n=612).
- 第二项研究研究了认知克制和BMI对水果和蔬菜摄入量的影响 (n=586).
- 分析将二分化/离散变量与使用ANOVA和回归的连续变量进行了比较.
主要成果:
- 单体化扭曲了效果大小,掩盖了健康素养的二次效应,并未检测到BMI的适度效应.
- 使用连续变量的回归分析揭示了ANOVA遗漏的显著效应和相互作用.
- 在研究2中的持续分析发现了BMI,认知克制以及它们与水果和蔬菜摄入量的相互作用的显著预测.
结论:
- 连续独立变量的二分化和分离导致了研究结果的重大扭曲.
- 研究人员应该避免这些做法,并利用多重回归来分析连续的独立和依赖变量.
- 连续变量分析保留了效应大小,并检测了复杂的关系,如二次和调节效应.
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