代表潜伏个人资料分析的子群体:使用练习者的目标定向采用个人资料进行非技术性介绍
E Whitney G Moore1, Alessandro Quartiroli2,3
1Department of Kinesiology, East Carolina University, Greenville, NC, USA. mooreer22@ecu.edu.
Journal of behavioral medicine
|September 9, 2025
概括
潜形状分析 (LPA) 使用连续数据识别子组,为复杂的行为医学研究提供潜类分析 (LCA) 的强有力的替代方案. 这种方法增强了子组的检查,并为研究人员提供了最佳实践指南.
科学领域:
- 行为医学是一种行为医学.
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
背景情况:
- 隐形形状分析 (LPA) 是一种有限混合模型,用于根据连续变量识别子组.
- 在处理行为医学中常见的连续指标时,LPA比隐性类分析 (LCA) 有优势.
研究的目的:
- 讨论LPA的优点,弱点和适当的应用.
- 从概念上介绍LPA,并用运动心理学中的最佳实践来说明其应用.
- 将LPA衍生模型与传统方法进行比较,并提供报告准则.
主要方法:
- 使用连续指标进行隐形形状分析 (LPA).
- 用运动心理学变量进行插图.
- 将LPA模型与使用相同数据集的典型方法进行比较.
主要成果:
- LPA有效地识别出具有复杂,多维特征的子组.
- 展示了对LPA进行和报告的最佳实践指南.
- 为可重复性提供了额外的数据和语法文件.
结论:
- 在行为医学中,LPA是检查复杂结构和生物标志物的宝贵工具.
- 遵守最佳实践对于准确的LPA实施和解释至关重要.
- 该研究提供了实用资源,以支持研究人员使用LPA.
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