基于异形因素加载模式的相似性,对个人进行集群.
Cara J Arizmendi1, Kathleen M Gates1
1The University of North Carolina Chapel Hill, Chapel Hill, NC, USA.
Multivariate behavioral research
|July 24, 2024
概括
研究人员开发了新的方法,以集群个人基于他们的独特测量模型从时间序列数据. 这种方法有助于识别子类型,并了解心理构造中的个体差异.
科学领域:
- 心理测量 心理测量 心理测量
- 量化心理学 量化心理学
- 统计建模 统计建模
背景情况:
- 图形测量模型 (p技术,动态因子分析) 评估个人层面的潜在结构.
- 个体特定的方法比对异质人群的聚合数据具有优势.
- 需要对具有相似测量模型的个体进行集群,以确定亚型.
研究的目的:
- 提出和评估基于从时间序列数据中测量模型负载的个人聚类方法.
- 为了确定测量模型子类型是否存在于个人之间.
- 评估不同的模型是否与相同的潜在概念相对应.
主要方法:
- 关于特征因子建模,测量不变性和时间序列聚类的文献综述.
- 开发用于单个测量模型负载的新型聚类方法.
- 两个模拟研究来测试拟议方法的实用性和有效性.
主要成果:
- 研究1证明了使用拟议的集群方法成功恢复模拟组的不同因素负载.
- 第二项研究将该方法扩展到动态因子分析 (DFA),并显示模拟集群的良好恢复.
- 该方法通过经验数据成功证明.
结论:
- 拟议的集群方法有效地识别了具有相似测量模型的个人子组.
- 这种方法推进了对特征数据的分析和对个体差异的理解.
- 这些方法为研究人员研究个人特定过程提供了有价值的工具.
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