隐性变量分析在以人为导向的研究中
Alexander von Eye1, Wolfgang Wiedermann2, Stefan von Weber3
1Michigan State University, USA.
Journal for person-oriented research
|June 30, 2023
概括
隐性变量分析,包括因子分析和隐性类分析,对于以人为本的研究是有价值的. 它揭示了总体发现如何可能不适用于特定的子群体,突出了个人层面的变化.
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:
- 研究中的总体发现可能不能准确地反映个体或子群体的特征.
- 将人口水平的结果推广到特定群体可能会导致不准确的结论.
- 了解个体差异需要超越简单聚合的方法.
研究的目的:
- 证明潜变量分析在以人为导向的研究中的实用性.
- 为了说明将总体发现推广到子群体的陷.
- 作为个人级别分析中的分类数据的方法,提出隐性类别分析.
主要方法:
- 对指标变量进行探索性因子分析.
- 确认因素分析. 确认因素分析.
- 对分类变量进行潜在类别分析.
- 使用个人级别数据的示例示范.
主要成果:
- 隐性变量分析有效地模拟了观察到的变量的共变量.
- 总体水平的结果往往无法对子群体进行概括.
- 隐性类分析可以应用于具有足够观察的单个数据.
- 隐性变量作为调节者,影响共变的结构.
结论:
- 潜在变量分析对于准确的以人为本的研究至关重要.
- 研究人员必须谨慎地将发现从种群推广到子种群.
- 隐性类分析提供了一种强大的方法来分析个人层面上的分类数据.
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