论文标题
拒绝潜在因素模型的结构解释的统计检验
A statistical test to reject the structural interpretation of a latent factor model
论文作者
论文摘要
因子分析通常用于评估单个单变量潜在变量是否足以解释某些基础结构的一组指标之间的大多数协方差。当有证据表明单个因素足够时，研究通常通过在随后的研究中使用指标进行单变量摘要进行研究。这种做法中隐含的是假设是有效的是潜在的潜在而不是指标。指标对随后的任何事物没有影响，并且它们本身仅受到潜在潜在的影响是一个有力的假设，这是一个有力的假设，这是一个有力的假设，有效地对潜在因子模型强加了结构性解释。在本文中，我们表明，即使潜在变量本身没有观察到，这种结构假设在经验上具有可检验的含义。我们开发了统计检验，以拒绝潜在因子模型的结构解释。我们将此测试应用于与生命级的满意度与随后的全因死亡率之间的关联的数据，该数据提供了有力的证据，以反对对量表潜在潜在潜在潜在的结构解释。讨论该结果对措施的开发，评估和使用以及因素分析本身的使用。
Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such practices is the assumption that it is the underlying latent, rather than the indicators, that is causally efficacious. The assumption that the indicators do not have effects on anything subsequent, and that they are themselves only affected by antecedents through the underlying latent is a strong assumption, effectively imposing a structural interpretation on the latent factor model. In this paper, we show that this structural assumption has empirically testable implications, even though the latent variable itself is unobserved. We develop a statistical test to potentially reject the structural interpretation of a latent factor model. We apply this test to data concerning associations between the Satisfaction-with-Life-Scale and subsequent all-cause mortality, which provides strong evidence against a structural interpretation for a univariate latent underlying the scale. Discussion is given to the implications of this result for the development, evaluation, and use of measures and for the use of factor analysis itself.
