Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill.
Department of Sociology, University of North Carolina at Chapel Hill.
Psychol Methods. 2024 Oct;29(5):868-889. doi: 10.1037/met0000530. Epub 2022 Oct 6.
It is common practice for psychologists to specify models with latent variables to represent concepts that are difficult to directly measure. Each latent variable needs a scale, and the most popular method of scaling as well as the default in most structural equation modeling (SEM) software uses a scaling or reference indicator. Much of the time, the choice of which indicator to use for this purpose receives little attention, and many analysts use the first indicator without considering whether there are better choices. When all indicators of the latent variable have essentially the same properties, then the choice matters less. But when this is not true, we could benefit from scaling indicator guidelines. Our article first demonstrates why latent variables need a scale. We then propose a set of criteria and accompanying diagnostic tools that can assist researchers in making informed decisions about scaling indicators. The criteria for a good scaling indicator include high face validity, high correlation with the latent variable, factor complexity of one, no correlated errors, no direct effects with other indicators, a minimal number of significant overidentification equation tests and modification indices, and invariance across groups and time. We demonstrate these criteria and diagnostics using two empirical examples and provide guidance on navigating conflicting results among criteria. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
心理学家通常会指定具有潜在变量的模型来表示难以直接测量的概念。每个潜在变量都需要一个量表，而最流行的量表方法以及大多数结构方程建模（SEM）软件的默认方法都使用一种量表或参考指标。大多数情况下，人们很少关注为此目的选择哪个指标，许多分析师会选择第一个指标，而不考虑是否有更好的选择。当潜在变量的所有指标都具有基本相同的属性时，那么选择就不那么重要了。但是，如果事实并非如此，我们可以从量表指标指南中受益。我们的文章首先展示了为什么潜在变量需要一个量表。然后，我们提出了一组标准和伴随的诊断工具，可以帮助研究人员就量表指标做出明智的决策。良好的量表指标的标准包括高表面效度、与潜在变量高度相关、因子复杂性为一、无相关误差、与其他指标无直接影响、最小数量的显著过度识别方程检验和修正指数，以及跨群体和时间的不变性。我们使用两个实证示例演示了这些标准和诊断，并就如何处理标准之间的冲突结果提供了指导。（PsycInfo 数据库记录（c）2024 APA，保留所有权利）。
