Bainter Sierra A, Bollen Kenneth A
Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Measurement ( Mahwah N J). 2014;12(4):125-140. doi: 10.1080/15366367.2014.968503.
In measurement theory causal indicators are controversial and little-understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning intended by a researcher. This article questions the validity of evidence used to claim that causal indicators are inherently susceptible to interpretational confounding. Further, a simulation study demonstrates that causal indicator coefficients are stable across correctly-specified models. Determining the suitability of causal indicators has implications for the way we conceptualize measurement and build and evaluate measurement models.
在测量理论中，因果指标存在争议且鲜为人知。关于因果指标的方法学分歧主要集中在因果指标是否本质上易受解释性混淆影响这一问题上，解释性混淆是指潜在构念的实证意义偏离研究者预期意义的情况。本文对用于声称因果指标本质上易受解释性混淆影响的证据的有效性提出质疑。此外，一项模拟研究表明，因果指标系数在正确设定的模型中是稳定的。确定因果指标的适用性对我们概念化测量以及构建和评估测量模型的方式具有影响。
