A Cautionary Note Regarding Multilevel Factor Score Estimates from Lavaan

Psych Pub Date : 2023-01-09 DOI:10.3390/psych5010004
Steffen Zitzmann
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引用次数: 2

Abstract

To compute factor score estimates, lavaan version 0.6–12 offers the function lavPredict( ) that can not only be applied in single-level modeling but also in multilevel modeling, where characteristics of higher-level units such as working environments or team leaders are often assessed by ratings of employees. Surprisingly, the function provides results that deviate from the expected ones. Specifically, whereas the function yields correct EAP estimates of higher-level factors, the ML estimates are counterintuitive and possibly incorrect. Moreover, the function does not provide the expected standard errors. I illustrate these issues using an example from organizational research where team leaders are evaluated by their employees, and I discuss these issues from a measurement perspective.
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关于Lavaan的多水平因子得分估计的警告说明
为了计算因子得分估计,lavan版本0.6–12提供了lavPredict()函数,该函数不仅可以应用于单级建模,还可以应用于多级建模,在多级建模中,工作环境或团队领导等上级单位的特征通常通过员工评级来评估。令人惊讶的是,该函数提供的结果与预期的结果不同。具体地说,尽管函数产生了更高层次因素的正确EAP估计,但ML估计是违反直觉的,并且可能是不正确的。此外,该函数没有提供预期的标准误差。我用组织研究中的一个例子来说明这些问题,在这个例子中,团队领导者由他们的员工进行评估,我从衡量的角度来讨论这些问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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