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Hypothesis Testing Using Factor Score Regression: A Comparison of Four Methods

机译：使用因子得分回归的假设检验：四种方法的比较

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摘要

In this article, an overview is given of four methods to perform factor score regression (FSR), namely regression FSR, Bartlett FSR, the bias avoiding method of Skrondal and Laake, and the bias correcting method of Croon. The bias correcting method is extended to include a reliable standard error. The four methods are compared with each other and with structural equation modeling (SEM) by using analytic calculations and two Monte Carlo simulation studies to examine their finite sample characteristics. Several performance criteria are used, such as the bias using the unstandardized and standardized parameterization, efficiency, mean square error, standard error bias, type I error rate, and power. The results show that the bias correcting method, with the newly developed standard error, is the only suitable alternative for SEM. While it has a higher standard error bias than SEM, it has a comparable bias, efficiency, mean square error, power, and type I error rate.
机译：本文概述了执行因子得分回归（FSR）的四种方法，即回归FSR，Bartlett FSR，Skrondal和Laake的偏倚避免方法以及Croon的偏倚校正方法。偏置校正方法已扩展为包括可靠的标准误差。通过使用解析计算和两次蒙特卡洛模拟研究，将这四种方法彼此进行比较，并与结构方程模型（SEM）进行比较，以检验其有限样本特征。使用了几种性能标准，例如使用非标准化和标准化参数设置的偏差，效率，均方误差，标准误差偏差，I类错误率和功率。结果表明，具有新开发的标准误差的偏差校正方法是SEM的唯一合适替代方法。尽管它具有比SEM高的标准误差偏差，但它具有可比的偏差，效率，均方误差，功率和I型误差率。

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