ABSTRACT Factor Score Regression (FSR) methods have received increased interest in the quantitative literature, with Croon’s bias-correcting method gaining particular traction. By fixing measurement parameters in place in an initial step, FSR methods aim to stymie the proliferation of bias in larger structural models that may contain misspecification. Although Croon’s approach was originally derived for factor models exhibiting simple structure and conditionally independent unique factors, Hayes and Usami recently extended this method to connected measurement models featuring correlated uniquenesses. In this article, we demonstrate that their formulas also correct bias in models that feature cross-loadings. We begin by discussing bias in SEMs that incorrectly impose simple structure. We then describe Croon’s approach in connected measurement models featuring cross-loadings and compare its performance to two other state-of-the-art FSR approaches both analytically and via a simulated demonstration.

中文翻译：

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包含交叉载荷的连接测量模型中的因子得分回归

摘要因子得分回归 (FSR) 方法在定量文献中受到越来越多的关注，Croon 的偏差校正方法获得了特别的关注。通过在初始步骤中固定测量参数，FSR 方法旨在阻止可能包含错误指定的较大结构模型中偏差的扩散。尽管 Croon 的方法最初是针对具有简单结构和条件独立的唯一因子的因子模型推导出来的，但 Hayes 和 Usami 最近将这种方法扩展到具有相关唯一性的连接测量模型。在本文中，我们证明了他们的公式还可以纠正具有交叉加载特征的模型中的偏差。我们首先讨论 SEM 中错误地强加简单结构的偏差。