Finite-sample bias is a pervasive challenge in the estimation of structural equation models (SEMs), especially when sample sizes are small or measurement reliability is low. A range of methods have been proposed to improve finite-sample bias in the SEM literature, ranging from analytic bias corrections to resampling-based techniques, with each carrying trade-offs in scope, computational burden, and statistical performance. We apply the reduced-bias M-estimation framework (RBM, Kosmidis & Lunardon, 2024, J. R. Stat. Soc. Series B Stat. Methodol.) to SEMs. The RBM framework is attractive as it requires only first- and second-order derivatives of the log-likelihood, which renders it both straightforward to implement, and computationally more efficient compared to resampling-based alternatives such as bootstrap and jackknife. It is also robust to departures from modelling assumptions. Using the same simulation setup as in Dhaene and Rosseel (2022), we illustrate that RBM estimators consistently reduce mean bias in the estimation of SEMs without inflating mean squared error. They also deliver improvements in both median bias and inference relative to maximum likelihood estimators, while maintaining robustness under non-normality. Our findings suggest that RBM offers a promising, practical, and broadly applicable tool for mitigating bias in the estimation of SEMs, particularly in small-sample research contexts.

翻译：有限样本偏差是结构方程模型（SEM）估计中普遍存在的挑战，尤其是在样本量较小或测量信度较低的情况下。SEM文献中已提出一系列改进有限样本偏差的方法，涵盖从解析偏差校正到基于重采样的技术，每种方法在适用范围、计算负担和统计性能方面均存在权衡。我们将减偏M估计框架（RBM，Kosmidis & Lunardon, 2024, J. R. Stat. Soc. Series B Stat. Methodol.）应用于SEM。该框架仅需对数似然函数的一阶和二阶导数，这使其实现过程直观，且与自助法和刀切法等基于重采样的替代方法相比具有更高的计算效率。同时，该框架对模型假设的偏离具有稳健性。采用与Dhaene和Rosseel（2022）相同的模拟设置，我们证明RBM估计量能持续降低SEM估计中的均值偏差，且不会增大均方误差。与最大似然估计量相比，RBM在减少中位数偏差和改进统计推断方面均表现出优势，并在非正态条件下保持稳健性。我们的研究结果表明，RBM为缓解SEM估计中的偏差提供了一种具有前景、实用且广泛适用的工具，尤其适用于小样本研究场景。