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. Through extensive simulations studies under a range of experimental conditions, 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估计中普遍存在的挑战，尤其在样本量较小或测量信度较低的情况下。SEM文献中已提出多种改进有限样本偏差的方法，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。我们将有偏降低的M估计框架应用于SEM。RBM框架具有吸引力，因为它仅需要对数似然函数的一阶和二阶导数，这使得其实施相对直接，且与基于重采样的替代方法相比计算效率更高。该框架对模型假设的偏离也具有稳健性。通过在一系列实验条件下进行广泛的模拟研究，我们证明RBM估计量在SEM估计中能持续降低均值偏差，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推断方面均有提升，同时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有前景、实用且广泛适用的工具，尤其适用于小样本研究情境。