Abstract

Structural Equation Models (SEMs) are typically estimated via Maximum Likelihood. Grounded in large sample theory, estimates are prone to finite sample bias. Although Restricted Maximum Likelihood (REML) can alleviate this bias, its applicability is constrained to SEMs that are mathematically equivalent to mixed effect models. Via Monte Carlo simulations, we explored whether resampling based corrections could serve as viable, more broadly applicable alternatives. Results indicate that Bootstrap and Jackknife corrections effectively attenuate small sample bias, at the expected expense of an increase in variability. Similar conclusions are drawn with respect to a more recently proposed analytic approach by Ozenne et al., which was included for comparison. For all corrective methods, caution is advised when dealing with non-normal data and/or low reliability.

摘要

结构方程模型 (SEM) 通常通过最大似然估计。基于大样本理论，估计容易出现有限样本偏差。尽管受限最大似然法 (REML) 可以减轻这种偏差，但它的适用性仅限于在数学上等同于混合效应模型的 SEM。通过蒙特卡罗模拟，我们探索了基于重采样的校正是否可以作为可行的、更广泛适用的替代方案。结果表明，Bootstrap 和 Jackknife 校正有效地减弱了小样本偏差，但预期会以增加可变性为代价。对于 Ozenne 等人最近提出的一种分析方法，也得出了类似的结论，该方法被包括在内以进行比较。对于所有纠正方法，