Division of Interdisciplinary Studies, School of Behavioral Health, Loma Linda University, 11065 Campus St., Loma Linda, CA, 92350, USA.
Psychometrika. 2020 Jun;85(2):275-300. doi: 10.1007/s11336-020-09702-9. Epub 2020 Apr 17.
A maximum likelihood estimation routine for two-level structural equation models with random slopes for latent covariates is presented. Because the likelihood function does not typically have a closed-form solution, numerical integration over the random effects is required. The routine relies upon a method proposed by du Toit and Cudeck (Psychometrika 74(1):65-82, 2009) for reformulating the likelihood function so that an often large subset of the random effects can be integrated analytically, reducing the computational burden of high-dimensional numerical integration. The method is demonstrated and assessed using a small-scale simulation study and an empirical example.
本文提出了一种用于具有随机斜率潜变量协变量的两层结构方程模型的极大似然估计程序。由于似然函数通常没有闭式解，因此需要对随机效应进行数值积分。该程序依赖于 du Toit 和 Cudeck（2009 年，《心理测量学》74（1）：65-82）提出的一种方法，用于重新制定似然函数，以便可以对随机效应的一个通常较大子集进行解析积分，从而降低高维数值积分的计算负担。该方法通过小规模模拟研究和实证示例进行了演示和评估。
