Institute of Psychology, Department of Research Methods and Evaluation, Goethe University Frankfurt, Theodor-W.-Adorno-Platz 6, 60629, Frankfurt am Main, Germany.
Behav Res Methods. 2024 Dec;56(8):8897-8931. doi: 10.3758/s13428-024-02476-3. Epub 2024 Sep 20.
The model-implied simulation-based power estimation (MSPE) approach is a new general method for power estimation (Irmer et al., 2024). MSPE was developed especially for power estimation of non-linear structural equation models (SEM), but it also can be applied to linear SEM and manifest models using the R package powerNLSEM. After first providing some information about MSPE and the new adaptive algorithm that automatically selects sample sizes for the best prediction of power using simulation, a tutorial on how to conduct the MSPE for quadratic and interaction SEM (QISEM) using the powerNLSEM package is provided. Power estimation is demonstrated for four methods, latent moderated structural equations (LMS), the unconstrained product indicator (UPI), a simple factor score regression (FSR), and a scale regression (SR) approach to QISEM. In two simulation studies, we highlight the performance of the MSPE for all four methods applied to two QISEM with varying complexity and reliability. Further, we justify the settings of the newly developed adaptive search algorithm via performance evaluations using simulation. Overall, the MSPE using the adaptive approach performs well in terms of bias and Type I error rates.
模型模拟的基于仿真的功效估计 (MSPE) 方法是一种新的功效估计通用方法（Irmer 等人，2024 年）。MSPE 是专门为非线性结构方程模型 (SEM) 的功效估计而开发的，但它也可以使用 R 包 powerNLSEM 应用于线性 SEM 和显式模型。在首先提供有关 MSPE 和新的自适应算法的一些信息后，该算法可通过仿真自动选择样本量以实现最佳功效预测，本文提供了一个使用 powerNLSEM 包进行二次和交互 SEM (QISEM) 的 MSPE 教程。对于四种方法，即潜在调节结构方程 (LMS)、无约束乘积指标 (UPI)、简单因子得分回归 (FSR) 和 QISEM 的量表回归 (SR) 方法，演示了功效估计。在两项模拟研究中，我们强调了 MSPE 对应用于两种具有不同复杂度和可靠性的 QISEM 的所有四种方法的性能。此外，我们通过使用仿真进行的性能评估来证明新开发的自适应搜索算法的设置是合理的。总体而言，使用自适应方法的 MSPE 在偏差和 I 型错误率方面表现良好。
