Most organizational researchers understand the detrimental effects of measurement errors in testing relationships among latent variables and hence adopt structural equation modeling (SEM) to control for measurement errors. Nonetheless, many of them revert to regression-based approaches, such as moderated multiple regression (MMR), when testing for moderating and other nonlinear effects. The predominance of MMR is likely due to the limited evidence showing the superiority of latent interaction approaches over regression-based approaches combined with the previous complicated procedures for testing latent interactions. In this teaching note, we first briefly explain the latent moderated structural equations (LMS) approach, which estimates latent interaction effects while controlling for measurement errors. Then we explain the reliability-corrected single-indicator LMS (RCSLMS) approach to testing latent interactions with summated scales and correcting for measurement errors, yielding results which approximate those from LMS. Next, we report simulation results illustrating that LMS and RCSLMS outperform MMR in terms of accuracy of point estimates and confidence intervals for interaction effects under various conditions. Then, we show how LMS and RCSLMS can be implemented with Mplus, providing an example-based tutorial to demonstrate a 4-step procedure for testing a range of latent interactions, as well as the decisions at each step. Finally, we conclude with answers to some frequently asked questions when testing latent interactions. As supplementary files to support researchers, we provide a narrated PowerPoint presentation, all Mplus syntax and output files, data sets for numerical examples, and Excel files for conducting the loglikelihood values difference test and plotting the latent interaction effects.

大多数组织研究人员了解测量误差在测试潜在变量之间的关系方面的有害影响，因此采用结构方程模型（SEM）来控制测量误差。尽管如此，在测试缓和和其他非线性影响时，许多方法还是恢复了基于回归的方法，例如中度多元回归（MMR）。MMR的优势很可能是由于有限的证据表明潜在交互作用方法优于基于回归的方法以及先前测试潜在交互作用的复杂程序所具有的优势。在本教学笔记中，我们首先简要解释潜在的适度结构方程（LMS）方法，该方法在控制测量误差的同时估算潜在的相互作用效应。然后，我们解释了经过可靠性校正的单指标LMS（RCSLMS）方法，该方法可通过累加的标度测试潜在的相互作用并校正测量误差，从而得出与LMS近似的结果。接下来，我们报告的模拟结果表明，在各种条件下，LMS和RCSLMS在点估计的准确性和交互作用的置信区间方面均优于MMR。然后，我们展示了如何使用Mplus来实现LMS和RCSLMS，并提供了一个基于示例的教程，以演示一个4步骤的过程来测试一系列潜在的交互作用以及每个步骤的决策。最后，我们在测试潜在交互时给出一些常见问题的答案。作为支持研究人员的补充文件，我们提供了旁白的PowerPoint演示文稿，