Latent variable models (LVMs) are incredibly flexible tools that allow users to address research questions they might otherwise never be able to answer (McDonald, 2013). However, one major limitation of LVMs is evaluating model fit. There is no universal consensus about how to evaluate model fit, either globally or locally. Part of the reason evaluating these models is difficult is because fit is typically reduced to a handful of statistics that may or may not reflect the model’s adequacy and/or assumptions. In this article we argue that proper evaluation of model fit must include visualizing both the raw data and the model-implied fit. Visuals reveal, at a glance, the fit of the model and whether the model’s assumptions have been met. Unfortunately, tools for visualizing LVMs have historically been limited. In this article, we introduce new plots and reframe existing plots that provide necessary resources for evaluating LVMs. These plots are available in a new open-source R package called flexplavaan, which combines the model plotting capabilities of flexplot with the latent variable modeling capabilities of lavaan.

中文翻译：

---

看到不可能的事情：使用 flexplavaan 可视化潜变量模型。


潜变量模型 (LVM) 是极其灵活的工具，允许用户解决他们可能永远无法回答的研究问题（McDonald，2013）。然而，LVM 的一个主要限制是评估模型拟合度。关于如何评估模型拟合度，无论是全球还是局部，都没有达成普遍共识。评估这些模型很困难的部分原因是，拟合通常被简化为少数统计数据，这些统计数据可能反映也可能不反映模型的充分性和/或假设。在本文中，我们认为模型拟合的正确评估必须包括可视化原始数据和模型隐含的拟合。视觉效果一目了然地揭示了模型的拟合度以及模型的假设是否得到满足。不幸的是，用于可视化 LVM 的工具历来都是有限的。在本文中，我们介绍了新的图并重新构建了现有图，为评估 LVM 提供了必要的资源。这些图可在名为 flexplavaan 的新开源 R 包中找到，该包结合了 flexplot 的模型绘图功能和 lavaan 的潜在变量建模功能。