Seeing the Impossible: Visualizing Latent Variable Models With Flexplavaan

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. Translational Abstract There is a class of models frequently used by researchers called latent variable models, or LVMs. These models are extremely flexible and useful, especially when one is trying to assess a characteristic that is "unobserved," like intelligence or depression. However, historically, there have not been intuitive methods for evaluating how well these models fit the data. This article introduces a software application called "flexplavaan," which makes it easy to visualize the fits of these models. flexplavaan makes it easy to identify sources of misfit and to evaluate the model's assumptions.