Following the onset of the novel coronavirus disease 2019 (COVID-19) pandemic, daily life significantly changed for the population. Accordingly, researchers interested in examining patterns of change over time may now face discontinuities around the pandemic. Researchers collecting in-person longitudinal data also had to cancel or delay data collection waves, further complicating analyses. Accordingly, the purpose of this article is to aid researchers aiming to examine latent growth models (LGM) in analyzing their data following COVID-19. An overview of basic LGM notions, LGMs with discontinuities, and solutions for studies that had to cancel or delay data collection waves are discussed and exemplified using simulated data. Syntax for R and Mplus is available to readers in online supplemental materials.

在 2019 年新型冠状病毒病 (COVID-19) 大流行之后，人们的日常生活发生了显着变化。因此，有兴趣检查随时间变化的模式的研究人员现在可能面临大流行的不连续性。收集面对面纵向数据的研究人员也不得不取消或延迟数据收集波，这进一步使分析复杂化。因此，本文的目的是帮助旨在检查潜在增长模型 (LGM) 的研究人员分析 COVID-19 后的数据。使用模拟数据讨论并举例说明了基本 LGM 概念、具有不连续性的 LGM 以及必须取消或延迟数据收集波的研究解决方案的概述。读者可以在在线补充材料中获得 R 和 Mplus 的语法。