Solutions for latent growth modeling following COVID-19-related discontinuities in change and disruptions in longitudinal data collection

Charlie Rioux, Zachary L. Stickley, Todd D. Little

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)463-473
Number of pages11
JournalInternational Journal of Behavioral Development
Volume45
Issue number5
DOIs
StatePublished - Sep 2021

Keywords

  • Growth curve
  • SARS-COV-2
  • analysis
  • discontinuity
  • missing data
  • piecewise

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