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.

Parceling is pre-modeling strategy to create fewer and more reliable indicators of constructs for use with latent variable models. Parceling is particularly useful for developmental scientists because longitudinal models can become quite complex and even intractable when measurement models of items are fit. In this Element the authors provide a detailed account of the advantages of using parcels, their potential pitfalls, as well as the techniques for creating them for conducting latent variable structural equation modeling (SEM) in the context of the developmental sciences. They finish with a review of the recent use of parcels in developmental journals. Although they focus on developmental applications of parceling, parceling is also highly applicable to any discipline that uses latent variable SEM.

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Poor birth outcomes such as low birth weight, low birth length and short gestational age, are public health concern issues in South Africa (SA). This study utilized structural equation modeling (SEM) to explore how nutritional and social factors contribute to favorable fetal growth conditions (FFGC) in pregnant women living with and without human immunodeficiency virus (HIV), in the Free State Province of SA. Sociodemographic characteristics, stress, health and nutrition-related information, and birth outcomes data were collected and analyzed from a subsample of 305 women enrolled in a cohort study from 2018–2020. Descriptive statistics were analyzed in R version 4.1.2 and SEM was conducted in Lavaan version 0.6–5. Higher gestational body mass index (GBMI) and income levels were associated with higher FFGC (p < 0.05). Household incomes were positively associated with dietary micronutrient quality (p = 0.002), GBMI (p = 0.012) and food security (p = 0.001). Low incomes (p = 0.004) and food insecurity (p < 0.001) were associated with higher stress, while social support was positively associated with food security status (p = 0.008). These findings highlight the complex interconnections between the social and nutritional factors that are associated with fetal growth conditions. Multisectoral community-based programs may be a useful strategy to address these challenges.

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