Item parcels are aggregates of two or more items that are used as indicators when estimating latent constructs. In this chapter, we provide an overview of the psychometric and model estimation benefits of parceling, and discuss how parcels can be particularly beneficial for small sample research. These benefits can only result from thoughtful construction of the parcels. Accordingly, methods for building parcels and handling missing data when using parcels are also reviewed.

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.

Wound aetiology, host characteristics and the wound microbiome contribute to chronic wound development. Yet, there is little accounting for the relative importance of these factors to predict wound healing. Here, a structural equation model was developed to provide such an explanatory and predictive framework. Chronic wounds from 565 patients treated at a clinic practicing biofilm‐based wound care were included. Patient information included DNA sequencing‐based wound microbiome clinical reports corresponding to the initial clinical visit. Wound microbiome data was integrated into the SEM as a latent variable using a pre‐modelling parcel optimization routine presented herein for the first time (available as R library parcelR). A microbiome latent construct associated with improved healing was validated, and the final SEM included this latent construct plus three species associated with diminished healing (Anaerococcus vaginalis, Finegoldia magna and Pseudomonas aeruginosa), as well as smoking, wound volume, slough, exudate, edema, percent granulation and wound etiology. This model explained 46% of variations in healing time, with the microbiome contributing the largest proportion of variance explained. Model validity was confirmed with an independent cohort (n = 79) through which ~60% of the variation in healing time was predicted. This model can serve as a foundation for the development of a predictive tool that may have clinical utility.

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.

Measurement of cognitive functions is typically based on the implicit assumption that the mental architecture underlying cognitive task performance is constant throughout the task. In contrast, skill learning theory implies that cognitively demanding task performance is an adaptive process that progresses from initial heavy engagement of effortful and task-general metacognitive and executive control processes towards more automatic and task-specific performance. However, this hypothesis is rarely applied to the short time spans of traditional cognitive tasks such as working memory (WM) tasks. We utilised longitudinal structural equation models on two well-powered data sets to test the hypothesis that the initial stages of WM task performances load heavily on a task-general g-factor and then start to diverge towards factors specific to task structure. In line with the hypothesis, data from the first experiment ( N = 296) were successfully fitted in a model with task-initial unity of the WM paradigm-specific latent factors, after which their intercorrelations started to diverge. The second experiment ( N = 201) replicated this pattern except for one paradigm-specific latent factor. These preliminary results suggest that the processes underlying WM task performance tend to progress rapidly from more task-general towards task-specific, in line with the cognitive skill learning framework. Such task-internal dynamics has important implications for the measurement of complex cognitive functions.

This study utilised a structural equation model to examine the relationship between diet quality, socioeconomic status, and cardiovascular disease (CVD) risk in South African learners. Confirmatory factor analysis was used to test the indirect effects model for diet, socioeconomic status, diet quality and cardiovascular risk using pre-existing cross-sectional data. The structural equation model was fit using Lavaan version 0.6–5 in R version 3.6.1. Data were analysed from 178 children and adolescents, aged 6–18 years, from five rural schools in Cofimvaba, South Africa. Latent variables were created for dietary quality, dyslipidaemia and the socioeconomic status of participants. A negative association was observed between socioeconomic status and dyslipidaemia in school-aged children (p = 0.029).