Primary scientific aims

The overarching purpose of LEVANTE is to identify how individual variability, group heterogeneity, and contextual variability influence children’s learning and development. LEVANTE will help researchers, educators, and policy-makers understand child development within individuals, within groups, and across contexts, thus improving future outcomes for diverse groups of children worldwide. A set of key scientific aims follow from this purpose: 

Estimate environmental and contextual moderation of learning and development for both average developmental outcomes and for variability in developmental outcomes.

  1. Characterize developmental changes in learning outcomes, and how these changes are moderated by contextual factors a) within and b) across sites. For example, estimate the moderation of reading outcomes by aspects of the home literacy environment, then consider the relative consistency and variability of this moderation relationship across datasets from different sites.
  2. Characterize within-site, between-person variation in learning outcomes and outcome trajectories, and model across sites how this variation is related to variation in key contextual moderators. For example, estimate how the variability of reading outcomes within each site is related to variability in contextual moderators such as socioeconomic status or home literacy environment.

Characterize the structure of developmental variation within and across contexts.

  1. Model the relationships between measures and their constructs both within and across traditional construct categories. Investigate variation in this construct structure across sites, as well as how variation in the construct structure is related to particular site-level moderators. 
  2. Examine developmental continuity between early childhood contextual and task-based measures and learning outcomes, with the goal of understanding prerequisites for learning success. 
  3. Estimate the dimensional structure of development across constructs. Examine the principal factors underlying differences between individuals, both within and across age groups, and test whether this factor is consistent across contexts and across age. 

Measure the impact of within-individual variability on learning outcomes. 

  1. Estimate within-individual variability through the use of trial-level variability models both within and across individual tasks, investigating the linkages between this variability and both concurrent and later learning outcomes. 
  2. Estimate within-individual developmental variability using longitudinal models to estimate deviation from predicted trajectories, and investigate linkages between this variability and learning outcomes.