The Learning Variability Network Exchange (LEVANTE) brings together researchers from around the world aiming to capture the richness and diversity of child development and learning.
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Prof. Domingue and Prof. Ram are based at Stanford University and will conduct the psychometric analyses on the pilot data collected in Colombia, Germany and Canada. The planned research will assess the suitability of the measures for usage in the larger-scale LEVANTE project. The research aims are: 1. Analyse pilot data to evaluate psychometric performance of individual tasks and surveys slated for inclusion in the broader framework. 2. Investigate cross-task patterns in pilot data to understand the challenges and opportunities of multi-site data collection and how those might be addressed in practice and analysis. The data and samples from the three LEVANTE pilot studies will undergo analysis using psychometric techniques, such as item response theory and factor analysis. These analyses will examine how each item/trial supports the measurement of the intended constructs, the relations among the constructs, and how the (latent) construct scores differ with age and context. Measurement invariance across child characteristics and context will be evaluated to inform adjustments for future LEVANTE studies.
Read more about the LEVANTE Pilot Sites.
Ben Domingue is an associate professor at the Graduate School of Education at Stanford University with research interests in psychometrics and quantitative methods. He is interested in how statistical tools can be used to better understand psychological and educational outcomes, such as a child’s reading ability. He aims to address the measurement difficulties associated with these outcomes in education, as well as in social and biomedical sciences more generally.
Nilam Ram researches the dynamics of psychological and media processes, exploring their fluctuations across different stages of life. His current research focuses on understanding short-term changes such as learning and emotion regulation over a lifespan, using advanced data science techniques and data streams from social media and smartphones.