This study compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations.

The COVID-19 pandemic, coupled with political and social unrest, has deeply disrupted the lives and learning experiences of most students. It has created an urgent need to address long-standing opportunity gaps and has highlighted the importance of providing resources that support student achievement, social-emotional learning, and physical and mental well-being. This urgency for action to address the impacts is valid, but it has also created a noisy environment of potential options of what might be best for students. It’s critical, now more than ever, for decisions to be grounded in solid research.

This research introduces a novel approach, using the Bayes factor, wherein a researcher can directly test for homogeneous within-person variance in hierarchical models. Additionally, we introduce a membership model that allows for classifying which (and how many) individuals belong to the common variance model.

How can we support academic growth for students with disabilities (SWD) who may have experienced disproportionate academic impacts from COVID-19?

This interactive tool provides context on the typical patterns of achievement and growth in mathematics and reading for private and Catholic schools who take MAP Growth assessments. It provides multiple ways to examine patterns for different groups of students, including by student gender, race/ethnic group, region, and state.