A Gentle Introduction to Bayesian Analysis with Applications to QuantCrit
A workshop to introduce quantitatively-trained researchers to the Bayesian paradigm with applications to QuantCrit
Welcome Setup Modules Resources About
About
Alberto Guzman-Alvarez is a research data scientist at American Institutes of Research. His research focuses on developing and applying quantitative methods to evaluate the effectiveness of education policies and interventions. He is particularly interested on college access issues affecting first-generation and historically marginalized students, with his research drawing from his personal experiences as a son of Mexican immigrants and a first-generation college student.
alberto-guzman.github.io
github.com/alberto-guzman
twitter.com/AlbertoGuzAlv
Taylor Burtch is a postdoctoral scholar at Florida State University. Her research centers on college access and success for multiple marginalized students, including those impacted by early life trauma and adversity. She is particularly interested in QuantCrit and critical mixed methods that afford more nuanced understandings of power and oppression in educational contexts.
github.com/taylorburtch
twitter.com/BurtchTay
Benjamin Skinner is an assistant professor of higher education and policy with an affiliation in the Institute of Higher Education at the University of Florida. His research interests center on college access, the outcomes of college participation, and using tools of data science such as machine learning and Bayesian statistics to critically examine higher education policy questions.