Research Project

by Daniel Lobo. Daniel Lobo, a PhD Student in Sociology at UC Berkeley, discusses his journey from growing up in the urban working class to making it to Harvard College and UC Berkeley. He credits his mentors who were able to see him in a way that he could not see himself as the keys to his success. This gift, the power to see others for who they are and who they could be, animates his research and teaching, including on the NSF-IUSE project.

by Mingfeng Xue. Hierarchical Linear Modeling (HLM) is an extension of linear models, which offers an approach to analyzing data structures with nested levels. This blog elucidates HLM's significance over traditional linear regression models, particularly in handling clustered data and multilevel predictors. Illustrated with an example from educational research, the blog demonstrates model implementation and interpretation steps. It showcases how HLM accommodates both independent variables from different levels and hierarchical structure data, providing insights into their impacts on the outcome variable. Recommended resources further aid readers in mastering HLM techniques.