Daniel Lipscomb provides data management and advanced analysis to several projects, including Teacher Working Conditions, COVID-Equity, and the PALS Pilot. He has worked with gigantic datasets with millions of rows, including EEG data and VLDS student records. Recently, he has utilized programming skills to expedite the construction and cleaning of data, as well as to automatically construct summary tables and graphs, as data is added or updated. Furthermore, he uses advanced modeling techniques (e.g., Factor Analysis, Mixed Effects, SEM, and Mixture modeling) to address research goals such as characterizing Teacher Working Conditions, developing a factor framework for school reopening measures, and assessing the fit for PALS Pilot tasks. Daniel has previously worked on mediation analyses for MyTeachingPartner; as well as database construction, online survey development, data management, and technical support for the Inter-brain Synchrony study with Brain Lab.
M.Ed., University of Virginia, 2017
B.S., University of Mary Washington, 2014