Join Dr. Marla Stuart, a BIDS Data Science Fellow, to discuss using R for machine learning with qualitative research codes. See the process and results, learning how these are different from a purely qualitative thematic analysis. There will be abundant time for conversation with the presenter.
QMG Presents: Dr. Marla Stuart on
Machine Learning with R Using Qualitative Research Codes
Date: March 13, 2019
Time: 11:30 AM to 1:00 PM
Location: Barrows 371, D-Lab, UC Berkeley
At UC Berkeley, Marla Stuart was a BIDS Data Science Fellow working with the Guizhou Berkeley Big Data Innovation Research Center (GBIC), a research hub based in Guizhou Province, China, dedicated to improving the health and well-being of China’s population. Her work with the GBIC focused on developing actionable programmatic and policy recommendations for consideration by government agencies. She led the GBIC computational lab, which collected, wrangled and modeled data from government bureaus and other sources to support the research goals of agency partners and GBIC faculty. Her own research concentrated on understanding the applicability of data science approaches in social welfare research and practice settings.
Previously, Marla had spent twenty years conducting practice-based research in public and private organizations that provide health and human services in vulnerable communities. This included fifteen years with the Navajo Nation in Arizona, where she worked with local communities to develop health and social services evaluation approaches derived from traditional Navajo philosophy and values.
Marla earned her Masters of Social Work from the University of Washington in Seattle with a focus on planned social change. She received her PhD from the School of Social Welfare at Berkeley. Her dissertation explored government efforts to scale the use of evidence-based services. It used public government records and crowd-sourced and computational data-extraction methods to create measures of these strategies. It assessed the relative effects of these public strategies on scaling progress using time-to-event analysis. It found that county governments are well positioned to implement scaling strategies and that the proportion of social service providers adopting evidence-informed services can be increased as can the proportion of county funding directed to these organizations. This study design is highly replicable and as such provides a general model to apply to other local environments to identify common county levers that effectively promote the scaling of evidence-informed social services.