Graduating CSSS-track PhDs present research, reflections on experiences with CSSS
Graduate students on the CSSS track who are finishing PhDs this year will present their research at a seminar on Wednesday, April 26, at 12:30 p.m. in Savery Hall room 409.
Students who are presenting include:
Michael Pearce is a graduate student in the Department of Statistics.
- Title: Statistical Preference Modeling in the Social Sciences.
- Description: Rankings and ratings are commonly used to express preferences over a given collection of items, such as movies, grant proposals, or political candidates. These distinct data types are usually collected and modeled separately, yet their complementary properties suggest joint analysis may improve preference analysis. I will present the first joint statistical models for rankings and ratings and describe their use in social science contexts.
Zhe Liu is a graduate student in the Department of Economics.
- Title: Who benefits from better Internet connectivity? Evidence from the labor market in South Africa
- Description: In this study, I explore the impact of fast internet availability on the labor market in South Africa, with a specific focus on job search behaviors and the role of online job information in substituting personal connections. Despite the increased use of online job search with the availability of fast internet, the reliance on personal networks remains unchanged.
Yulan Kim is a graduate student at the Daniel J. Evans School of Public Policy & Governance.
- Title: Do Collaborative Platforms Breed Trust? A Repeated Measures Study of Trust in Social Security Consultative Bodies (SSCBs)".
- Description: This paper questions whether participating in government-led collaborative governance enhances trust. I use multilevel models to analyze repeated measures data collected from a panel of SSCB participants over a 16-month period. Preliminary findings reveal that as a result of participating in government-led collaborative governance, participants show an increase in individual-level trust but no change in institutional-level trust. Results also suggest that different factors affect trust at individual and institutional levels.
Eunice Jun is a graduate student in Computer Science and Engineering.
- Eunice Jun’s research combines ideas and techniques from human-computer interaction, programming languages/software engineering, and statistics. In January, Eunice gave a full-length CSSS seminar on her work on data analysis tools for statistical non-experts and we are excited to feature her as a graduating student as well.
Clockwise from top left: Yulan Kim Zhe Liu, Eunice Jun, Michael Pearce
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