harnessing the power of data analysis to further understanding of complex systems
Joshua Gary Mausolf
Joshua is a Quantitative UX Researcher at Meta. He earned his PhD in Sociology from the Department of Sociology at the University of Chicago. His academic research utilizes computational, statistical, and experimental methods to study changes in partisan polarization within and across firms and how partisanship affects hiring and promotional behavior in corporations. Joshua leverages these skillsets, in combination with survey research methodology, to deliver high-impact cross-org UX research insights at Meta.
I completed my PhD, MA at the University of Chicago, Department of Sociology, my BA, summa cum laude from New York University, and was a research fellow at Data Science for Social Good. My dissertation examined the effects of partisan and affective polarization within firms, especially how partisanship affects hiring and careers. I have also published work in the study of social movements and their effect on public and political discourse. My CV and resume summarize my academic research and professional UX research experience, and a portfolio of select research projects can be found here.
Teaching
In teaching, I have emphasized helping individuals improve statistical skills, computational analyses, programming, and research design the undergraduate, graduate, and professional levels. A sampling of topics include multivariate regression modeling, factor analysis, multiple imputation, programming in R and Python, SQL, web scraping, natural language processing, and machine learning. I offer detailed information on my academic teaching under my courses page. Additionally, I served as a Senior Preceptor in the Masters of Computational Social Science program at the University of Chicago. I have continued my passion for teaching at Meta by instructing other UX researchers in the use of R and SQL.
Analyst
Practiced in statistical data analysis, I specialize in building econometric models for both linear and categorical data, including multiple imputation and time-series analyses. Beyond statistical modeling, I am well versed in the design and implementation of large-scale and international UX surveys. Beyond basic statistical skills, I am also adept at data wrangling and cleaning, and have led multiple data-driven projects.