A large part of my quantitative and survey methodology training comes from working at large-scale social surveys.
- I was mainly in charge of cleaning the 2014 and 2016 child datasets. Key procedures include clarifying valid samples, checking if the data's skip logic is correct, rectifying abnormal values and filling in missing values based on interview records and interviewers' notes etc.
- I also participated in generating variables such as household income, child test scores, and occupational prestige scores.
- Before the data became publicly available, I ran analyses to check data quality and cross-year consistency
After starting my doctoral training at the University of Maryland, I joined the IHDS team as a research assistant. IHDS is a nationally representative and longitudinal survey of Indian households.
Working for large-scale social surveys has given me special insights when using survey data for analysis. I am particularly interested in how the design of survey questions and the context/environment of conducting interviews would influence data quality.
I wrote two reports on how the design of survey questions on paid work would influence data quality on labor force participation:
- Stone, Eric, and Xu Yan. 2020. Measurement Brief: Paid Work. Women’s Empowerment: Data for Gender Equality
- Ji, Yingchun, and Xu Yan. 2020. Measuring Paid Work in China: Lessons Learnt from The China Family Panel Study (CFPS). Women’s Empowerment: Data for Gender Equality.