Weilin Li’s research focuses on evaluation of early childhood programs, fidelity of curriculum implementation, dosage and quality in early care, and advanced statistical methods.
Weilin was involved in several research projects funded by the IES and NIH to examine impacts of quality care on school readiness. In addition, she was the residential methodologist for the meta-analysis project in National Forum on Early Childhood Programs and Policy. She has conducted quantitative analysis using advanced methods including propensity score matching, multiple imputation, hierarchical linear modeling, growth curve modeling, and instrumental variable approach. She has worked with multi-site datasets, including the NICHD Study of Early Care and Youth Development, the National Head Start Impact Study, and the Early Head Start Research and Evaluation Program.
Weilin has authored and co-authored papers published in Child Development, Developmental Psychology, and the Journal of Social Issues. She has also presented her work at many conferences, including annual conferences of the Society of Research on Educational Effectiveness (SREE), the Association for Public Policy Analysis & Management (APPAM), and the Society for Research on Child Development (SRCD). She serves as a reviewer for Child Development and Early Childhood Research Quarterly and the SRCD Theme meeting of developmental methodology.
Some of Weilin’s analyses involve the “big data” issue and can be compute-intensive. Therefore, Weilin has explored advanced techniques such as parallel algorithms in Python, MapReduce, GPGPU, and more. Weilin is an EMC certified data scientist.