Date of Award


Document Type


Degree Name


Organizational Unit

Morgridge College of Education, Research Methods and Information Science, Research Methods and Statistics

First Advisor

P. Bruce Uhrmacher

Second Advisor

Paul Sutton

Third Advisor

Antonio Olmos

Fourth Advisor

Jing Li


Educational attainment predictions, Hierarchical linear models, Multi-source and multidimensional data, Power analysis, Spatial temporal developmental trajectories


Education is a human right, and equal access to education is not only crucial for an individual’s well-being, but also essential for eradicating poverty, ensuring long-term prosperity for all, transforming the society, and achieving sustainable development. Measuring education development, especially the variations of educational attainment, in a timely and accurate manner can help educators, practitioners, scientists, and policymakers compare and evaluate various education indicators at both subnational and national levels. This research presents an approach that combines multi-source and multidimensional data including population distribution, human settlement, and education data to assess and explore educational attainment trajectories at both national and subnational levels across multiple years. In addition, this study contributes to the power discussions by validating the robustness of models using replication datasets with missing values.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Bingxin Qi


Received from ProQuest

File Format




File Size

149 pgs


Statistics, Geography