First Advisor
Ameeta Agrawal
Date of Award
Spring 6-13-2026
Document Type
Thesis
Degree Name
Bachelor of Science (B.S.) in Computer Science and University Honors
Department
Computer Science
Language
English
Subjects
imputation, longitudinal surveys, transformers, semantic embeddings, missing data
Abstract
Longitudinal surveys are ubiquitous in the social sciences as a means of tracking changes in behavior and opinions with time and identifying potential causal mechanisms. These surveys are frequently plagued by missing data and semantic drift, both of which limit their effectiveness and scientific utility. Imputation algorithms allow researchers to fill gaps in collected survey datasets, imperfectly reconstructing lost data. Although deep learning algorithms have been used in imputation to great success, approaches which simultaneously leverage the semantic and temporal structure of longitudinal surveys have not yet been developed. We propose a novel imputation architecture which is capable of leveraging both forms of metadata to improve imputation and evaluate its performance on the real-world Flu+COVIDPaths survey dataset.
Recommended Citation
Rezvani, Julia, "A Novel, Embedding-Based Approach to Longitudinal Survey Data Imputation" (2026). University Honors Theses. Paper 1882.