| Title: |
Addressing Missing Data Due to COVID-19: Two Early Childhood Case Studies |
| Language: |
English |
| Authors: |
Avi Feller; Maia C. Connors; Christina Weiland; John Q. Easton; Stacy B. Ehrlich; John Francis; Sarah E. Kabourek; Diana Leyva; Anna Shapiro; Gloria Yeomans-Maldonado |
| Source: |
Journal of Research on Educational Effectiveness. 2025 18(1):226-245. |
| Availability: |
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: |
Y |
| Page Count: |
20 |
| Publication Date: |
2025 |
| Sponsoring Agency: |
Institute of Education Sciences (ED) |
| Contract Number: |
R305A180510; R305B150012; R305B170015 |
| Document Type: |
Journal Articles; Reports - Research |
| Education Level: |
Early Childhood Education; Elementary Education; Kindergarten; Primary Education; Preschool Education; Grade 1; Grade 2; Grade 3 |
| Descriptors: |
COVID-19; Pandemics; Data Collection; Educational Research; Research Methodology; Research Projects; Data Analysis; Cohort Analysis; Kindergarten; Hispanic American Students; Academic Achievement; Cognitive Ability; Culturally Relevant Education; Intervention; School Districts; Preschool Education; Access to Education; Enrollment; Standardized Tests; Grade 1; Grade 2; Grade 3; Multivariate Analysis; Longitudinal Studies; Randomized Controlled Trials; Early Childhood Education; Models; Decision Making; Educational Policy |
| Geographic Terms: |
Illinois (Chicago) |
| DOI: |
10.1080/19345747.2024.2321438 |
| ISSN: |
1934-5747; 1934-5739 |
| Abstract: |
One part of COVID-19's staggering impact on education has been to suspend or fundamentally alter ongoing education research projects. This article addresses how to analyze the simple but fundamental example of a multi-cohort study in which student assessment data for the final cohort are missing because schools were closed, learning was virtual, and/or assessments were canceled or inconsistently collected due to COVID-19. We argue that current best-practice recommendations for addressing missing data may fall short in such studies because the assumptions that underpin these recommendations are violated. We then provide a new, simple decision-making framework for empirical researchers facing this situation and provide two empirical examples of how to apply this framework drawn from early childhood studies, one a cluster randomized trial and the other a descriptive longitudinal study. Based on this framework and the assumptions required to address missing data, we advise against the standard recommendation of adjusting for missing outcomes (e.g., via imputation or weighting). Instead, we generally recommend changing the target quantity by restricting to fully observed cohorts or by pivoting to focusing on an alternative outcome. |
| Abstractor: |
As Provided |
| IES Funded: |
Yes |
| Entry Date: |
2026 |
| Accession Number: |
EJ1492476 |
| Database: |
ERIC |