Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 1 |
| Since 2017 (last 10 years) | 2 |
| Since 2007 (last 20 years) | 2 |
Descriptor
Author
| Botelho, A. F. | 1 |
| Erickson, J. A. | 1 |
| Gagnon-Bartsch, J. A. | 1 |
| Heffernan, N. T. | 1 |
| Jia Quan | 1 |
| Miratrix, L. W. | 1 |
| Sales, A. C. | 1 |
| Walter Leite | 1 |
| Wei Li | 1 |
| Wu, E. | 1 |
Publication Type
| Reports - Research | 2 |
Education Level
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkWei Li; Walter Leite; Jia Quan – Society for Research on Educational Effectiveness, 2023
Background: Multilevel randomized controlled trials (MRCTs) have been widely used to evaluate the causal effects of educational interventions. Traditionally, educational researchers and policymakers focused on the average treatment effects (ATE) of the intervention. Recently there has been an increasing interest in evaluating the heterogeneity of…
Descriptors: Artificial Intelligence, Identification, Hierarchical Linear Modeling, Randomized Controlled Trials
Download full textGagnon-Bartsch, J. A.; Sales, A. C.; Wu, E.; Botelho, A. F.; Erickson, J. A.; Miratrix, L. W.; Heffernan, N. T. – Grantee Submission, 2019
Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…
Descriptors: Randomized Controlled Trials, Educational Research, Prediction, Algorithms
