 Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTsPeter Z. Schochet
Journal of Educational and Behavioral Statistics, 2018, vol. 43, issue 5, 568-593 Abstract: Design-based methods have recently been developed as a way to analyze randomized controlled trial (RCT) data for designs with a single treatment and control group. This article builds on this framework to develop design-based estimators for evaluations with multiple research groups. Results are provided for a wide range of designs used in education research, including clustered and blocked designs. Because analysis in the multi-armed setting involves pairwise contrasts across the research groups, the key methodological question addressed is: How do the estimators for the two-group design need to be adjusted for multi-armed trials? The critical insight is that in multi-armed trials where the goal is to identify the most effective treatments, the samples for each pairwise contrast are representative of the full set of randomized units, not just of themselves. The implication is that variance terms need to be adjusted slightly under the finite-population framework that can reduce precision, and blocks need to be weighted to reflect the full randomized sample in the block or biases can result. An empirical example using data from a multi-armed education RCT demonstrates the issues. Keywords: multi-armed RCTs; average treatment effects; design-based estimators; impact and variance estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:43:y:2018:i:5:p:568-593 Access Statistics for this article More articles in Journal of Educational and Behavioral Statistics |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.