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Leveraging ground-breaking work of Black feminist scholars alongside established techniques of focus group and community-based participatory research, we explain sister-girl talk as a novel method for collecting and analyzing group interview data with Black women. We outline the procedures for consultation, facilitation and preliminary analysis of the sister-girl talk method.
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Bayesian Partial Pooling to Improve Inference Across A/B Tests in EDM
This paper will explain how analyzing experiments as a group can improve estimation and inference of causal effects– even when the experiments are testing unrelated treatments. The method, composed of ideas from meta-analysis, shrinkage estimators, and Bayesian hierarchical modeling, is particularly relevant in studies of educational technology. Analyzing experiments as a group–”partially pooling” their respective datasets–increases overall accuracy and avoids issues of multiple comparisons, while incurring small bias. The paper will explain how the method works, demonstrate it on a set of randomized experiments run within the ASSISTments platform, and illustrate its properties in a simulation study.
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- Award ID(s):
- 1724889
- PAR ID:
- 10095372
- Date Published:
- Journal Name:
- Proceeding of the Educational Data Mining Conference
- Page Range / eLocation ID:
- 521-524
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
