|Title:||Statistical Innovations for Clustered Observational Studies|
|Principal Investigator:||Keele, Luke||Awardee:||University of Pennsylvania|
|Program:||Statistical and Research Methodology in Education [Program Details]|
|Award Period:||3 years (07/01/2021 - 06/30/2024)||Award Amount:||$899,023|
|Type:||Methodological Innovation||Award Number:||R305D210014|
Co-Principal Investigator: Page, Lindsay
In many observational research settings in education, the treatment is allocated to entire clusters of students, instead of to individual students. These clustered observational studies (COSs) arise when the treatment is applied at the group level (e.g., teachers, classes, or schools) with outcomes of interest measured at the student level. The intervention is not randomized, but it is clustered such that all students within a given cluster are treatment or control students. There is very little research on the best way to design COSs and on statistical methods for the analysis of data from a COS.
The purpose of this research is to develop causal estimands for COSs, guidelines for their use, and software for computing the estimands. The development and guidelines will involve theoretical work and Monte Carlo simulation studies. The software will be an open-source R package for which the research team will also create vignettes, a user's guide, and other training materials. The theoretical work and the software package will be published via journal manuscripts, conference presentations, and seminars.