The Design of Clustered Observational Studies in Education

Version
V0
Resource Type
Dataset
Creator
  • Page, Lindsay C. (University of Pittsburgh)
  • Lenard, Matthew A. (Harvard Graduate School of Education)
  • Keele, Luke (University of Pennsylvania)
Publication Date
2020-11-23
Funding Reference
  • Spencer Foundation
    • Award Number: 201900074
Free Keywords
causal inference; hierarchical/multilevel data; observational study; optimal matching
Description
  • Abstract

    Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practices for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in a large U.S. school district.
Geographic Coverage
  • North Carolina
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Has version
    DOI: 10.3886/E121381V1
Publications
  • Page, Lindsay C., Matthew A. Lenard, and Luke Keele. “The Design of Clustered Observational Studies in Education.” AERA Open 6, no. 3 (July 2020): 233285842095440. https://doi.org/10.1177/2332858420954401.
    • ID: 10.1177/2332858420954401 (DOI)

Update Metadata: 2020-11-24 | Issue Number: 1 | Registration Date: 2020-11-24