The Design of Clustered Observational Studies in Education

Principal Investigator(s):  View help for Principal Investigator(s) Lindsay C. Page, University of Pittsburgh; Matthew A. Lenard, Harvard Graduate School of Education; Luke Keele, University of Pennsylvania

Version:  View help for Version V1

Name File Type Size Last Modified
Balance.R text/x-r-syntax 2.6 KB 09/02/2016 06:12:AM
Match_Myon_Final.R text/x-r-syntax 5 KB 11/10/2016 02:47:AM
Outcomes_Final.R text/x-r-syntax 2.1 KB 11/10/2016 03:12:AM
Test_of _Equivalance_Final.R text/x-r-syntax 1.4 KB 09/02/2020 06:54:AM
analysis_file.csv text/csv 429.6 KB 10/05/2020 11:16:AM
codebook.docx application/vnd.openxmlformats-officedocument.wordprocessingml.document 12.3 KB 09/10/2020 11:40:AM
myon_match.RData application/x-rlang-transport 7.6 MB 09/10/2020 11:40:AM
print_table.R text/x-r-syntax 462 bytes 09/04/2016 09:52:AM
test_equiv.R text/x-r-syntax 2.4 KB 11/10/2016 02:56:AM

Project Citation: 

Page, Lindsay C., Lenard, Matthew A., and Keele, Luke . The Design of Clustered Observational Studies in Education. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. https://doi.org/10.3886/E121381V1

Project Description

Summary:  View help for Summary 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.
Funding Sources:  View help for Funding Sources Spencer Foundation (201900074)

Scope of Project

Subject Terms:  View help for Subject Terms causal inference; hierarchical/multilevel data; observational study; optimal matching
Geographic Coverage:  View help for Geographic Coverage North Carolina

Methodology

Data Source:  View help for Data Source School district administrative data
Unit(s) of Observation:  View help for Unit(s) of Observation Individual

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Published Versions

V1 [2020-11-23]

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