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ERIC Number: ED519508
Record Type: Non-Journal
Publication Date: 2011
Pages: 5
Abstractor: ERIC
Reference Count: 7
Matching Strategies for Observational Data with Multilevel Structure
Steiner, Peter M.
Society for Research on Educational Effectiveness
Given the different possibilities of matching in the context of multilevel data and the lack of research on corresponding matching strategies, the author investigates two main research questions. The first research question investigates the advantages and disadvantages of different matching strategies that can be pursued with multilevel data structures. The goal is first to outline possible matching strategies and then to identify an optimal matching strategy for different treatment selection scenarios. In following Hong & Raudenbush (2006), theoretical foundations are discussed within the Rubin Causal Model framework and its potential outcomes notation (Rubin, 1974). The second research question focuses on the matching of students in more detail. For hierarchical data structures, theoretical considerations and preliminary simulation results clearly indicate that matching approaches for causal inference need to reflect the (multilevel) selection process. If matching does not reflect the selection process that actually took place biased treatment effect may result. If selection takes place at the student level (within schools) one should match students within school. Matching students between schools may result in biased treatment effects. However, if treatment and control students cannot be matched within schools, matching students between schools might still be considered but stronger assumptions are required. If treatment selection takes place at the school level an individual-student matching is not necessarily required.
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Publication Type: Reports - Research
Education Level: Elementary Secondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: Society for Research on Educational Effectiveness (SREE)