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Design-Based Approaches to Causal Replication Studies

Recent interest to promote and support replication efforts assume that there is well-established methodological guidance for designing and implementing these studies. However, no such consensus exists in the methodology literature. This article addresses these challenges by describing design-based approaches for planning systematic replication studies. Our general approach is derived from the Causal Replication Framework (CRF), which formalizes the assumptions under which replication success can be expected. The assumptions may be understood broadly as replication design requirements and individual study design requirements. Replication failure occurs when one or more CRF assumptions are violated. In design-based approaches to replication, CRF assumptions are systematically tested to evaluate the replicability of effects, as well as to identify sources of effect variation when replication failure is observed. In direct replication designs, replication failure is evidence of bias or incorrect reporting in individual study estimates, while in conceptual replication designs, replication failure occurs because of effect variation due to differences in treatments, outcomes, settings, and participant characteristics. The paper demonstrates how multiple research designs may be combined in systematic replication studies, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.    

Replication, causal inference, open science, methodology
Education level
10/2020476.4 KB
01/2021560.99 KB
Document Object Identifier (DOI)

EdWorkingPaper suggested citation:

Wong, Vivian C., Peter M. Steiner, and Kylie L. Anglin. (). Design-Based Approaches to Causal Replication Studies. (EdWorkingPaper: 20-311). Retrieved from Annenberg Institute at Brown University:

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