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Design-Based Approaches to Causal Replication Studies


Recent interest in promoting replication efforts assumes 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. The paper describes research designs for replication and demonstrates how multiple designs may be combined in systematic replication efforts, as well as how diagnostic measures may be used to assess the extent to which CRF assumptions are met in field settings.

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  1. Currently, there is no standard approach for determining replication failure. Researchers often compare the direction, size, and statistical significance patterns of study effects; they have also examined statistical tests of difference and/or equivalence of study results. In this article, we will define replication failure as statistical differences in two or more study effect estimates.

  2. The replication effort actually consisted of six individual RCTs and five replication study designs. We limit our discussion to include only the first three RCTs and replications studies because of space considerations. Results of the systematic conceptual replication study is available at Krishnamachari (2021).

  3. A full review of how researchers may apply semantic similarity methods is beyond the scope of this paper, but we provide readers with an intuition for the approach here. To quantify the similarity between texts, researchers represent texts numerically by their relative word frequencies or by the extent to which they include a set of abstract topics. After each transcript is represented as a numerical vector, researchers calculate the similarity of vectors by measuring the cosine of the angle between them. Two texts that share the same relative word frequencies will have a cosine similarity of one and two texts that share no common terms (or concepts) will be perpendicular to one-another and have a cosine similarity of 0. Importantly, semantic similarity methods create continuous measures which can be used to identify studies where treatments were delivered more or less consistently, or with more or less adherence. Anglin and Wong (2020) describe the method and provide an example of how it may be used in replication contexts.


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The research reported here was supported by the Institute of Education Sciences, US Department of Education, through Grant #R305B140026 and Grant #R305D190043 to the Rectors and Visitors of the University of Virginia.

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Correspondence to Vivian C. Wong.

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Approval was obtained from the ethics committee of University of Virginia. The procedures used in this study adhere to the tenets of the Declaration of Helsinki (Ethics approval numbers: 2170, 2727, 2875, 2918).

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Informed consent was obtained from all individual participants included in the study.

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The authors declare no competing interests.

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Wong, V.C., Anglin, K. & Steiner, P.M. Design-Based Approaches to Causal Replication Studies. Prev Sci 23, 723–738 (2022).

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  • Replication
  • Causal inference
  • Open science