Feasibility and desirability of a realist CMOC database: Lessons learned

Central to realist evaluation is its focus on underlying generative mechanisms that cause outcomes in particular contexts, often presented as context–mechanism–outcome configurations. The first author brought together 22 realist evaluation experts in a Delphi technique-inspired exercise to explore the potential of creating a database from context–mechanism–outcome configurations identified in the empirical literature, to enhance learning about what context–mechanism–outcome configurations are and to further the cumulation of knowledge. The exercise, and the comments it evoked, showed why developing such a database is not straightforward and the views on its desirability and utility are mixed. The main reasons are the lack of common concepts to organize the database and the problem of the triple hermeneutic, different uses and non-uses of the context–mechanism–outcome configuration heuristic, and different perceptions of the quality of a context–mechanism–outcome configuration. The discussion points raised in this exercise offer valuable insights into realist reasoning and highlight interesting avenues for further research and debate.

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Publication Details

Journal
Evaluation
Published
2024-09-02
DOI
https://doi.org/10.1177/13563890241267732
Citations
1
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.71
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Feasibility and desirability of a realist CMOC database: Lessons learned

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Feasibility and desirability of a realist CMOC database: Lessons learned

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article en
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Abstract

Central to realist evaluation is its focus on underlying generative mechanisms that cause outcomes in particular contexts, often presented as context–mechanism–outcome configurations. The first author brought together 22 realist evaluation experts in a Delphi technique-inspired exercise to explore the potential of creating a database from context–mechanism–outcome configurations identified in the empirical literature, to enhance learning about what context–mechanism–outcome configurations are and to further the cumulation of knowledge. The exercise, and the comments it evoked, showed why developing such a database is not straightforward and the views on its desirability and utility are mixed. The main reasons are the lack of common concepts to organize the database and the problem of the triple hermeneutic, different uses and non-uses of the context–mechanism–outcome configuration heuristic, and different perceptions of the quality of a context–mechanism–outcome configuration. The discussion points raised in this exercise offer valuable insights into realist reasoning and highlight interesting avenues for further research and debate.

EvaluationVol. 30(4)
Université Libre de Bruxelles (BE), The University of Melbourne (AU), Aston University (GB), Charles Darwin University (AU), Maastricht University (NL), Université Mohammed VI des Sciences et de la Santé (MA), University of Plymouth (GB)
Openalex Percentile: Top 27%
Advanced Causal Inference Techniques
0.71
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