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Cited by 213

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Inter-Rater Reliability Methods in Qualitative Case Study Research

Open access
Cole, Rosanna·2024·Sociological Methods & Research

Abstract

The use of inter-rater reliability (IRR) methods may provide an opportunity to improve the transparency and consistency of qualitative case study data analysis in terms of the rigor of how codes and constructs have been developed from the raw data. Few articles on qualitative research methods in the literature conduct IRR assessments or neglect to report them, despite some disclosure of multiple researcher teams and coding reconciliation in the work. The article argues that the in-depth discussion and reconciliation initiated by IRR may enhance the findings and theory that emerges from qualitative case study data analysis, where the main data source is often interview transcripts or field notes. To achieve this, the article provides a missing link in the literature between data gathering and analysis by expanding an existing process model from five to six stages. The article also identifies seven factors that researchers can consider to determine the suitability of IRR to their work and it offers an IRR checklist, thereby providing a contribution to the broader literature on qualitative research methods.

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Metadata

Authors
Cole, R.
Venue
Sociological Methods & Research
Published
2023-02-22
Volume
53
Issue
4
Pages
1944-1975
ISSN
  • 0049-1241 (ISSN-L)
  • 1552-8294
OpenAlex
W4321599876
Cited by
213
References
10
FWCI
157.90
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Inter-Rater Reliability Methods in Qualitative Case Study Research. https://app.cochat.ai/papers/10.1177/00491241231156971
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Cole, R. (2024). Inter-Rater Reliability Methods in Qualitative Case Study Research. Sociological Methods & Research. https://doi.org/10.1177/00491241231156971

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.