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Methodologic and Data-Analysis Triangulation in Case Studies: A Scoping Review
Margarithe Charlotte Schlunegger1,2, Maya Zumstein-Shaha1,2, Rebecca Palm2,3
1Department of Health Professions, Applied Research & Development in Nursing, Bern University of Applied Sciences, Bern, Switzerland.
This scoping review highlights that while methodologic and data-analysis triangulation processes are described in nurse practitioner case studies, they lack detail. Improved reporting is needed for standardization and research traceability in nursing.
Area of Science:
- Nursing Research
- Health Services Research
- Methodology
Background:
- Triangulation is a key method for enhancing the validity of case study research.
- Understanding triangulation processes in nurse practitioner primary health care research is crucial for methodological rigor.
Purpose of the Study:
- To explore the processes of methodologic and data-analysis triangulation in case studies.
- To examine the application of triangulation in research focusing on nurse practitioners in primary health care.
Main Methods:
- A scoping review was conducted using Arksey and O'Malley's framework.
- Searches included MEDLINE and CINAHL databases, hand searching nursing journals, and reference list examination.
- Studies published in English or German before August 2023, defining a case study design and using multiple data sources, were included.
Main Results:
- Eight articles were included, with five describing within-method triangulation and three detailing between/across-method triangulation.
- Data collection methods included interviews, observation, documentation, service records, and questionnaires.
- Data analysis involved various qualitative and quantitative methods, but details on comparing results were often lacking.
Conclusions:
- Processes for methodologic and data-analysis triangulation in case studies lack sufficient detail, hindering standardization and research traceability.
- Terminological confusion complicates triangulation in nursing research.
- Authors should critically reflect on triangulation processes and utilize tools like protocols or mixed-methods matrices for transparent reporting.
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