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What Is Qualitative Research? An Overview and Guidelines
Open accessAbstract
This guide explains the focus, rigor, and relevance of qualitative research, highlighting its role in dissecting complex social phenomena and providing in-depth, human-centered insights. The guide also examines the rationale for employing qualitative methods, underscoring their critical importance. An exploration of the methodology’s strengths, such as its ability to offer contextual depth and holistic perspectives, is juxtaposed against challenges like constrained generalizability and potential researcher bias, accompanied by practical mitigation strategies. Covering a spectrum of qualitative approaches, including grounded theory, phenomenology, ethnography, action research, and general inquiry, the guide sheds light on diverse data collection and analysis techniques. Noteworthily, the guide underscores the crucial aspect of trustworthiness in qualitative research, detailing methods to establish credibility, dependability, confirmability, and transferability. The integration of technologies like recording and transcribing tools with data analysis software and the growing influence of artificial intelligence, machine learning, and deep learning are also explored. Concluding with a discussion on ethical considerations, from participant recruitment to data stewardship, this guide serves as an essential resource that offers insightful, actionable guidance for conducting effective and impactful qualitative research.
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“Drawing on current literature and recent case studies from academic libraries, this article demonstrates that qualitative assessment, though sometimes perceived as time-and labor-intensive, can be feasibly integrated into existing workflows, even with limited resources.”
From Beyond Numbers (2025) · cites this paper as Lim, 2025
In the citing paper
“Qualitative methods are especially advantageous under such conditions because they are adaptable, low-cost, and scalable (Kohler, 2024;Lim, 2025).”
From Beyond Numbers (2025) · cites this paper as Lim, 2025
In the citing paper
“This study adopted a qualitative research design, which is appropriate for exploring meanings, expectations, and interpretations rather than measuring frequencies or testing hypotheses (Lim, 2024).”
From Use of artificial intelligence tools in the publishing process: expectations from publishers through author guidelines (2026) · cites this paper as Lim, 2024
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Self-citationSystematic Literature Reviews: Reflections, Recommendations, and Robustness Check
Lim, W. M. (2025) · Journal of Consumer Behaviour · doi:10.1002/cb.2479 · Cited by 76
Self-citationA Guide to a Mixed-Methods Approach to Healthcare Research
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Sample Size in Qualitative Research: Moving from Data Saturation to Theoretical Saturation
Lim, W. M. (2025) · Journal of Global Marketing · doi:10.1080/08911762.2025.2590757 · Cited by 84
Self-citation
Metadata
- Authors
- Lim, W. M.
- Affiliations
- Swinburne University of Technology (AU)
- Corresponding author
- Lim, Weng Marc
- ORCID
- Lim, Weng Marc
- Venue
- Australasian Marketing Journal
- Publisher
- SAGE Publications
- Published
- 2024-07-25
- Volume
- 33
- Issue
- 2
- Pages
- 199-229
- ISSN
- 1441-3582 (ISSN-L)
- 1839-3349
- OpenAlex
- W4401036688
- Cited by
- 985
- References
- 67
- Citations by year
- 2026: 553 · 2025: 392 · 2024: 40
- FWCI
- 1234.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Australasian Marketing Journal (AMJ) (publishedVersion, cc-by)
- Topics
- Social Sciences › Education › Qualitative Research Methods and Applications
- Social Sciences › Sociology and Political Science › Qualitative Research Methods and Ethics
- Social Sciences › Business, Management and Accounting › Strategy and Management › Business Strategies and Management Research
- Keywords
- qualitative research, grounded theory, phenomenology, ethnography, action research, trustworthiness, credibility, researcher bias, generalizability, data collection, data analysis, artificial intelligence in qualitative research, research ethics, data stewardship
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