5 Qualitative Research Methods

Foundations and research approaches

Qualitative research explores how people interpret experiences, relationships, and social settings. It primarily uses words, actions, and other meaning-rich evidence rather than numerical measurement to understand how or why something happens, how it is experienced, or what meanings people give it.

A coherent study matches its research question with its approach, participants, data-collection methods, and analysis. The question should guide these choices so that the evidence gathered and the way it is interpreted are suited to what the study aims to understand.

Matching the question to an approach

A qualitative approach is the overall logic of inquiry; a data-collection method, such as an interview or observation, can be used within different approaches. The approaches are not interchangeable labels, so choose one that fits the research question and ensure the proposed analysis also suits it.

• Phenomenology investigates how people experience a shared phenomenon, such as adjusting to a new diagnosis.

• Ethnography examines the practices and meanings of a culture or group, usually through sustained observation in context.

• Grounded theory develops an explanation of a social process from systematically gathered and analyzed data.

• Case study explores a bounded case, such as one school, program, or organization, in depth and in context.

Selecting participants

Qualitative studies usually select participants deliberately: they seek people or settings likely to provide relevant, detailed perspectives rather than a statistically representative sample. A sampling plan should state whom the study needs, how those participants will be recruited, and why the choices fit the research question.

Common strategies include:

• Criterion sampling: participants meet a defined condition.

• Maximum-variation sampling: participants differ on important characteristics.

• Snowball sampling: participants refer others.

• Theoretical sampling: additional participants or settings are chosen as emerging analysis identifies what needs further investigation.

For example, a study of new nurses’ experiences might recruit nurses in their first year and deliberately include different shifts or clinical settings. The example illustrates purposeful selection suited to the topic, not a requirement that every study use those characteristics.

Judging sample adequacy

Sample size depends on the scope of the question, the richness and variety of the data, the approach, and the practical context. Researchers may use saturation—the point at which further data add little relevant insight—as one guide, but it is not a universal numerical threshold. Explain how sample adequacy was judged rather than relying on a fixed rule.

Gathering interviews and observations

Semi-structured interviews use a guide of open-ended questions while allowing follow-up questions. Neutral prompts such as “Can you tell me more?” and “What happened next?” invite elaboration. For instance, “How did you decide whether to ask for help?” invites a fuller account than “Was it difficult?”

Pilot the interview guide, listen without steering answers, and adapt questions when new relevant topics emerge. These practices help the interview remain responsive while keeping questions focused on the research topic.

Observation records what people do and how interactions unfold in a setting. Researchers may participate to varying degrees or observe without joining in; the chosen role affects what can be seen and how participants behave. Field notes should distinguish concrete description—who did what, where, and when—from the researcher’s reflections and early interpretations.

Interviews and observations can complement each other: what people say they do can be considered alongside what happens in practice.

Coding and interpreting data

Analysis begins during data collection, not only after it ends. Researchers review recordings, transcripts, and field notes, become familiar with the material, and note patterns, surprises, and unanswered questions.

Coding labels meaningful portions of data so they can be compared. Codes may be developed inductively from the material, deductively from concepts or questions established in advance, or through a clearly explained combination. For example, an interview statement such as “I didn’t want to bother anyone” might be coded as fear of burdening colleagues. Comparing codes across accounts may help develop a broader pattern, such as concealing uncertainty to appear competent.

A theme is not merely a repeated topic; it is an interpretive pattern that helps answer the research question. In thematic analysis, researchers move iteratively through familiarization, coding, developing and reviewing themes, defining them, and writing the analysis.

Interpretation requires attention to context and to the researcher’s role in producing knowledge. Keep analytic notes about why codes or themes changed, examine accounts that challenge an emerging pattern, and support claims with relevant evidence. A quotation can illustrate an interpretation, but it does not by itself establish that a finding applies to everyone.

Assessing trustworthiness

Qualitative quality is often discussed through four related criteria. They help readers assess the evidence and research process, but no single checklist guarantees quality; practices should fit the study’s approach.

• Credibility: Are interpretations well supported by the evidence? Strategies include sustained engagement with data, comparing different sources or perspectives, and examining contradictory cases.

• Transferability: Can readers judge whether findings may be useful in another context? Describe the setting, participants, and research process clearly.

• Dependability: Is the research process documented well enough for readers to understand how decisions were made? Keep a record of methodological changes and analytic steps.

• Confirmability: Can readers trace interpretations to the data rather than only to the researcher’s preferences? Preserve an audit trail, use evidence transparently, and reflect on assumptions and relationships that may shape the study.

Ethical practice and reflexivity

Research involving people should respect their autonomy, minimize harm, and recruit fairly. Explain the study in accessible language, obtain voluntary informed consent, protect privacy and confidentiality, and store recordings and transcripts securely.

Consider risks particular to the topic or setting. For example, consider whether a participant’s comments could affect their employment or relationships. Reflect on power differences, how the researcher’s presence shapes the data, and whether participants can decline or withdraw without pressure. Seek appropriate institutional review before beginning research involving human participants.

Bringing the research process together

A strong qualitative study builds context-rich understanding through a coherent match among the research question, approach, participant selection, data collection, and analysis. Researchers gather detailed accounts or observations, code and interpret patterns iteratively, and explain how evidence supports their conclusions. Reflexivity, transparent methods, and careful ethical practice help readers assess the trustworthiness of the findings.