3.4 Qualitative Data Collection — Notes

  • Open vs Semi-Structured Interviews
    • Open: The researcher and participants talk freely about the topic, without a guide.
    • Semi-Structured: The researcher follows a question guide, but can explore interesting points in more detail.
  • Data collection methods in qualitative research
    • Qualitative data focuses on collecting and analyzing text-based data rather than numbers; data often comes from spoken or written texts, but can also include symbols, observations, or artifacts expressed in words.
    • Common data collection methods:
    • Interviews: Asking people questions in detail to understand their thoughts or experiences.
    • Observations: Watching behavior or events and recording them in detail.
    • Text analyses: Studying documents, articles, or other written material to find patterns and meanings.
    • Qualitative research aims to understand meanings, experiences, and context, not counting or measuring things.
    • Qualitative data collection usually happens through conversations, such as individual interviews, expert talks, or group discussions.
  • Fieldwork and data collection settings
    • Data is often collected in the field, such as at the interviewee’s workplace.
    • Qualitative data is typically collected through conversations: individual interviews, expert talks, or group discussions.
  • Data collection formats and context
    • Open and semi-structured conversations are common formats.
    • Observations focus on watching behavioral patterns, including interactions during conversations.
    • Text analysis involves studying documents or written material to interpret their meaning.
    • In qualitative research, the researcher’s perspective is part of the process, enabling deeper insights than in quantitative research.
    • Researchers can ask follow-up questions if something isn’t understood or if something interesting arises.
  • Sampling and fieldwork practicality
    • Qualitative data is usually collected in the field and often uses small samples because analyzing large amounts of text is very time-consuming.
  • Data analysis: overall approach
    • Analysis in qualitative research is mainly interpretative, working with text-based data to find patterns or meanings.
    • Common analytical approaches include methods by Mayring (2000, 2022) and Kuckartz (2014), which are useful resources for this kind of analysis.
    • Analyzing qualitative data should be systematic and clear (Veal, 2018, p. 465).
    • Today, software can help process large amounts of text more easily.
  • Main goal of qualitative data analysis
    • The main goal is to sort interview statements into categories.
    • Categories can be:
    • Predefined, or
    • Developed from the data itself (Mayring, 2000).
    • Example: if employees from different sectors are asked what motivates them, analysis might reveal that non-material incentives—such as praise, recognition, or development opportunities—are the most motivating.
    • As more interviews are analyzed, these categories are checked and adjusted if needed.
  • Evolution of categories over the study
    • Unlike quantitative research, where response options are fixed in advance, qualitative categories evolve throughout the study until the analysis is complete.
    • This evolving approach allows researchers to capture nuances and patterns that emerge naturally from the participants’ responses.
  • Connections to broader concepts and implications
    • Qualitative data collection emphasizes understanding meanings and contexts, rather than numerical counts.
    • The approach supports in-depth insights, reflection on real-world settings, and iterative theory-building.
  • References and sources cited in the material
    • Veal (2018): qualitative data collection and the importance of systematic, clear analysis; pp. 136, 286, 465.
    • Saunders et al. (2019): qualitative data collection basics; p. 180; p. 179.
    • Mayring (2000, 2022): coding approaches and category development.
    • Kuckartz (2014): qualitative data analysis methods.
  • Practical takeaways for exams and research practice
    • Distinguish between open vs. semi-structured formats.
    • Recognize that qualitative data emphasizes meaning, context, and experiences over counts.
    • Expect small sample sizes and field-based data collection.
    • Be prepared to explain how categories can evolve and why iterative refinement is essential in qualitative analysis.
  • Note on terminology and framing
    • Qualitative data analysis is interpretative and text-centered; aim for transparent, repeatable coding procedures.
    • The researcher’s perspective is not separate from the analysis but integrated into the interpretive process.
  • Quick recap of the workflow
    • Collect: open/semi-structured conversations, field-based data.
    • Analyze: interpretative coding to identify patterns.
    • Category development: predefined or data-driven, evolving with additional interviews.
    • Validate: refine categories as analysis proceeds to capture nuances.