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Qualitative Research Philosophy

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Bell Eapen edited this page Dec 29, 2025 · 1 revision

Qualitative Research Philosophy: A Primer for CRISP-T Users

CRISP-T is designed not just as a toolset, but as a methodological partner that respects the nuanced nature of qualitative research. To get the most out of it, it helps to understand the philosophical stance it supports.

1. Ontology: What is the nature of reality?

In data science (and positivist research), reality is often seen as objective and measurable—something "out there" waiting to be discovered.

CRISP-T adopts an Interpretivist/Constructivist Ontology. We believe that "reality" in social research is constructed through the meanings people ascribe to their experiences. There isn't one single "truth" about a dataset; there are multiple valid interpretations.

  • Your Role: You are not just extracting facts; you are constructing a narrative.
  • CRISP-T's Role: It acts as a mirror, showing you patterns (topics, clusters) that you might miss, but you decide if those patterns are meaningful.

2. Epistemology: How do we know what we know?

In quantitative research, the goal is often to be "objective" and remove the researcher from the equation.

CRISP-T embraces Subjectivity. We view the researcher's background, theory, and intuition as vital tools for sense-making.

  • Co-Creation: Knowledge is co-created by the interaction between the researcher and the data.
  • AI as Interlocutor: When you use CRISP-T's AI features, treat the AI not as an authoritative oracle, but as a colleague who might have a different (sometimes weird, sometimes brilliant) perspective. Debate with it!

3. Triangulation: Deepening Understanding

Triangulation is often misunderstood as "cross-checking" to see if different methods give the same result. If they don't, one must be wrong.

In CRISP-T, Triangulation is about Enrichment. We use multiple methods to get a fuller, more complex picture of the phenomenon.

  • Methodological Triangulation: Combining Qualitative (textual nuance) and Quantitative (numeric patterns) methods.
    • Example: Your interview transcripts (Qual) might show people feel "anxious," while your survey data (Quant) shows high "sleep quality." This contradiction is a finding in itself—perhaps they are anxious about sleeping well!
  • Data Triangulation: Using different data sources (e.g., Interviews + Tweets).
  • Theoretical Triangulation: Using different lenses (e.g., Feminist Theory + Systems Theory) to interpret the same results.

4. The "CRISP" Philosophy

  • Computational: We use power to handle scale.
  • Reflective: We constantly check our assumptions.
  • Inductive: We let the patterns emerge from the data first (bottom-up).
  • Sense-making: The goal is understanding, not just prediction.
  • Participatory: The researcher is an active participant in the analysis.

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