A note on theoretical sampling

 A note on theoretical sampling

On 5 main ideas of theoretical sampling and describe 2 main claims of it in terms of Toulmin's model of arguments.

    Theoretical sampling, a core technique in grounded theory methodology developed by Glaser and Strauss, involves iteratively selecting data sources based on emerging theoretical insights rather than predefined populations, aiming for conceptual density until saturation. It contrasts with probability sampling by prioritizing theory development over statistical representation.

Main Ideas of Theoretical Sampling

·        Sampling decisions emerge from ongoing data analysis: Initial purposive cases generate categories, guiding subsequent selections to test, refine, or saturate them.

·        Theoretical saturation ends the process: Data collection stops when new cases yield no novel insights, ensuring comprehensive category development.

·        It favors deviant and maximum variation cases: Researchers seek negative instances or diverse examples to challenge and elaborate emerging theory, avoiding confirmation bias.

·        Not pre-planned but jointly collects, codes, and analyzes: Flexible, inductive-deductive process builds rigor in qualitative theory generation.

·        Focuses on conceptual relevance over representativeness: Samples theoretical units (e.g., processes, types) tied to the phenomenon, ignoring demographics unless theoretically pertinent.

Claims in Toulmin's Model

Claim 1: Theoretical sampling generates robust, grounded theory. Data: Iterative case selection refines categories via saturation, as in Glaser/Strauss's awareness of dying studies. Warrant: Analysis-driven choices ensure data fits emerging concepts; random sampling misses theoretical depth. Backing: Grounded theory's success in sociology/health validates precision over breadth. Qualifier: Best for exploratory theory-building.

Claim 2: Seeking deviant cases strengthens explanatory power. Data: Negative instances prompt reformulation, maximizing variation for robust boundaries (e.g., discourse analysis). Warrant: Confirmation bias weakens theory; challenges build density/complexity. Backing: Methodology's emphasis on exhaustive category properties confirms universality within scope. Rebuttal: Impractical for large-scale description.