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Morse, J. M. (2009). Sampling in Grounded Theory. The SAGE Handbook of Grounded Theory, 229-244. doi:10.4135/9781848607941.n11

By Janice M. Morse

Q: Would determining the interviewees in advance contradict GT?

Principles of Sampling for Quantitative Inquiry

Principle 1: Excellent research skills are essential for obtaining good data

  • The better the interview, the less interviews you’ll need
  • Quickly gain trust of the interviewees to get accurate information (data) from them

Principle 2: It is necessary to locate “excellent” participants to obtain excellent data

  • Excellent interviewees are: experienced, articulate, willing, available
  • Strategize sampling based on conceptual information needs of the study; make it valuable for the interviewee

Principle 3: Sampling techniques must be targeted and efficient

  • Too much data is hard to analyze and sift through; leads to “conceptual blindness”
  • Excellent qualitative inquiry is biased – but this doesn’t necessarily mean the research is bad
  • You have to stick to analyzing the best examples
  • Theoretical saturation cannot be reached in randomized sampling

Principles of Sampling in GT

Main types of sampling:

  1. Convenience Sampling
    • Finding people available to scope the boundaries of phenomenon to explore
    • Leads to snowball sampling
  2. Purposeful Sampling
    • Looking for participants in a certain stage (which was identified in convenience sampling)
    • Confirms trajectory of research
    • Biased process, but it’s ok – leads to saturation
    • “Shadowed data” = participants speaking for others
    • You sample the next participant based on concepts in your current data. If person A talks about the influence of x types of people, you want to interview people with x characteristics
  3. Theoretical Sampling
    • Emerging categories and developing theories guide the sampling
    • How should I articulate the way I will be changing the sampling of interviews in my proposal?
  4. Theoretical Group Interviews
    • Fill in missing pieces of puzzle
    • Researcher presents ongoing analysis and asks to fill in the “thin” areas

Terminating Data Collection

  • Occurs when characteristics of instances are constant
  • Ex. Modesty as a cultural value for Fiji-Indians interferes with maternal and infant health

Sampling Data

  • Researchers may disregard some data that’s not helpful or irrelevant
  • Not all data will be the same – some will have better descriptions than others
  • The better examples will be used to draw categories and theories from
  • This is not biased because not all data is equal