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(I Can’t Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research

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  • Frank J van Rijnsoever

Abstract

I explore the sample size in qualitative research that is required to reach theoretical saturation. I conceptualize a population as consisting of sub-populations that contain different types of information sources that hold a number of codes. Theoretical saturation is reached after all the codes in the population have been observed once in the sample. I delineate three different scenarios to sample information sources: “random chance,” which is based on probability sampling, “minimal information,” which yields at least one new code per sampling step, and “maximum information,” which yields the largest number of new codes per sampling step. Next, I use simulations to assess the minimum sample size for each scenario for systematically varying hypothetical populations. I show that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population. Moreover, the minimal and maximal information scenarios are significantly more efficient than random chance, but yield fewer repetitions per code to validate the findings. I formulate guidelines for purposive sampling and recommend that researchers follow a minimum information scenario.

Suggested Citation

  • Frank J van Rijnsoever, 2017. "(I Can’t Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
  • Handle: RePEc:plo:pone00:0181689
    DOI: 10.1371/journal.pone.0181689
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    References listed on IDEAS

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    1. Dustin J. Bluhm & Wendy Harman & Thomas W. Lee & Terence R. Mitchell, 2011. "Qualitative Research in Management: A Decade of Progress," Journal of Management Studies, Wiley Blackwell, vol. 48(8), pages 1866-1891, December.
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    2. Nassim Ghondaghsaz & Zarina Chokparova & Sven Engesser & Leon Urbas, 2022. "Managing the Tension between Trust and Confidentiality in Mobile Supply Chains," Sustainability, MDPI, vol. 14(4), pages 1-25, February.
    3. Hennink, Monique & Kaiser, Bonnie N., 2022. "Sample sizes for saturation in qualitative research: A systematic review of empirical tests," Social Science & Medicine, Elsevier, vol. 292(C).
    4. Mary Ryder & Elisabeth Jacob & Joyce Hendricks, 2019. "An inductive qualitative approach to explore Nurse Practitioners views on leadership and research: An international perspective," Journal of Clinical Nursing, John Wiley & Sons, vol. 28(13-14), pages 2644-2658, July.
    5. Fatoumata Fofana & Pat Bazeley & Antoine Regnault, 2020. "Applying a mixed methods design to test saturation for qualitative data in health outcomes research," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-12, June.
    6. Jessica Eise & Natalie J. Lambert & Eric C. Wiemer, 2021. "Leveraging communities’ network strengths to support climate change adaptation information-sharing: a study with coffee farmers in Risaralda, Colombia," Climatic Change, Springer, vol. 168(1), pages 1-19, September.

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