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(I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research.
PLoS One. 2017; 12(7):e0181689.Plos

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.

Authors+Show Affiliations

Innovation Studies, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. INGENIO (CSIC-UPV), Universitat Politècnica de València, Valencia, Spain.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28746358

Citation

van Rijnsoever, Frank J.. "(I Can't Get No) Saturation: a Simulation and Guidelines for Sample Sizes in Qualitative Research." PloS One, vol. 12, no. 7, 2017, pp. e0181689.
van Rijnsoever FJ. (I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research. PLoS ONE. 2017;12(7):e0181689.
van Rijnsoever, F. J. (2017). (I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research. PloS One, 12(7), e0181689. https://doi.org/10.1371/journal.pone.0181689
van Rijnsoever FJ. (I Can't Get No) Saturation: a Simulation and Guidelines for Sample Sizes in Qualitative Research. PLoS ONE. 2017;12(7):e0181689. PubMed PMID: 28746358.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - (I Can't Get No) Saturation: A simulation and guidelines for sample sizes in qualitative research. A1 - van Rijnsoever,Frank J, Y1 - 2017/07/26/ PY - 2017/02/20/received PY - 2017/07/04/accepted PY - 2017/7/27/entrez PY - 2017/7/27/pubmed PY - 2017/10/24/medline SP - e0181689 EP - e0181689 JF - PloS one JO - PLoS ONE VL - 12 IS - 7 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/28746358/_I_Can't_Get_No__Saturation:_A_simulation_and_guidelines_for_sample_sizes_in_qualitative_research_ L2 - https://dx.plos.org/10.1371/journal.pone.0181689 DB - PRIME DP - Unbound Medicine ER -