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.
Highlights
Qualitative research is becoming an increasingly prominent way to conduct scientific research in business, management, and organization studies [1]
I assess the minimum sample sizes required to reach theoretical saturation for three different sampling scenarios: “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
The results demonstrate that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population
Summary
Qualitative research is becoming an increasingly prominent way to conduct scientific research in business, management, and organization studies [1]. Minimal information is a purposive scenario that works in the same way as random chance, but adds as extra condition that at least one new code must be observed at each sampling step. This is equivalent to a situation in which the researcher actively seeks information sources that reveal new codes, for example by making enquiries about the source beforehand. Making the unrealistic assumption that all codes have the same probability of being uncovered allows me to calculate the number of sampling steps mathematically (see S1 Appendix Section C: Reaching theoretical saturation) This calculation is not a result of the paper, it only helps me to validate results from the simulations.
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