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Table Table 1 From the research bench

The qualitative saturation stopping card

Declare the saturation object, sample information deliberately, and record why another case is no longer changing the answer.

Synthetic stopping cardCode saturationMeaning saturationTheoretical saturation
Primary testNo relevant new code appearsNew data adds little conceptual depthNo relevant theoretical relationship remains unintegrated
Sampling implicationCover the intended topic spaceSeek variation that can deepen interpretationSample information sources that can challenge the account
Required recordCodebook and case-by-code trailAnalytic memos and comparison trailTheoretical memos and negative-case decisions
Stop questionWould another case add a relevant category?Would another case change the explanation?Would another case alter the theoretical integration?

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Reference & Evidence

Source: Author's synthetic decision framework grounded in Saunders et al. (2018), Hennink and Kaiser (2022), and van Rijnsoever (2017). The sequence is not a universal sample-size rule.

Each line is a claim from the register this journal publishes against, resolved from the register at build time.

  • A Saturation is widely used to indicate that more data are unnecessary, and the authors say the concept is used inconsistently: "It is commonly taken to indicate that, on the basis of the data that have been collected or analysed hitherto, further data collection and/or analysis are unnecessary", yet "there appears to be uncertainty as to how saturation should be conceptualized, and inconsistencies in its use" Saunders et al. (2018) · SAU18-C1
  • A The article distinguishes different conceptualisations rather than treating saturation as one universal event: "We identify four distinct approaches to saturation, which differ in terms of the extent to which an inductive or a deductive logic is adopted" Saunders et al. (2018) · SAU18-C2
  • A The recommendation is to make the form and rationale explicit: "We conclude that saturation should be operationalized in a way that is consistent with the research question(s), and the theoretical position and analytic framework adopted" Saunders et al. (2018) · SAU18-C3
  • A The design is a systematic review, in the authors' words: "We conducted a systematic review of four databases to identify studies empirically assessing sample sizes for saturation in qualitative research", and "We identified 23 articles that used empirical data (n = 17) or statistical modeling (n = 6) to assess" saturation Hennink & Kaiser. (2022) · HK22-C1
  • A The reviewed range, verbatim: "Studies using empirical data reached saturation within a narrow range of interviews (9-17) or focus" group discussions (4-8), particularly in relatively homogeneous populations Hennink & Kaiser. (2022) · HK22-C2
  • A The caution is the authors' own and travels with the range: "these findings apply to certain types of studies (e.g., those with homogenous study populations)" Hennink & Kaiser. (2022) · HK22-C3
  • A The simulation's object, in the author's words: "I conceptualize a population as consisting of sub-populations that contain different types of information sources that hold a number of codes" van Rijnsoever. (2017) · VR17-C1
  • A The three sampling scenarios differ in what they require: "the minimal and maximal information scenarios are significantly more efficient than random chance", though they yield fewer repetitions per code van Rijnsoever. (2017) · VR17-C2
  • A The result that decides the guidance: "I show that theoretical saturation is more dependent on the mean probability of observing codes than on the number of codes in a population" van Rijnsoever. (2017) · VR17-C3

Grades: A, verified against the printed page of the primary source · B, primary source, text layer only · C, authoritative secondary · D, reported.