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Creators/Authors contains: "Wutich, Amber"

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  1. Writing winning proposals for funding research is an essential skill for doctoral students in the social sciences. Still, most anthropology programs lack formal instruction on this, relying instead on informal mentorship. To advance this, we evaluated the Value Proposition framework in teaching anthropology Ph.D. students to write proposals. Our findings from the feedback from students and faculty in the NSF-funded Cultural Anthropology Methods Program (CAMP) offer insights for using this framework to bridge the proposal-writing gap in the training of cultural anthropologists. 
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    Free, publicly-accessible full text available March 24, 2026
  2. Free, publicly-accessible full text available March 29, 2026
  3. Free, publicly-accessible full text available December 1, 2025
  4. There has been a recent explosion of articles on minimum sample sizes needed for analyzing qualitative data. The purpose of this integrated review is to examine this literature for 10 types of qualitative data analysis (5 types of saturation and 5 common methods). Building on established reviews and expanding to new methods, our findings extract the following sample size guidelines: theme saturation (9 interviews; 4 focus groups), meaning saturation (24 interviews; 8 focus groups), theoretical saturation (20–30+ interviews), metatheme saturation (20–40 interviews per site), and saturation in salience (10 exhaustive free lists); two methods where power analysis determines sample size: classical content analysis (statistical power analysis) and qualitative content analysis (information power); and three methods with little or no sample size guidance: reflexive thematic analysis, schema analysis, and ethnography (current guidance indicates 50–81 data documents or 20–30 interviews may be adequate). Our review highlights areas in which the extant literature does not provide sufficient sample size guidance—not because it is epistemologically flawed, but because it is not yet comprehensive and nuanced enough. To address this, we conclude by proposing ways researchers can navigate and contribute to the complex literature on sample size estimates. 
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