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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. 

Abstract We use a mix of qualitative and quantitative analyses to examine 1354 survey responses from members of the American Anthropological Association about their practice and teaching of cultural anthropology research methods. Latent profile analysis and an examination of responses to open‐ended survey questions reveal distinctive methodological clustering among anthropologists. However, two historical approaches to ethnography remain prominent:deep hanging outand amixed methods toolkit, with the former remaining central to the practice and teaching of all forms of contemporary cultural anthropology. Further, many anthropologists are committed to advancing research methods that account for power imbalances in fieldwork, such as through community‐based and participatory approaches. And a substantial number also teach a wider array of methods and techniques that open new career pathways for anthropologists. Overall, our study reveals a core set of ethnographic practices—loosely, participant‐observation, informal interviews, and the experiential immersion of the ethnographer—while also highlighting the great breadth of cultural anthropological research practice and pedagogy. The findings presented here can help inform how current and future anthropological practitioners and educators position themselves to meet the ever‐changing demands of community members, funders, clients, collaborators, and students. 

We outline a process for using large coder teams (10 + coders) to code large-scale qualitative data sets. The process reflects experience recruiting and managing large teams of novice and trainee coders for 18 projects in the last decade, each engaging a coding team of 12 (minimum) to 54 (maximum) coders. We identify four unique challenges to large coder teams that are not presently discussed in the methodological literature: (1) recruiting and training coders, (2) providing coder compensation and incentives, (3) maintaining data quality and ensuring coding reliability at scale, and (4) building team cohesion and morale. For each challenge, we provide associated guidance. We conclude with a discussion of advantages and disadvantages of large coder teams for qualitative research and provide notes of caution for anyone considering hiring and/or managing large coder teams for research (whether in academia, government and non-profit sectors, or industry).