Today, ethnographers and qualitative researchers in fields such as urban poverty, immigration, and social inequality face an environment in which their work will be read, cited, and assessed by demographers, quantitative sociologists, and even economists. They also face a demand for case studies of poor, minority, or immigrant groups and neighborhoods that not only generate theory but also somehow speak to empirical conditions in other cases (not observed). Many have responded by incorporating elements of quantitative methods into their designs, such as selecting respondents `at random' for small, in-depth interview projects or identifying `representative' neighborhoods for ethnographic case studies, aiming to increase generalizability. This article assesses these strategies and argues that they fall short of their objectives. Recognizing the importance of the predicament underlying the strategies — to determine how case studies can speak empirically to other cases — it presents two alternatives to current practices, and calls for greater clarity in the logic of design when producing ethnographic research in a multi-method intellectual environment.
Stored only when both papers are in the corpus — 2,022 citations exist upstream.