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Quantitative researchers increasingly draw on ethnographic research that may not be generalizable to inform and interpret results from statistical analyses; at the same time, while generalizability is not always an ethnographic research goal, the integration of quantitative data by ethnographic researchers to buttress findings on processes and mechanisms has also become common. Despite the burgeoning use of dual designs in research, there has been little empirical assessment of whether the themes, narratives, and ideal types derived from qualitative fieldwork are broadly generalizable in a manner consistent with estimates obtained from quantitative analyses. We draw on simulated and real-world data to assess the bias associated with failing to align samples across qualitative and quantitative methodologies. Our findings demonstrate that significant bias exists in mixed-methods studies when sampling is incongruent within research designs. We propose three solutions to limit bias in mixed-methods research.

Amid ongoing controversies in ethnography concerning representation, reproducibility, and generalizability, social scientific scholarship has increasingly taken a mixed-methods turn. While studies that blend qualitative and quantitative data promise to enhance the validity of representations of social worlds under analysis, they cannot escape contending with foundational dilemmas of scientific translation, integration, and commensurability across methodological paradigms. Recent debates have ignited a new line of inquiry about the integration of multiple methods in ethnography. In this paper, we argue that “cameo appearances”—the summoning of either qualitative or quantitative analyses in separate, purely mono-method studies—amounts to a form of methodological tokenism under the guise of methodological pluralism. We articulate sampling design, enhanced training, and curriculum development as crucial for arbitrating these debates as mixed-methods research emerges as a distinct innovation in twenty-first-century ethnography.