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Commonly, two approaches for collecting network data by means of a questionnaire are distinguished: rosters (complete name lists) and free recall (name generators) with subsequent merging of the ego-networks. However, both methods are reaching their limits when dealing with larger networks: rosters, on the one hand, increase in length with a larger network size, so that respondents either respond more unreliably due to fatigue, or they even abort answering the questionnaire all together. With free recall, on the other hand, weak ties and unpopular persons tend to be forgotten by the respondents, a problem that is also amplified with an increasing network size. In this paper, I want to propose an alternative method for collecting network data via questionnaire: the subgroup-based recall. With this method, it is possible to reliably collect data on mid-sized networks (50 < n < 200). When employing the subgroup-based recall, the network actors are divided into subgroups and a separate name generator is used for each group; the subgroups serve as cues without letting the questionnaire become too long. The use of the subgroup-based recall, however, leads to new methodological challenges, mainly the appropriate division of actors into subgroups. The discussion of subgroup determination shows that the method is best suited for collecting network data in organizational settings as they already provide detailed formal subgroups like for example departments. The article ends with specific recommendations for when to employ rosters, free recall and the subgroup-based recall.

In this paper we develop and apply a comparative framework for the epistemic regimes of scientific fields, which we hope may contribute to strengthening field-comparative research in the sociology of science. We start from the comparative framework developed by Richard Whitley (1984) but modified it radically in order to develop an approach that includes more characteristics of research practices and social structure, and uses characteristics that can be empirically operationalised for a fine-grained comparative analysis of epistemic regimes. We use data from several empirical studies for a comparative description of the epistemic regimes of experimental atomic and molecular optics (AMO physics), plant biology, early modern history, and automotive engineering. This comparison serves as proof of concept. The usefulness of our framework is demonstrated by applying it to an explanation of the emergence of individual research programmes in the four fields. Further possible applications that are briefly discussed include field-specific effects of evaluation regimes, field-specific career patterns, and field-specific practices of data sharing.