Doctoral Dissertation Research: The Relational Bases of Everyday Life: Behavioral Similarity and Partitioning of a Local Population

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The everyday life behaviors of nearly any contemporary population exhibit a grouped distribution, and the majority of any locale's population seems to divide into a relatively small number of very distinct groups. This doctoral dissertation research will: (1) assess the extent to which a local population is partitioned into distinct groups within which behavior is broadly similar; (2) evaluate the utility of existing, attributional models and concepts for explaining why persons are or are not broadly similar with respect to their everyday behaviors; and (3) propose and test an explanation for shared cluster membership derived from social network principles. Data will be collected using a population survey of the social networks and everyday behaviors in a small town, and analyzed using network positional methods to assess behavioral similarity. The potential findings and method offered here challenge current assumptions about social stratification and identity and may also suggest a new approach to the analysis of market segmentation. This study is expected to reveal stronger distinctions within a local population than those suggested by prevailing stratification models, which do not focus on local contexts, and show the importance of specific social relations for everyday behavioral differentiation, while controlling for a variety of individual attributes often regarded as relevant. This research will also create one of the largest data sets on interpersonal social networks available to date.