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

几乎所有当代人口的日常生活行为都表现出分组分布，任何地区的大多数人口似乎都分为相对较少的非常不同的群体。本博士论文的研究将：（1）评估当地人口被划分为行为大致相似的不同群体的程度;（2）评估现有归因模型和概念的效用，以解释为什么人们在日常行为方面大致相似或不相似;以及（3）提出并测试一个解释共享集群成员来自社会网络的原则。数据将通过一个小城镇的社交网络和日常行为的人口调查来收集，并使用网络定位方法进行分析，以评估行为相似性。这里提供的潜在的研究结果和方法挑战目前的假设社会分层和身份，也可能提出一种新的方法来分析市场细分。这项研究预计将揭示更强的区别在当地人口比流行的分层模型，不注重当地的情况下，并显示日常行为分化的特定社会关系的重要性，同时控制各种个人属性往往被认为是相关的。这项研究还将创建迄今为止最大的人际社交网络数据集之一。

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.