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

几乎任何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。本博士论文研究将：(1)评估一个地方人口在多大程度上被划分为行为大致相似的不同群体；(2)评估现有的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具有或不具有广泛的相似性；(3)提出并检验了基于社会网络原理的共享集群隶属度的解释。数据将通过对一个小镇的社会网络和日常行为进行人口调查来收集，并使用网络定位方法来评估行为相似性。这里提供的潜在发现和方法挑战了当前关于社会分层和身份的假设，也可能提出一种分析市场细分的新方法。该研究预计将揭示当地人口中比主流分层模型更强的差异，这些模型不关注当地背景，并显示特定社会关系对日常行为差异的重要性，同时控制各种通常被认为相关的个体属性。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.