Analyzing Social Networks and Behavior

描述(由申请人提供)：长期以来，人们一直认为个人的特征对其社交网络的形成起到了作用。例如，友谊网络的特点往往是具有相似属性的人之间的联系。最近，人们研究了相反的现象，在这种现象中，个人的社会网络可能会影响他们的个人特征。例如：青少年可能会改变他们的饮酒或吸烟行为，以便与他们的朋友更接近。更广泛地说，一个人的网络很可能在确定他们的传染病状况方面发挥作用。这样的结果表明，不是将个体级别的属性视为影响网络的固定量，而是这些属性可能随着社交网络而变化，或者作为社交网络的结果而变化。然而，对于网络和节点属性数据的联合分析，统计方法的发展很少。在典型的数据分析中，网络或节点属性数据被选为“结果变量”。但将属性数据视为结果变量的分析通常无法正确解释由于社会网络效应而产生的统计相关性，从而导致夸大统计意义。当属性数据不完整或缺失时，以网络为结果的分析就会遇到问题：通常的做法是删除数据不完整的案例。这种特别的数据缩减丢弃了有价值的信息，并可能导致参数估计和统计推断的偏差。这一研究项目的目标是通过开发用于网络和节点属性数据的联合分析的统计方法和软件来纠正这些问题。这些方法将基于经过充分研究和熟悉的数据分析方法的扩展，如因子分析、线性回归和概率模型。该项目将为社会网络和个人层面的数据提供7种统计方法；7青少年健康数据集的分析；7纵向网络数据的分析方法；7研究人员的开源数据分析工具。

DESCRIPTION (provided by applicant): It has long been understood that an individual's characteristics play a role in the formation of their social network. For example, friendship networks are often characterized by ties among people with similar attributes. More recently the converse phenomenon has been studied, in which an individual's social network may affect their personal characteristics. For example: teenagers may change their alcohol or smoking behavior to more closely match those of their friends. More generally, an individual's network is likely to play a role in determining their status for a communicable disease. Such results indicate that, rather than think of individual-level attributes as fixed quantities which impact a network, the attributes might vary along with or as a result of the social network. However, there has been very little development of statistical methodology for the joint analysis of network and nodal attribute data. In typical data analyses, either the network or the nodal attribute data is chosen as the "outcome variable." But analyses that treat the attribute data as the outcome variable generally fail to properly account for statistical dependencies due to social network effects, leading to inflated claims of statistical significance. Analyses in which the network is the outcome run into problems when there is incomplete or missing attribute data: common practice is to delete cases for which the data are incomplete. Such ad hoc data reductions discard valuable information and can result in biased parameter estimates and statistical inferences. The goal of this research project is to remedy these problems by developing statistical methods and software for the joint analysis of networks and nodal attribute data. The methods will be based on extensions of well-studied and familiar data analysis methods such as factor analysis, linear regression and probit models. This project will provide 7statistical methods for the joint analysis of social network and individual-level data; 7analysis of Adolescent health datasets; 7methods for the analysis of longitudinal network data; 7open source data analysis tools for researchers.