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在其社会网络的形成中发挥作用。例如，友谊网络的特征通常是具有相似属性的人之间的联系。最近，人们研究了一种匡威的现象，即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，以更接近他们朋友的行为。更一般地说，一个人的网络可能在确定其传染病状态方面发挥作用。这些结果表明，个人层面的属性并不是影响网络的固定数量，而是沿着社会网络的变化而变化。 然而，很少有统计方法的发展，网络和节点属性数据的联合分析。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”。“但是，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交网络效应导致的统计依赖性，从而导致统计显著性的夸大。当属性数据不完整或缺失时，网络是结果的分析会遇到问题：通常的做法是删除数据不完整的案例。这种特别的数据简化丢弃了有价值的信息，并可能导致有偏差的参数估计和统计推断。本研究项目的目标是通过开发网络和节点属性数据联合分析的统计方法和软件来解决这些问题。这些方法将基于扩展的研究和熟悉的数据分析方法，如因素分析，线性回归和概率模型。该项目将提供7种社会网络和个人层面数据联合分析的统计方法; 7种青少年健康数据集分析方法; 7种纵向网络数据分析方法; 7种面向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.