通过调查来衡量人际网络

T. Kogovsek, V. Hlebec
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引用次数: 1

摘要

与社会科学的其他研究领域一样,在社会网络研究中,最常用的测量方法是调查。与其他测量对象(如意见、态度或价值观网络)相比,测量更为复杂,因此往往更具挑战性。测量通常分为两个主要阶段。首先,测量(生成)网络单元。其次,确定单位和其他单位特征(例如人口统计属性)之间的关系,同时出现一些具体问题,如是否要测量整体或以自我为中心的(个人)网络。在本文中,我们将自己局限于测量个人网络,特别是当与不同的生成网络的方法进行比较时。有五种生成个人网络的基本方法:名称生成器、角色生成器、事件生成器、位置生成器和上下文生成器。每个都与特定的研究目标、成本(财务、时间、受访者负担)、优势和局限性有关。此外,生成网络的复杂性和特殊性意味着必须考虑数据收集模式的特征(例如面对面、电话、网络)。在这个意义上,我们将展示各种生成个人网络的方法的优点和局限性,批判性地和比较地评估它们,并用经常使用的例子来说明它们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring personal networks with surveys
Like in other fields of inquiry in the social sciences, in social network research the most frequently used measurement method is the survey. Compared with other measurement objects such as networks of opinions, attitudes or values, measurement is more complex and thus often more challenging. Measurement typically occurs in two main phases. First, network units are measured (generated). Second, the relationships among the units and other unit characteristics (e.g. demographic properties) are determined, while some specific questions arise as to whether whole or egocentric (personal) networks are to be measured. In this paper, we limit ourselves to measuring personal networks, especially when compared with different methods for generating networks. There are five basic approaches to generating a personal network: name generator, role generator, event generator, positional generator, and contextual generator. Each is associated with particular research goals, costs (financial, time, respondent burden), advantages, and limitations. Moreover, the complexity and specifics of generating networks mean one must consider the characteristics of data collection modes (e.g. face-to-face, telephone, web). In this sense, we will present the advantages and limits of various methods of generating personal networks, evaluate them critically and comparatively, and illustrate them with often used examples.
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