Background: The social networks of people play a prominent role in theories on fertility. Investigating how networks shape behaviour is hard, because of the difficulty in measuring (large) networks among representative samples. Therefore, comprehensive studies of the variation in the structure and composition of networks and their impact on fertility outcomes are lacking. Objective: I aim to, first, describe the Dutch Social Networks and Fertility Study, and, second, describe the R-package FertNet that processes data from this study and transforms it into an easy-to-use format for researchers. Methods: The data used are from the Longitudinal Internet Social Survey (LISS) panel, a representative panel of Dutch households. The focus is on the Social Networks and Fertility Study that includes a subsample of women between the ages of 18‒40. Specific survey software was designed to capture each respondent’s personal network comprising 25 individuals with whom they had a relationship. In total, 758 women reported on over 18,750 relationships. For each person with whom the respondent had a relationship, several questions were asked about fertility-related topics. Uniquely, the connections between these people were also assessed. The R-package FertNet corrects data issues and transforms unstructured network data into alter-attribute and alter-tie datasets that can be handled by a diversity of network analytical approaches. Contribution: The Social Networks and Fertility Study is a unique resource that allows for a comprehensive investigation of how networks shape fertility behaviour. It provides better estimates of network characteristics than earlier literature based on smaller networks. The R-package FertNet assists researchers in their analyses.

中文翻译：

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描述荷兰社交网络和生育力研究以及如何处理它（作者：Gert Stulp）

背景：人们的社交网络在生育理论中发挥着重要作用。研究网络如何塑造行为是很困难的，因为在代表性样本中测量（大型）网络很困难。因此，缺乏对网络结构和组成的变化及其对生育结果影响的全面研究。目标：首先，我的目标是描述荷兰社交网络和生育力研究，其次，描述 R 包 FertNet，它处理该研究的数据并将其转换为研究人员易于使用的格式。方法：使用的数据来自纵向互联网社会调查（LISS）小组，这是荷兰家庭的代表性小组。重点是社交网络和生育力研究，其中包括 18-40 岁女性的子样本。特定的调查软件旨在捕获每个受访者的个人网络，其中包括与他们有关系的 25 个人。总共有 758 名女性报告了超过 18,750 段关系。对于与受访者有关系的每个人，都会询问几个有关生育相关主题的问题。独特的是，这些人之间的联系也得到了评估。R 包 FertNet 可以纠正数据问题，并将非结构化网络数据转换为可通过多种网络分析方法处理的更改属性和更改关系数据集。贡献：社交网络和生育力研究是一个独特的资源，可以对网络如何塑造生育行为进行全面调查。与基于较小网络的早期文献相比，它提供了更好的网络特征估计。R 包 FertNet 协助研究人员进行分析。