Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany.
Behav Res Methods. 2022 Apr;54(2):611-631. doi: 10.3758/s13428-021-01606-5. Epub 2021 Aug 2.
Researchers studying social networks and inter-personal sentiments in bounded or small-scale communities face a trade-off between the use of roster-based and free-recall/name-generator-based survey tools. Roster-based methods scale poorly with sample size, and can more easily lead to respondent fatigue; however, they generally yield higher quality data that are less susceptible to recall bias and that require less post-processing. Name-generator-based methods, in contrast, scale well with sample size and are less likely to lead to respondent fatigue. However, they may be more sensitive to recall bias, and they entail a large amount of highly error-prone post-processing after data collection in order to link elicited names to unique identifiers. Here, we introduce an R package, DieTryin, that allows for roster-based dyadic data to be collected and entered as rapidly as name-generator-based data; DieTryin can be used to run network-structured economic games, as well as collect and process standard social network data and round-robin Likert-scale peer ratings. DieTryin automates photograph standardization, survey tool compilation, and data entry. We present a complete methodological workflow using DieTryin to teach end-users its full functionality.
研究人员在研究有界或小规模社区中的社交网络和人际情感时，需要在基于名册的和自由回忆/名称生成器的调查工具之间进行权衡。基于名册的方法在样本量方面的扩展性较差，并且更容易导致受访者疲劳；然而，它们通常会产生质量更高、不易受回忆偏差影响且需要较少后期处理的数据。相比之下，基于名称生成器的方法在样本量方面扩展性较好，并且不太可能导致受访者疲劳。但是，它们可能更容易受到回忆偏差的影响，并且在数据收集后需要进行大量高度易错的后期处理，以便将引出的名称与唯一标识符相关联。在这里，我们介绍了一个名为“DieTryin”的 R 包，它允许像基于名称生成器的方法一样快速地收集和输入基于名册的二元数据；DieTryin 可以用于运行网络结构的经济游戏，以及收集和处理标准社交网络数据和循环李克特量表同伴评分。DieTryin 实现了照片标准化、调查工具编译和数据输入的自动化。我们展示了一个完整的方法学工作流程，使用“DieTryin”来教授最终用户其全部功能。
