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Is less more? A commentary on the practice of ‘metric hacking’ in animal social network analysis
Animal Behaviour ( IF 2.5 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.anbehav.2020.08.011
Quinn M.R. Webber , David C. Schneider , Eric Vander Wal

The use of social network analysis to quantify animal social relationships has increased exponentially over the last two decades. A popular aspect of social network analysis is the use of individually based network metrics. Despite the diversity of social network metrics that exist and the large number of studies that generate network metrics, little guidance exists on the number and type of metrics that should be analysed in a single study. Here, we comment on the ‘hypothesize after results are known’ (HARKing) phenomenon in the context of social network analysis, a practice that we term ‘metric hacking’ and define as the use of statistical criteria to select which metrics to use rather than a priori choice based on a research hypothesis. We identify three situations where metric hacking can occur in studies quantifying social network metrics: (1) covariance among network metrics as explanatory variables in the same model; (2) covariance among network metrics as response variables in multiple models; and (3) covariance between response and explanatory variables in the same model. We outline several quantitative and qualitative issues associated with metric hacking, provide alternative options and guidance on the appropriate use of multiple network metrics to avoid metric hacking. By increasing awareness of the use of multiple social network metrics, we hope to encourage better practice for the selection and use of social network metrics in animal social network analysis.

中文翻译:

是少还是多?动物社交网络分析中“度量黑客”的实践评述

在过去的二十年中,使用社交网络分析来量化动物的社会关系呈指数级增长。社交网络分析的一个流行方面是使用基于个人的网络指标。尽管存在社交网络指标的多样性以及生成网络指标的大量研究,但关于应在单个研究中分析的指标的数量和类型的指导很少。在这里,我们评论了社交网络分析背景下的“已知结果后的假设”(HARKing)现象,我们将这种做法称为“度量黑客”并定义为使用统计标准来选择要使用的度量,而不是基于研究假设的先验选择。我们确定了量化社交网络指标的研究中可能发生指标黑客攻击的三种情况:(1) 网络度量之间的协方差作为同一模型中的解释变量;(2) 多个模型中作为响应变量的网络度量之间的协方差;(3) 同一模型中响应变量和解释变量之间的协方差。我们概述了与度量黑客相关的几个定量和定性问题,提供了有关适当使用多个网络度量以避免度量黑客的替代选项和指导。通过提高对使用多种社交网络指标的认识,我们希望鼓励在动物社交网络分析中选择和使用社交网络指标的更好实践。(3) 同一模型中响应变量和解释变量之间的协方差。我们概述了与度量黑客相关的几个定量和定性问题,提供了有关适当使用多个网络度量以避免度量黑客的替代选项和指导。通过提高对使用多种社交网络指标的认识,我们希望鼓励在动物社交网络分析中选择和使用社交网络指标的更好实践。(3) 同一模型中响应变量和解释变量之间的协方差。我们概述了与度量黑客相关的几个定量和定性问题,提供了有关适当使用多个网络度量以避免度量黑客的替代选项和指导。通过提高对使用多种社交网络指标的认识,我们希望鼓励在动物社交网络分析中选择和使用社交网络指标的更好实践。
更新日期:2020-10-01
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