1. Social network methods have become a key tool for describing, modelling and testing hypotheses about the social structures of animals. However, due to the non‐independence of network data and the presence of confounds, specialised statistical techniques are often needed to test hypotheses in these networks. Datastream permutations, originally developed to test the null hypothesis of random social structure, have become a popular tool for testing a wide array of null hypotheses in animal social networks. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models.
2. Here, we show that these datastream permutations typically do not represent the null hypothesis of interest to researchers interfacing animal social network analysis with regression modelling, and use simulations to demonstrate the potential pitfalls of using this methodology.
3. Our simulations show that, if used to indicate whether a relationship exists between network structure and a covariate, datastream permutations can result in extremely high type I error rates, in some cases approaching 50%. In the same set of simulations, traditional node‐label permutations produced appropriate type I error rates (~5%).
4. Our analysis shows that datastream permutations do not represent the appropriate null hypothesis for these analyses. We suggest that potential alternatives to this procedure may be found in regarding the problems of non‐independence of network data and unreliability of observations separately. If biases introduced during data collection can be corrected, either prior to model fitting or within the model itself, node‐label permutations then serve as a useful test for interfacing animal social network analysis with regression modelling.

中文翻译：

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动物社交网络数据的常见数据流排列不适合使用回归模型进行假设检验

1. 社交网络方法已成为描述、建模和测试有关动物社会结构的假设的关键工具。然而，由于网络数据的非独立性和混淆的存在，通常需要专门的统计技术来检验这些网络中的假设。最初开发用于测试随机社会结构的零假设的数据流排列已成为测试动物社交网络中各种零假设的流行工具。特别是，它们已被用于通过将这些排列与回归模型相连接来测试外生因素是否与网络结构相关。
2. 在这里，我们展示了这些数据流排列通常不代表研究人员感兴趣的零假设，他们将动物社会网络分析与回归建模相结合，并使用模拟来证明使用这种方法的潜在缺陷。
3. 我们的模拟表明，如果用于指示网络结构和协变量之间是否存在关系，数据流排列会导致极高的 I 类错误率，在某些情况下接近 50%。在同一组模拟中，传统的节点标签排列产生了适当的 I 类错误率 (~5%)。
4. 我们的分析表明，数据流排列并不代表这些分析的适当零假设。我们建议，对于网络数据的非独立性和观察的不可靠性问题，可以找到该程序的潜在替代方案。如果可以在模型拟合之前或在模型本身内纠正数据收集过程中引入的偏差，则节点标签排列可以作为将动物社交网络分析与回归建模连接起来的有用测试。