1. Because of the nature of social interaction or association data, when testing hypotheses using social network data it is common for network studies to rely on permutations to control for confounding variables, and to not also control for them in the fitted statistical model. This can be a problem because it does not adjust for any bias in effect sizes generated by these confounding effects, and thus the effect sizes are not informative in the presence of confounding variables.
2. We implemented two network simulation examples and analysed an empirical dataset to demonstrate how relying solely on permutations to control for confounding variables can result in highly biased effect size estimates of animal social preferences that are uninformative when quantifying differences in behaviour.
3. Using these simulations, we show that this can sometimes even lead to effect sizes that have the wrong sign and are thus the effect size is not biologically interpretable. We demonstrate how this problem can be addressed by controlling for confounding variables in the statistical dyadic or nodal model.
4. We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data.

中文翻译：

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在动物社交网络分析中计算效应量

1. 由于社交互动或关联数据的性质，当使用社交网络数据测试假设时，网络研究通常依靠排列来控制混杂变量，而不是在拟合的统计模型中也对它们进行控制。这可能是一个问题，因为它无法调整由这些混杂效果产生的效果大小的任何偏差，因此，在存在混杂变量的情况下，效果大小无法提供足够的信息。
2. 我们实施了两个网络模拟示例，并分析了一个经验数据集，以演示仅依靠排列来控制混杂变量的方法如何导致高度偏颇的动物社会偏好的效应量估计，而这在量化行为差异时是没有信息的。
3. 使用这些模拟，我们表明有时甚至会导致效果大小带有错误的符号，因此效果大小在生物学上无法解释。我们演示了如何通过控制统计二进或节点模型中的混杂变量来解决此问题。
4. 我们建议在对动物社交网络数据进行统计分析时，应将此方法作为标准做法。