Calculating effect sizes in animal social network analysis
Calculating effect sizes in animal social network analysis
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DOI：
10.1111/2041-210x.13429
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发表时间：
2020-06-21
影响因子：
6.6
通讯作者：
Croft, Darren P.
中科院分区：
文献类型：
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作者：
Franks, Daniel W.;Weiss, Michael N.;Croft, Darren P.
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，网络研究通常依赖于排列来控制混杂变量，并且在拟合的统计模型中也不控制它们。这可能是一个问题，因为它没有调整这些混杂效应产生的效应量的任何偏倚，因此在存在混杂变量的情况下，效应量没有信息。我们实施了两个网络模拟示例，并分析了一个经验数据集，以证明仅依靠排列来控制混杂变量如何导致动物社会偏好的高度偏倚效应大小估计，这些估计在量化行为差异时无法提供信息。使用这些模拟，我们表明，这有时甚至会导致具有错误符号的效应量，因此效应量在生物学上不可解释。我们演示了如何通过控制统计二元或节点模型中的混杂变量来解决这个问题。我们建议将这种方法作为动物社会网络数据统计分析的标准实践。
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. 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. 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. We recommend this approach should be adopted as standard practice in the statistical analysis of animal social network data.