ArticleMethods in ecology and evolution2022
Permutation tests for hypothesis testing with animal social network data: Problems and potential solutions.
Article in Methods in ecology and evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social network dynamics under experimental manipulations of predation risk and food abundance in wild rock hyraxes.The Journal of animal ecology · 2026Pooled it
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- Bayesian multiplex network models in R using STRAND: methods for biologists and social scientists.Royal Society open science · 2025Article
- Social and spatial drivers of the multitiered structure of zebra finch social networks.Proceedings. Biological sciences · 2025Article
- A causal framework for the drivers of animal social network structure.PLoS computational biology · 2025Article
- How can we use social network analysis to better understand Chimpanzee and Gorilla sociality and communication?Animal cognition · 2025Review
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- Animal social networks are robust to changing association definitions.Behavioral ecology and sociobiology · 2025Article
- The information gain of explicitly provided over self-generated contextual knowledge for behavioral control.PloS one · 2025Article
- Population age structure shapes selection on social behaviour in a long-lived insect.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Expanding theory, methodology and empirical systems at the spatial-social interface.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
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- A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.Movement ecology · 2024Article
- Hierarchically embedded scales of movement shape the social networks of vampire bats.Proceedings. Biological sciences · 2024Article
- Spatiotemporal-social association predicts immunological similarity in rewilded mice.Science advances · 2023Article
- The Benefits of Friendships in Academic Settings: A Systematic Review and Meta-Analysis.Cureus · 2023Review
- BISoN: A Bayesian Framework for Inference of Social Networks.Methods in ecology and evolution · 2023Article
- Group-level differences in social network structure remain repeatable after accounting for environmental drivers.Royal Society open science · 2023Article
- Generation and applications of simulated datasets to integrate social network and demographic analyses.Ecology and evolution · 2023Article
- Indirect genetic effects for social network structure inPhilosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
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2 authors.
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Abstract
Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control 'nuisance effects' like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.Regressions on simulated datasets suggest that this 'double permutation' approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.
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