PT  - JOURNAL ARTICLE
AU  - Jordan D. A. Hart
AU  - Michael N. Weiss
AU  - Lauren J. N. Brent
AU  - Daniel W. Franks
TI  - Common Permutation Methods in Animal Social Network Analysis Do Not Control for Non-independence
AID  - 10.1101/2021.06.04.447124
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.04.447124
4099  - http://biorxiv.org/content/early/2021/06/07/2021.06.04.447124.short
4100  - http://biorxiv.org/content/early/2021/06/07/2021.06.04.447124.full
AB  - The non-independence of social network data is a cause for concern among behavioural ecologists conducting social network analysis. This has led to the adoption of several permutation-based methods for testing common hypotheses.One of the most common types of analysis is nodal regression, where the relationships between node-level network metrics and nodal covariates are analysed using a permutation technique known as node-label permutation. We show that, contrary to accepted wisdom, node-label permutations do not account for the types of non-independence assumed to exist in network data, because regression-based permutation tests still assume exchangeability of residuals. The same theoretical condition also applies to the quadratic assignment procedure (QAP), a permutation-based method often used for conducting dyadic regression.We highlight that node-label permutations produce the same p-values as equivalent parametric regression models, but that in the presence of confounds, parametric regression models produce more accurate effect size estimates. We also note that QAP only controls for a specific type of non-independence between edges that are connected to the same nodes, and that appropriate parametric regression models are also able to account for this type of non-independence.Based on this, we advocate the retirement of permutation tests for regression analyses, in favour of well-specified parametric models. Moving away from permutation-based methods will reduce over-reliance on p-values, generate more reliable estimates of effect sizes, and facilitate the adoption of more powerful types of statistical analysis.Competing Interest StatementThe authors have declared no competing interest.