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. 2020 Dec 4;10:21296. doi: 10.1038/s41598-020-77940-z

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© The Author(s) 2020, corrected publication 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Male fundamental frequency (f_o) and formant position (P_f) as mediators of vocalizers’ condition and perceivers’ ratings. (a) Although height predicted physical dominance ratings, f_o and P_f did not mediate this relationship. (b) P_f, but not f_o, significantly mediated the relationship between composite size and physical dominance ratings. (c) Although f_o was not found to be a significant mediator between height and physical dominance ratings in the present study, a meta-analysis using a random-effects model indicated a significant mediating effect, with f_o mediating 44% of the relationship between height and physical dominance. Proportion mediated lower than 0 indicates the suppression effect of a mediating variable. In addition, the current study used mean dominance ratings as the primary unit of analyses for calculating proportion mediated, whereas Aung et al., Study 1 (n = 8,103 observations) and Study 2 (n = 6,586 observations) used individual ratings. (d) Using the “nFactors” package¹¹² and rotated factors with Varimax method using the “psych” package¹¹³, we reduced the set of size related measures into one dimensional factor (n = 1), which we labelled “composite size”, via principal axis factoring analysis. ***p < .001.