Assessment of Sexual Dimorphism: A Critical Discussion in a (Paleo-) Anthropological Context

Ipiña, Santiago L.; Durand, Ana I.

doi:10.3378/027.082.0205

Cited by 2 publications

(5 citation statements)

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“…There are several functions available to assess the degree of overlap between the sexes and the effect size difference between the sexes. These are the dissimilarity index (Chakraborty & Majumder, 1982), the mixture index and overlap of two normal distributions (Ipiña & Durand, 2010), and Hedges' g (Hedges, 1981). All of these are solely for summarizing the univariate sexual dimorphism within a single sample, but because they can be used to calculate confidence intervals for the relevant parameters the relationship of these confidence intervals can be used to examine differences in sexual dimorphism.…”

Section: Resultsmentioning

confidence: 99%

“…Our function uses either the known counts of males and females or the estimated “mixing” proportion for females. In the former case the parametric bootstrap can be used to obtain a confidence interval for the MI . “ The overlapping area between two normal distributions ” (NI) is defined in Ipiña and Durand (2010:215) as well as in Ipiña and Durand (2000:937) and Ipiña (2002:150). Because the NI index is the same as Inman and Bradley's (Inman & Bradley, 1989) OVL, the sum of the D index and NI is 1.0.…”

Section: Resultsmentioning

confidence: 99%

“…Chakraborty and Majumder's (Chakraborty & Majumder, 1982) D index could be found as one minus the overlap coefficient in R using the function “p_overlap” in the library “effect size” (Ben‐Shachar et al, 2020), but this function requires raw data. Cavazzuti et al (2019) provide R code to calculate the D index from summary statistics, but they use a calculation for the standard deviation of the D index that Ipiña and Durand (2004:515) first stated “seems to be nonsensical” and later they (Ipiña & Durand, 2010:215) referred to as “obviously nonsensical.” TestDimorph calculates the D index and uses a parametric bootstrap and the bias‐corrected percentile method to form a confidence interval for the D index.…”

Section: Discussionmentioning

confidence: 99%

“…Borgognini‐Tarli and Repetto (1986:56) refer to a class of sexual dimorphism measures that consist of the “…difference between average values of the two sexes weighted by the standard deviation.” These measures take variability into account, rather than simply comparing means. There are now numerous methods for measuring sexual dimorphism for single variables and some of them allow for testing difference in sexual dimorphism between two samples (Bennett, 1981; Chakraborty & Majumder, 1982; Greene, 1989; Ipiña, 2002; Ipiña & Durand, 2010; Marini et al, 1999; Plavcan, 1994; Relethford & Hodges, 1985). Rarer are multivariate tests for differences in sexual dimorphism between multiple samples (Key & Jantz, 1981; Konigsberg, 1991; van Vark et al, 1989) as they require the adequate reporting of summary statistics or the results of multivariate analyses originally performed on raw data.…”

Section: Introductionmentioning

confidence: 99%

“…dimorphism for single variables and some of them allow for testing difference in sexual dimorphism between two samples (Bennett, 1981;Chakraborty & Majumder, 1982;Greene, 1989;Ipiña, 2002;Ipiña & Durand, 2010;Marini et al, 1999;Plavcan, 1994;Relethford & Hodges, 1985). Rarer are multivariate tests for differences in sexual dimorphism between multiple samples (Key & Jantz, 1981;Konigsberg, 1991;van Vark et al, 1989) as they require the adequate reporting of summary statistics or the results of multivariate analyses originally performed on raw data.…”

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confidence: 99%

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TestDimorph: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics

Abulnoor,

Attia,

Konigsberg

et al. 2023

American Journal of Biological Anthropology

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ObjectiveThe degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics.Materials and MethodsWe provide two approaches of analysis of inter‐sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within‐sex size variability between samples is compared using one‐way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non‐overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples.ResultsWe demonstrate various results from applying TestDimorph functions to data supplied with the package.DiscussionThe package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.

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Section: Resultsmentioning

confidence: 99%