A methodological comparison of discriminant function analysis and binary logistic regression for estimating sex in forensic research and case-work

Assigned sex estimation with the clavicle and scapula: A study in a Portuguese reference sample

The estimation of biological sex is a critical step in the assessment of the biological profile of an anonymous skeletonized individual. In certain recovery circumstances, the most dimorphic skeletal areas, such as the pelvis, are absent or fragmented; in that case, other bones of the skeleton, including the clavicle and scapula, can be used to predict sex. The purpose of this research is to generate new models for the estimation of sex with clavicular and scapular measurements using a study-sample of 129 individuals with clavicle (65 males and 64 females) and 112 individuals with scapula (50 males and 62 females) from the Lisbon Identified Skeletal Collection (Portugal). A decision tree classifier (C4.5) and logistic regression (LR) were employed to create univariable and multivariable sex prediction models. Accuracy under cross-validation of the classification models is high (up to 93.8%), with minimal bias (<5%), particularly in the multivariable models. The proposed LR models facilitate the probabilistic estimation of biological sex, accounting for the significant overlap in the expression of sexual dimorphism.