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

Abstract

The purpose of this study is to assess the accuracy of two multivariate statistical approaches for estimating sex from human external ear anthropometry, namely, discriminant function analysis (DFA) and binary logistic regression (BLR). A cross-sectional sample of 497 participants (233 males and 264 females) aged 18–35 years (24.42 ± 5.17) was obtained from Himachal Pradesh state of North India. Both the ears of the participants (994) were examined for anthropometric measurements. A total of 12 anthropometric measurements were taken independently on the left and right ear of each individual with the help of a pair of sliding calipers using a standard method. The sex of the population groups was discriminated against using binary logistic regression and discriminant function analysis. The predictive percentage of sex estimation computed from both the models were substantially the same, that is, 76.3% from DFA and 76.2% from BLR, with nearly comparable (∼0.02) sensitivity, specificity, positive predictive value, and negative predictive values, whereas the values of correct predicted percentage were 0.1% higher in DFA than BLR. Moreover, the other comparison metrics, such as classification error, B-index, and Matthews correlation coefficient indicated that both models performed equally well. The study highlighted that if the assumptions of the statistical methods are met, both methods are equally capable of discriminating the population depending on sex. The study recommends that the discriminant function analysis and binary logistic regression may be used synonymously in forensic research and case-work pertaining to the estimation of sex and various other forensic situations.