A Comparative Study of Different Stature Estimation Methods: Analysing the Purpose and Effectiveness of Biases in the Regression Formulae

The study assesses the effectiveness of various stature estimation methods that utilise biases such as sex and race. Based on the literature gathered, the plausibility that stature estimation methods that use regression equations in their computation may just be a result of mathematical coincidence. In order to evaluate the need for group-biased methods, the research devised its own set of regression equations for the sampled population and compared it against region-biased, sex-biased, and height-categorisation approaches. The sample population was taken entirely from Delhi, India and the English dataset used by Mays (2016). The sampling included all long bone measurements of the humerus, radius, ulna, femur, tibia, and fibula, along with the sex and ancestry of the participants. The findings revealed that the general regression model provided the lowest mean standard error estimate (SEE), initially suggesting that a non-biased approach to stature estimation may be more effective. However, upon analysis, it was found that the general model resulted in a fairly consistent overestimation of stature, although no particular trend of how this was occurring was noticeable. Along with this, the height-categorisation method, though mathematically very interesting, produced the highest mean SEE, indicating that the trends seen in stature estimation methods are not a result of mathematical coincidences. Looking at the group-specific models, a consistent performance was noticed in the statistical assessment and in the literature review. With a few caveats of certain bone measurements outperforming others, the group-specific models provide confirmation that the stature of any population has clear trends and can be quantified for estimation purposes. In the field of forensic anthropology, the complexity of accuracy, efficiency, and inclusivity is in constant discussion. Traditional race and sex biases being applied to modern contexts is challenging, especially with the rise of violence towards marginalised groups. Additionally, given the increase in cultural and genetic diversity of populations now, there needs to be immediate reconsideration of the terminology and sampling utilised in these long-standing methodologies. Future research should focus on developing more inclusive and adaptable stature estimation models.