Trotter and Gleser’s (1958) equations outperform Trotter and Gleser’s (1952) equations in stature estimation of the US White males

: Trotter and Gleser presented two sets of stature estimation equations for the US White males in their 1952 and 1958 studies. Following Trotter’s suggestion favouring the 1952 equations simply due to the smaller standard errors, the 1958 equations have been seldom used and have gone without additional systematic validation tests. This study aims to assess the performance of the Trotter and Gleser 1952, Trotter and Gleser 1958, and FORDISC equations for the White males in a quantitative and systematic way, particularly when applied to the WWII and Korean War casualties. In sum, 27 equations (7 from the 1952 study, 10 from the 1958 study, and 10 from FORDISC) were applied to the osteometric data of 240 accounted-for White male casualties of the WWII and Korean War. Then, the bias, accuracy, and Bayes factor for each set of stature estimates were calculated. The results show that, overall, Trotter and Gleser’s 1958 equations outperform the 1952 and FORDISC equations in terms of all three measures. Particularly, the equations with higher Bayes factors produced stature estimates where distributions were closer to that of the reported statures than those with lower Bayes factors. When considering Bayes factors, the best performing equation was the “Radius” equation from the 1958 study (BF = 15.34) followed by the “Humerus+Radius” equation from FORDISC (BF = 14.42) and the “Fibula” equation from the 1958 study (BF = 13.82). The results of this study will provide researchers and practitioners applying the Trotter and Gleser stature estimation method with a practical guide for equation selection. KEY POINTS: The performance of three stature estimation methods was compared quantitatively. Trotter and Gleser’s (1952, 1958) and FORDISC White male equations were included. Overall, Trotter and Gleser’s 1958 method outperformed the other methods. This study provides a practical guide for stature estimation equation selection.