Statistical Designs for Stature Estimation by Cephalic and Nasal Metrics Among Jordanians

This study was carried out to evaluate a group of cephalic and nasal metrics as stature predictors among a sample of living adult Jordanians; 352 males and 391 females. The subjects were chosen from three northern Jordanian governorates. The three cephalic; length, width and circumference, and two nasal morphometrics; nasal breadth and height were measured and analyzed statistically by SPSS 22. The findings revealed that Pearson’s correlations of stature with all measures were significant, but weakly associated. Correlations of stature were (0.277, p< 0.01) with cephalic length, (0.238, p < 0.01) with width, (0.165, p < 0.01) with circumference, (0.534, p < 0.01) with nasal height, and (0.440, p< 0.01) with nasal breadth. Multiple linear regression (MLR) model was conducted for the combined data of both sexes. The findings showed that R square was 34.9%. In addition, all dimensions, except head circumference, contributed to the model significantly. The second model for stature estimation was designed by principal component analysis (PCA), which reduced the five cephalic and nasal dimensions into two components. Results of MLR on both components revealed a lower R square of 32.8%. We concluded that the designed models of stature prediction by cephalic and nasal metrics were weak, and to improve their strength, they should be combined with another stature proxy.