TABLE 5.
Summary of correlations of visual functions with skiing performances.
| Variable | Raw-WPNS points | DH Raw-WPAS points | GS Raw-WPAS points | SG Raw-WPAS points | SL Raw-WPAS points |
| Static VA (logMAR) | τb = 0.26, p = 0.792 (26) | τb = 0.54, p = 0.598 (9) | τb = 0.50, p = 0.140 (15) | τb = 0.57, p = 0.098 (13) | τb = 0.35, p = 0.77 (15) |
| CS (logCS) | τb = −0.23, p = 1.000 (26) | τb = −0.50, p = 0.72 (9) | τb = −0.46, p = 0.221 (15) | τb = −0.51, p = 0.221 (13) | τb = −0.37, p = 0.708 (15) |
| GLS (change in logMAR) | τb = 0.18, p = 1.000 (19) | τb = 0.31, p = 1.000 (9) | τb = 0.21, p = 1.000 (13) | τb = −0.02, p = 1.000 (11) | τb = 0.08, p = 1.000 (13) |
| GLR (change in logMAR) | τb = 0.21, p = 1.000 (19) | τb = 0.48, p = 1.000 (9) | τb = −0.01, p = 1.000 (13) | τb = −0.13, p = 1.000 (11) | τb = 0.12, p = 1.000 (13) |
| LS (change in logMAR) | τb = −0.06, p = 1.000 (19) | τb = −0.20, p = 1.000 (7) | τb = −0.21, p = 1.000 (10) | τb = −0.33, p = 1.000 (8) | τb = −0.16, p = 1.000 (10) |
| Dynamic VA (logMAR) | τb = −0.22, p = 1.000 (16) | τb = 0.59, p = 0.616 (8) | τb = 0.25, p = 1.000 (11) | τb = 0.46, p = 0.90 (9) | τb = −0.06, p = 1.000 (11) |
| TMP (%) | τb = −0.10, p = 1.000 (15) | τb = 0.44, p = 1.000 (9) | τb = 0.39, p = 0.96 (12) | τb = 0.49, p = 0.492 (11) | τb = 0.23, p = 1.000 (12) |
| RMP (%) | τb = −0.24, p = 1.000 (15) | τb = 0.03, p = 1.000 (9) | τb = 0.08, p = 1.000 (12) | τb = −0.04, p = 1.000 (11) | τb = −0.02, p = 1.000 (12) |
| VF (%) | τb = −0.37, p = 0.169 (26) | τb = 0.09, p = 1.000 (9) | τb = −0.33, p = 1.000 (15) | τb = −0.34, p = 1.000 (13) | τb = −0.49, p = 0.182 (15) |
The adjusted p-values based on the Bonferroni–Holm corrections are presented in the table with sample sizes.