Table 3.
Multiple linear regression model for FJS-12 and WOMAC-Total
| FJS-12 | WOMAC-Total | |||||||
|---|---|---|---|---|---|---|---|---|
|
Predictors |
Adjusted R2 |
Change adjusted R2 |
F |
p |
Adjusted R2 |
Change adjusted R2 |
F |
p |
| Gender |
0.018 |
0.018 |
4.75 |
0.030 |
0.019 |
0.019 |
4.84 |
0.029 |
| + Education |
0.036 |
0.018 |
2.88 |
0.024 |
0.043 |
0.024 |
2.18 |
0.014 |
| + Location |
0.063 |
0.027 |
3.67 |
0.003 |
0.093 |
0.050 |
3.24 |
<0.001 |
| + BSI-GSI |
0.237 |
0.174 |
11.34 |
<0.001 |
0.353 |
0.260 |
8.71 |
<0.001 |
| + Catastrophising |
0.363 |
0.126 |
17.29 |
<0.001 |
0.636 |
0.283 |
13.00 |
<0.001 |
| + BSI-Somatisation | 0.379 | 0.016 | 16.27 | <0.001 | 0.683 | 0.047 | 12.20 | <0.001 |
Equations for the final regression models (unstandardised):
WOMAC Total = −5.176 + 0.986*sex - 1.614*education_d1 - 3.503*education_d2 -3.939*education_d3 + 2.058*location + 0.311*BSI-GSI + 7.984*Catastrophising + 13.292*BSI-Somatisation.
FJS-12 = 84.521 - 2.258*sex + 0.540*education_d1 + 3.125*education_d2 + 13.073*education_d3 - 4.178*location - 7.105*BSI-GSI - 8.675*Catastrophising - 13.102*BSI-Somatisation.
Coding of predictors:
Sex: Male = 0, Female = 1.
Education (dummy-coded):
Apprenticeship: d1 = 1.
A-level/professional school: d2 = 1.
University: d3 = 1.
Else: d1. d2. d3 = 0.
Location: 1 = THA, 2 = TKA.