Model summary rotationa (total variance explained by each dimension of adaptive capacity indicators)
| Dimension | Cronbach's alpha | Variance accounted for | |
|---|---|---|---|
| Total (Eigenvalue) | % of variance | ||
| 1 | 0.643 | 1.925 | 9.626 |
| 2 | 0.609 | 1.600 | 7.999 |
| 3 | 0.249 | 1.109 | 5.546 |
| 4 | 0.360 | 1.021 | 5.103 |
| 5 | 0.142 | 1.006 | 5.029 |
| 6 | 0.223 | 1.006 | 5.028 |
| 7 | 0.185 | 1.004 | 5.019 |
| 8 | 0.255 | 1.004 | 5.018 |
| 9 | 0.273 | 1.003 | 5.013 |
| 10 | 0.088 | 1.002 | 5.012 |
| 11 | 0.252 | 1.002 | 5.012 |
| 12 | 0.103 | 1.002 | 5.010 |
| 13 | 0.048 | 1.002 | 5.009 |
| 14 | 0.151 | 1.001 | 5.007 |
| 15 | 0.105 | 1.001 | 5.004 |
| 16 | 0.286 | 1.000 | 4.999 |
| 17 | 0.296 | 0.998 | 4.991 |
| 18 | 0.444 | 0.959 | 4.793 |
| 19 | 0.049 | 0.325 | 1.626 |
| 20 | −6.518 | 0.031 | 0.155 |
| Total | 1.000b | 20.000 | 100.000 |
| Dimension | Cronbach's alpha | Variance accounted for | |
|---|---|---|---|
| Total (Eigenvalue) | % of variance | ||
| 1 | 0.643 | 1.925 | 9.626 |
| 2 | 0.609 | 1.600 | 7.999 |
| 3 | 0.249 | 1.109 | 5.546 |
| 4 | 0.360 | 1.021 | 5.103 |
| 5 | 0.142 | 1.006 | 5.029 |
| 6 | 0.223 | 1.006 | 5.028 |
| 7 | 0.185 | 1.004 | 5.019 |
| 8 | 0.255 | 1.004 | 5.018 |
| 9 | 0.273 | 1.003 | 5.013 |
| 10 | 0.088 | 1.002 | 5.012 |
| 11 | 0.252 | 1.002 | 5.012 |
| 12 | 0.103 | 1.002 | 5.010 |
| 13 | 0.048 | 1.002 | 5.009 |
| 14 | 0.151 | 1.001 | 5.007 |
| 15 | 0.105 | 1.001 | 5.004 |
| 16 | 0.286 | 1.000 | 4.999 |
| 17 | 0.296 | 0.998 | 4.991 |
| 18 | 0.444 | 0.959 | 4.793 |
| 19 | 0.049 | 0.325 | 1.626 |
| 20 | −6.518 | 0.031 | 0.155 |
| Total | 1.000 | 20.000 | 100.000 |
Notes:
Rotation method: Varimax with Kaiser normalisation;
Total Cronbach's alpha is based on the total eigenvalue