Table 3 Binomial logistic regression with the gender of drawn engineers as the dependent variable
Model 1 | Model 2 | Model 3 | Model 4 | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
W (df) | B (SE) | OR (95% CI) | W (df) | B (SE) | OR (95% CI) | W (df) | B (SE) | AOR (95% CI) | W (df) | B (SE) | AOR (95% CI) | |||
Constant | 256.641 (1)** | 2.538 (0.158) | 12.651 | 62.862 (1)** | 1.050 (0.132) | 2.857 | 146.348 (1)** | 2.423 (0.199) | 11.171 | 64.070 (1)** | 1.917 (0.239) | 6.800 | ||
Gender | 148.869 (1)** | − 2.195 (0.180) | 0.111 (0.078, 0.158) | 151.833 (1)** | − 2.234 (0.181) | 0.107 (0.075, 0.153) | 26.939 (1)** | − 1.529 (0.295) | 0.217 (0.122, 0.386) | |||||
Country | 2.689 (2) | 7.196 (2)* | 9.193 (2)* | |||||||||||
Country (Ireland) | 1.99 (1) | 0.250 (0.178) | 1.285 (0.907, 1.819) | 4.729 (1)* | 0.422 (0.194) | 1.526 (1.043, 2.233) | 7.388 (1)** | 1.133 (0.417) | 3.105 (1.372, 7.027) | |||||
Country (Kenya) | 0.015 (1) | 0.021 (0.172) | 1.021 (0.728, 1.432) | 0.001 (1) | − 0.005 (0.191) | 0.995 (0.685, 1.445) | 5.147 (1)* | 0.829 (0.365) | 2.290 (1.119, 4.685) | |||||
Country × Gender | 8.123 (2)* | |||||||||||||
Country (Ireland) × Gender | 4.124 (1)* | − 0.958 (0.472) | 0.384 (0.152, 0.967) | |||||||||||
Country (Kenya) × Gender | 7.178 (1)** | − 1.146 (0.428) | 0.318 (0.137, 0.735) | |||||||||||
χ2 | χ2(1) = 197.893, p < 0.001 | χ2(2) = 2.727, p = 0.256 | χ2(3) = 205.238, p < 0.001 | χ2(5) = 213.216, p < 0.001 | ||||||||||
-2 Log likelihood | 1075.840 | 1271.006 | 1068.495 | 1060.518 | ||||||||||
Cox and Snell R2 | 0.158 | 0.002 | 0.163 | 0.169 | ||||||||||
Nagelkerke R2 | 0.236 | 0.004 | 0.244 | 0.252 | ||||||||||
Hosmer and Lemeshow | χ2(0) = 0 | χ2(1) = 0, p = 1 | χ2(4) = 8.386, p = .078 | χ2(4) = 0, p = 1 | ||||||||||