TABLE 5.
Multilevel linear regressions for preference and difficulty scorings with crossed-random effects for police/military hybrid sample.
| Models | β | SE | Odds ratio [exp(β)] | p |
| (1) Situational awareness (N = 400) | ||||
| Constant | 19.698 | 14.700 | 3.588*108 | 0.180 |
| Need for closure score (NFC) | 0.586 | 0.270 | 1.798 | 0.030* |
| Maximization score (MAX) | 0.184 | 0.180 | 1.202 | 0.306 |
| Military scenario (yes = 1) | 11.208 | 5.741 | 7.375 | 0.051 |
| Gender (male = 1) | –3.689 | 7.634 | 0.025 | 0.629 |
| Age | 0.086 | 0.436 | 1.089 | 0.844 |
| Police experience (years) | –0.128 | 0.456 | 0.880 | 0.779 |
| (2) Choice time (N = 400) | ||||
| Constant | 0.217 | 3.924 | 1.242 | 0.956 |
| Need for closure score (NFC) | –0.006 | 0.073 | 0.994 | 0.939 |
| Maximization score (MAX) | –0.007 | 0.049 | 0.993 | 0.885 |
| Military scenario (yes = 1) | –0.697 | 1.310 | 0.498 | 0.595 |
| Gender (male = 1) | –0.542 | 2.062 | 0.582 | 0.793 |
| Age | 0.198 | 0.118 | 1.219 | 0.092 |
| Police experience (years) | –0.066 | 0.123 | 0.936 | 0.590 |
| (3) Decision time (N = 400) | ||||
| Constant | –1.016 | 5.585 | 0.362 | 0.856 |
| Need for closure score (NFC) | –0.036 | 0.104 | 0.965 | 0.731 |
| Maximization score (MAX) | 0.049 | 0.069 | 1.050 | 0.478 |
| Military scenario (yes = 1) | –0.896 | 1.881 | 0.408 | 0.634 |
| Gender (male = 1) | 1.336 | 2.927 | 3.802 | 0.648 |
| Age | 0.284 | 0.167 | 1.329 | 0.089 |
| Police experience (years) | –0.138 | 0.175 | 0.871 | 0.428 |
| (4) Commitment time (N = 400) | ||||
| Constant | –1.223 | 3.193 | 0.291 | 0.699 |
| Need for closure score (NFC) | –0.030 | 0.058 | 0.970 | 0.605 |
| Maximization score (MAX) | 0.056 | 0.039 | 1.058 | 0.149 |
| Military scenario (yes = 1) | –0.199 | 1.260 | 0.819 | 0.874 |
| Gender (male = 1) | 1.877 | 1.647 | 6.535 | 0.255 |
| Age | 0.086 | 0.094 | 1.090 | 0.361 |
| Police experience (years) | –0.072 | 0.098 | 0.931 | 0.464 |
| (5) Decision difficulty score (N = 400) | ||||
| Constant | 8.904 | 4.145 | 7363.768 | 0.032 |
| Need for closure score (NFC) | –0.008 | 0.079 | 0.992 | 0.918 |
| Maximization score (MAX) | 0.024 | 0.053 | 1.024 | 0.649 |
| Military scenario (yes = 1) | 2.154 | 0.532 | 8.622 | 0.000*** |
| Gender (male = 1) | –2.021 | 2.231 | 0.133 | 0.365 |
| Age | 0.214 | 0.127 | 1.238 | 0.093 |
| Police experience (years) | –0.151 | 0.133 | 0.860 | 0.257 |
| (6) Avoidance score (N = 390)a | ||||
| Constant | 3.454 | 0.164 | 31.625 | 0.000 |
| Need for closure score (NFC) | 0.032 | 0.008 | 1.033 | 0.000*** |
| Maximization score (MAX) | –0.027 | 0.005 | 0.973 | 0.000*** |
| Military scenario (yes = 1) | 1.231*108 | 1.000 | 0.999 | |
| Gender (male = 1) | 0.956 | 0.181 | 2.601 | 0.000*** |
| Police experience (years) | 0.018 | 0.008 | 1.018 | 0.030* |
*p < 0.05; ***p < 0.001. A series of models were also run examining interaction effects between Maximization scores and scenario type on each of the outcome variables, but there was no evidence to suggest that there was a significant interaction. a“Age” was removed as a model variable due to multicollinearity issues with “Police Experience” in the avoidance score model.