Table 3.
Object acquisition scenario | Competition scenario | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Step | Predictor | β | β SE | 95% CI | ∆R2 | df | F change | β | β SE | 95% CI | ∆R2 | df | F change | ||
1 | Vignette: Instrumental Goals | 0.37 | 0.20 | -0.02-0.77 | 0.03 | 1,174 | 4.54* | 0.40 | 0.25 | -0.07-0.91 | 0.02 | 1,175 | 3.75 | ||
2 | Vignette: Instrumental Goals | 0.27 | 0.19 | -0.10-0.65 | 0.02 | 1,173 | 4.38* | 0.23 | 0.25 | -0.26-0.75 | 0.05 | 1,174 | 10.27** | ||
VR: Instrumental Goals | 0.37 | 0.20 | -0.01-0.75 | 0.58** | 0.18 | 0.21-0.94 | |||||||||
1 | VR: Instrumental Goals | 0.44* | 0.20 | 0.04-0.82 | 0.04 | 1,174 | 6.66* | 0.63** | 0.19 | 0.27–1.01 | 0.07 | 1,175 | 13.02*** | ||
2 | VR: Instrumental Goals | 0.37 | 0.20 | -0.01-0.75 | 0.01 | 1,173 | 2.30 | 0.58** | 0.19 | 0.22-0.95 | 0.01 | 1,174 | 1.18 | ||
Vignette: Instrumental Goals | 0.27 | 0.19 | -0.10-0.64 | 0.23 | 0.25 | -0.21-0.72 | |||||||||
1 | Vignette: Aggressive Responses | 0.44* | 0.20 | 0.07-0.83 | 0.04 | 1,174 | 6.86* | 0.74*** | 0.20 | 0.36–1.15 | 0.10 | 1,175 | 19.98*** | ||
2 | Vignette: Aggressive Responses | 0.34 | 0.19 | -0.02-0.72 | 0.02 | 1,173 | 4.12* | 0.61** | 0.19 | 0.24–1.00 | 0.05 | 1,174 | 9.19** | ||
VR: Aggressive Responses | 0.35 | 0.19 | -0.00-0.73 | 0.50** | 0.17 | 0.15-0.84 | |||||||||
1 | VR: Aggressive Responses | 0.45* | 0.20 | 0.08-0.84 | 0.04 | 1,174 | 7.09** | 0.66*** | 0.18 | 0.31–1.00 | 0.08 | 1,175 | 16.07*** | ||
2 | VR: Aggressive Responses | 0.35 | 0.19 | -0.00-0.72 | 0.02 | 1,173 | 3.90 | 0.50** | 0.17 | 0.17-0.83 | 0.06 | 1,174 | 12.39*** | ||
Vignette: Aggressive Responses | 0.34 | 0.19 | -0.01-0.71 | 0.61** | 0.19 | 0.24-0.97 |
Hierarchical Regression Analyses were run for the Two Instrumental Gain Scenarios separately, both with Vignettes and VR Entered First. Model output is based on a non-bootstrapped procedure whereas output on separate predictors is based on a bootstrapping procedure
* p < .05; ** p < .01; *** p < .001