TABLE 8.
Binary probit regression predicting support for renewable energy.
| Mean estimate | 2.5th percentile | 97.5th percentile | |
| ThreatScale | 0.173 | 0.004 | 0.347 |
| KnownScale | 0.085 | –0.059 | 0.232 |
| MoralScale | 0.163 | –0.018 | 0.352 |
| EfficacyScale | –0.050 | –0.277 | 0.168 |
| Conservative | –0.010 | –0.139 | 0.116 |
| Female | 0.076 | –0.289 | 0.440 |
| Age | 0.057 | –0.039 | 0.153 |
| Age = Unknown | 0.161 | –0.175 | 0.508 |
| Constant | –0.242 | –0.537 | 0.036 |
Dependent variable: “Which of the following types of research do you think governments should fund now with tax dollars? Research to… make renewable energy cheaper and better (1 = Yes, 0 = No, N = 400). Mean Log Likelihood = −223, Nagelkerke pseudo-R2 = 0.23. Bootstrapped confidence intervals