N Parameters
\[y_{i} = \beta_{0} + \beta_{1} age\_z + \beta_{2}male_i + \beta_{3}civic\_duty_i + \\ \beta_{4}hawthorne_i + \beta_{5}self_i + \beta_{6}neighbors_i + \\ \beta_{7}Sometimes\ vote_i + \beta_{8}Always\ vote_i + \\ \beta_{9}civic\_duty_i Sometimes\ vote_i + \beta_{10}hawthorne_i Sometimes\ vote_i + \\ \beta_{11}self_i Sometimes\ vote_i + \beta_{11}neighbors_i Sometimes\ vote_i + \\ \beta_{12}civic\_duty_i Always\ vote_i + \beta_{13}hawthorne_i Always\ vote_i + \\ \beta_{14}self_i Always\ vote_i + \beta_{15}neighbors_i Always\ vote_i + \epsilon_{i}\]
Characteristic |
Beta |
95% CI 1 |
|---|---|---|
| (Intercept) | 0.155 | 0.137, 0.174 |
| age_z | 0.036 | 0.031, 0.042 |
| sex | ||
| sexMale | 0.004 | -0.005, 0.014 |
| treatment | ||
| No Postcard | — | — |
| treatmentCivicDuty | 0.016 | -0.027, 0.057 |
| Hawthorne | 0.004 | -0.037, 0.043 |
| Self | -0.007 | -0.049, 0.035 |
| Neighbors | 0.084 | 0.041, 0.126 |
| voter_class | ||
| Rarely Vote | — | — |
| voter_classSometimesVote | 0.115 | 0.095, 0.135 |
| voter_classAlwaysVote | 0.298 | 0.275, 0.321 |
| treatment * voter_class | ||
| treatmentCivicDuty * voter_classSometimesVote | -0.005 | -0.051, 0.043 |
| Hawthorne * voter_classSometimesVote | 0.010 | -0.033, 0.054 |
| Self * voter_classSometimesVote | 0.059 | 0.012, 0.106 |
| Neighbors * voter_classSometimesVote | -0.009 | -0.057, 0.038 |
| treatmentCivicDuty * voter_classAlwaysVote | -0.009 | -0.061, 0.045 |
| Hawthorne * voter_classAlwaysVote | 0.034 | -0.017, 0.086 |
| Self * voter_classAlwaysVote | 0.047 | -0.007, 0.100 |
| Neighbors * voter_classAlwaysVote | -0.002 | -0.056, 0.050 |
| 1
CI = Credible Interval |
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I generated a full plot that shows that past voter experience influences whether or not people vote. I also scrutinized the data to the principles of Justice and created a Population Table. Then I created a data generating mechanism through the use of modeling the data.