Warning: package 'brms' was built under R version 4.4.1Warning: package 'Rcpp' was built under R version 4.4.1Warning: package 'tidybayes' was built under R version 4.4.1Warning: package 'gtsummary' was built under R version 4.4.1Causal Effect
Warning in tidy.brmsfit(x, ..., effects = "fixed"): some parameter names
contain underscores: term naming may be unreliable!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 |
||
\[y_{i} = \beta_{0} + \beta_{1} age\_z + \beta_{2}male_i + \beta_{3}civic\_duty_i + \\ \beta_{4}hawthorne_i + \beta_{5}self_i + \beta_{6}neighbors_i + \\ \beta_{7}Sometimes\ vote_i + \beta_{8}Always\ vote_i + \\ \beta_{9}civic\_duty_i Sometimes\ vote_i + \beta_{10}hawthorne_i Sometimes\ vote_i + \\ \beta_{11}self_i Sometimes\ vote_i + \beta_{11}neighbors_i Sometimes\ vote_i + \\ \beta_{12}civic\_duty_i Always\ vote_i + \beta_{13}hawthorne_i Always\ vote_i + \\ \beta_{14}self_i Always\ vote_i + \beta_{15}neighbors_i Always\ vote_i + \epsilon_{i}\]
Using the data from an experiment to find out whether and to what extent people are motivated to vote by social pressure, we seek to forecast the causal effect on voter participation of sending postcards in the Texas gubernatorial general election of 2026. Stability might not be true because the way people view politics has changed from 2006 because of new things such as social media. We modeled primary_06, a binary 0/1 integer variable indicating whether the respondent voted in the 2006 primary election, and the type of postcard they recieved. People who have been voting in the past are more likely to vote again.