N Parameters

Author

Elaine Zhang

We have a table of citizens in Michigan and we want to find if a postcard would change the civic engagement of the citizens in 2026. One problem with the data is that it’s from Michigan, which wouldn’t accurately portray Texas citizens. The neighbors treatment seems to be the most effective.

\[y_{i} = \beta_{0} + \beta_{1} age\_z + \beta_{2}male_i + \beta_{3}civic\_duty_i + \\ \beta_{4}hawthorne_i + \beta_{5}self_i + \beta_{6}neighbors_i + \\ \beta_{7}Sometimes\ vote_i + \beta_{8}Always\ vote_i + \\ \beta_{9}civic\_duty_i Sometimes\ vote_i + \beta_{10}hawthorne_i Sometimes\ vote_i + \\ \beta_{11}self_i Sometimes\ vote_i + \beta_{11}neighbors_i Sometimes\ vote_i + \\ \beta_{12}civic\_duty_i Always\ vote_i + \beta_{13}hawthorne_i Always\ vote_i + \\ \beta_{14}self_i Always\ vote_i + \beta_{15}neighbors_i Always\ vote_i + \epsilon_{i}\]

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