Pretest Data

Condition 4: Judicial Independence - Courtpacking

Decisive / Indecisive evaluation

Indecisive associated with feminine stereotypes, decisive with masculine

Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.000   2.588   4.000   5.000 

Mean 2.6, skews somewhat masculine

Strong / Weak evaluation

Weak associated with feminine stereotypes, strong with masculine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   3.044   4.250   5.000 

Mean 3.0, neutral

Inspiring / Uninspiring evaluation

Uninspiring associated with feminine stereotypes, inspiring with masculine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.750   3.000   3.412   4.000   5.000 

Mean 3.4, skews somewhat feminine

Dishonest / Honest evaluation

Dishonest associated with masculine stereotypes, honest with feminine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   3.000   2.515   3.250   5.000 

Mean scores 2.5, somewhat masculine

Harsh / Compassionate evaluation

Harsh associated with masculine stereotypes, compassionate with feminine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.000   2.368   3.000   4.000 

Mean scores 2.36, skews very masculine

Against equality / For equality

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.500   2.588   4.000   5.000 

Mean 2.6, skews slightly masculine

Party Identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   2.985   4.000   5.000 

Democrat party identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   2.00    2.00    3.00    3.48    5.00    5.00 

Republican party identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.000   2.353   3.000   5.000 

Difference in means T test

filtered_data_no_independents <- pretest_data |> 
  filter(Condition.number == 4) |> #filter for condition 4
  filter(partyid != 0) #filter out independents

#print t test
t.test(perceived.party ~ partyid, data = filtered_data_no_independents)

    Welch Two Sample t-test

data:  perceived.party by partyid
t = -2.8887, df = 33.027, p-value = 0.00678
alternative hypothesis: true difference in means between group -1 and group 1 is not equal to 0
95 percent confidence interval:
 -1.920815 -0.333303
sample estimates:
mean in group -1  mean in group 1 
        2.352941         3.480000 

Statistically significant difference in means