Pretest Data

Condition 3: Media Censorship

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   1.884   2.000   4.000 

Mean 1.88, skews very masculine

Strong / Weak evaluation

Weak associated with feminine stereotypes, strong with masculine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   2.000   2.333   3.000   5.000 

Mean 2.3, skews masculine

Inspiring / Uninspiring evaluation

Uninspiring associated with feminine stereotypes, inspiring with masculine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   3.174   5.000   5.000 

Mean 3.1, skews somewhat feminine

Dishonest / Honest evaluation

Dishonest associated with masculine stereotypes, honest with feminine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   3.029   4.000   5.000 

Mean scores 3.02, neutral

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   2.377   3.000   5.000 

Mean scores 2.37, skews very masculine

Against equality / For equality

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   2.986   4.000   5.000 

Mean 2.9, neutral

Party Identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   3.000   4.000   3.478   5.000   5.000 

Democrat party identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      2       3       4       4       5       5 

Republican party identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      1       2       3       3       5       5 

Difference in means T test

filtered_data_no_independents <- pretest_data |> 
  filter(Condition.number == 3) |> #filter for condition 3
  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 = -2.2039, df = 28.38, p-value = 0.03581
alternative hypothesis: true difference in means between group -1 and group 1 is not equal to 0
95 percent confidence interval:
 -1.92888912 -0.07111088
sample estimates:
mean in group -1  mean in group 1 
               3                4 

Statistically significant difference in means