Pretest Data

Condition 1: Executive Aggrandizement

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   2.206   3.000   5.000 

Mean 2.2, skews 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   2.714   4.000   5.000 

Mean 2.74, skews somewhat masculine

Inspiring / Uninspiring evaluation

Uninspiring associated with feminine stereotypes, inspiring with masculine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   3.000   4.000   3.508   5.000   5.000 

Mean 3.5, skews feminine

Dishonest / Honest evaluation

Dishonest associated with masculine stereotypes, honest with feminine

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.500   3.000   2.571   4.000   5.000 

Mean scores 2.5, skews 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   2.397   3.000   5.000 

Mean scores 2.39, skews masculine

Against Equality / For Equality

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   3.000   2.413   3.000   5.000 

Mean 2.4, skews masculine

Party Identification

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    2.00    3.00    3.27    4.00    5.00 

Democrat evaluation of ideology

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   3.000   4.000   3.737   5.000   5.000 

Republican evaluation of ideology

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   2.000   3.000   2.714   3.000   5.000 

Difference in means T test

filtered_data_no_independents <- pretest_data |> 
  filter(Condition.number == 1) |> #filter for condition 1
  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 = -2.7237, df = 33.473, p-value = 0.01018
alternative hypothesis: true difference in means between group -1 and group 1 is not equal to 0
95 percent confidence interval:
 -1.785953 -0.259160
sample estimates:
mean in group -1  mean in group 1 
        2.714286         3.736842 

#why would I make Republican = -1 and Democrat = 1. Did i think that was clever? Do I enjoy confusing future emma???

Statistically significant difference in means