#load packages needed for this assignment
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 3.3.2
## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Warning: package 'ggplot2' was built under R version 3.3.2
## Warning: package 'tidyr' was built under R version 3.3.2
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag():    dplyr, stats
library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

Data cleaning

d = read.csv("~/Desktop/PSYC254/livingston2012/Pilot_A.csv")


d.tidy = d %>%
  filter(manipcheck == 0) %>%
  dplyr::select(T_race, T_gender, T_behavior, Q11_1:Q11_4, Q13_1, Q14_1, Q17,Q19,Q21,Q23,Q25, Q27,Q29) %>%
  rename(lead_well=Q11_1) %>%
  rename(lead_max=Q11_2) %>%
  rename(lead_admire=Q11_3) %>%
  rename(lead_respect=Q11_4) %>%
  rename(lead_salary = Q13_1) %>%
  rename(PvsS = Q14_1) %>%
  rename(race = Q17) %>%
  rename(age = Q19) %>%
  rename(gender = Q21) %>%
  rename(educ = Q23) %>%
  rename(workexp = Q25) %>%
  rename(pol_s = Q27) %>%
  rename(pol_e = Q29)

d.tidy$T_race = as.factor(d.tidy$T_race)
levels(d.tidy$T_race)[1] = "white"
levels(d.tidy$T_race)[2] = "black"
d.tidy$T_gender = as.factor(d.tidy$T_gender)
levels(d.tidy$T_gender)[1] = "male"
levels(d.tidy$T_gender)[2] = "female"
d.tidy$T_behavior = as.factor(d.tidy$T_behavior)
levels(d.tidy$T_behavior)[1] = "agentic"
levels(d.tidy$T_behavior)[2] = "communal"


d.tidy$lead_well = as.numeric(d.tidy$lead_well) 
d.tidy$lead_max = as.numeric(d.tidy$lead_max)
d.tidy$lead_admire = as.numeric(d.tidy$lead_admire)
d.tidy$lead_respect = as.numeric(d.tidy$lead_respect)
d.tidy$lead_salary = as.numeric(d.tidy$lead_salary)

d.tidy$leadev = rowMeans(d.tidy[,4:8]) #make composite 
lead = matrix(c(d.tidy$lead_well, d.tidy$lead_max, d.tidy$lead_admire, d.tidy$lead_respect,d.tidy$lead_salary), ncol=5)
#alpha(lead) #reliability check -- won't run with the small dataset I have now, but should work in the future

Analyses

The main analyses from Livingston et al, 2012.

#summary(lm(leadev ~ T_race*T_gender*T_behavior, d.tidy)) #main 3 way interaction (leader evaluations)

d.women = d.tidy %>%
  filter(T_gender == "female")

#summary(lm(leadev ~ T_race*T_behavior, d.women)) #2 way for women (leader evaluations)

d.men = d.tidy %>%
  filter(T_gender == "male")

#summary(lm(leadev ~ T_race*T_behavior, d.men)) #2 way for men (leader evaluations)


#summary(lm(PvsS ~ T_race*T_gender*T_behavior,d.tidy)) #3 way interaction (attribution of behavior)
#summary(lm(PvsS ~ T_race*T_behavior,d.women)) #2 way for women (attribution of behavior)
#summary(lm(PvsS ~ T_race*T_behavior,d.men)) #2 way for men (attribution of behavior)
summary(lm(PvsS ~ T_race,d.tidy)) #main effect of race
## 
## Call:
## lm(formula = PvsS ~ T_race, data = d.tidy)
## 
## Residuals:
##          1          2          3          4          5 
##  5.000e-01 -8.327e-17 -5.000e-01  0.000e+00  0.000e+00 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   4.0000     0.2357  16.971 0.000446 ***
## T_raceblack  -0.5000     0.3727  -1.342 0.272228    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4082 on 3 degrees of freedom
## Multiple R-squared:  0.375,  Adjusted R-squared:  0.1667 
## F-statistic:   1.8 on 1 and 3 DF,  p-value: 0.2722