1 Environment preparation

# ### Data import ###
# install.packages("readr")     # read datasets
# install.packages("qualtRics") # read qualtrics datasets
# ### Data manipulation ###
# install.packages("dplyr")     # pipes and data manipulation
# ### Visualization ###
# install.packages("ggplot2")    # graphing capabilities
# ### Estimation ###
# install.packages("cjoint")    # base amce package
# install.packages("cregg")     # amce and mm 
# install.packages("factorEx")  # amce with non-uniform distribution

## Custom build functions 
# library(devtools)
# install_github("albertostefanelli/cjoint") # fixes some problem with cjoint

### Data import ###
library("readr")     
library("qualtRics") 
### Data manipulation ###
library("dplyr")     
### Visualization ###
library("ggplot2")    
### Estimation ###
library("cjoint")   
library("cregg")     
library("factorEx")  

2 Data

1. From the paper “The Ties that Double Bind” (Teele, Kalla, and Rosenbluth 2018)
2. We are going to use the Legislators and Voters sample
3. The authors want to test different type of gender biases
   • Among Legislators
   • Among Voters

• Variables in the dataset
  • CJ choice task= “winner”
    
  • Type of sample = “sample”
  • Respondent’s gender=“female_respondent”
    
  • Respondent’s PID= “democrat_respondent”, “republican_respondent”
    
  • Candidate’s gender= “orig_cand_female”
    
  • Candidate’s political Experience= “orig_0ys”, “orig_1ys”, “orig_3ys”, “orig_8ys”
  • Candidate’s number of Children= “orig_0ch”, “orig_1ch”, “orig_3ch”
    
  • Candidate’s political Post= “orig_may”, “orig_leg”
    
  • Candidate’s occupation= “orig_law”, “orig_leg”, “orig_may”, “orig_teach”
    
  • Candidate’s spouse’s Occupation= “orig_UN_sp”, “orig_FM_sp”, “orig_MD_sp”
    
  • Candidate’s candidate’s Age= “orig_29”, “orig_45”, orig_65”

3 Task

1. Attributes’ interactions
   • Postulate Hypothesises/Expectation about attributes interactions
   • Test your hypothesises
   • Estimate the least-liked profile
2. Sub-group analysis
   • Postulate Hypothesises/Expectation about subgroups
   • Test your hypothesises
   • Estimate the least-liked profile
3. Estimate a model with a non-uniform marginal distribution (data taken from legislatoR)
   • Gender .4, .6
   • Experience .2, .30, .3, .2
   • Children .33, .33, .33
   • Occupation .10, .60, .20, .10
   • Spouse’s Occupation .33, .33, .33
   • Age .20, .55, .25
4. Try replicate the sub-group analysis using a mixture model [OPTIONAL]
   • Include a concomitant variable (respondent-varying) in the estimation of the mixture components

# load the data 
teele_data <- readr::read_csv("https://github.com/albertostefanelli/conjoint_class/raw/master/data/teele.csv")