Bring in the current data and necessary packages.
setwd("C:/Work Files/RDA/Consults/Ewa Golebiowska")
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## cjoint: AMCE Estimator for Conjoint Experiments
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## Authors: Soubhik Barari, Elissa Berwick, Jens Hainmueller, Daniel Hopkins, Sean Liu, Anton Strezhnev, Teppei Yamamoto
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## viewTESS_Data <-read_sav("TESS conjoint data.sav")Create a set of new variables for Q9 - Q16 based on the party affiliations for candidates A and B for each comparison. This new variable is named Qn_Partisan_Comp, where n=question number (e.g., Q9, Q10, etc.). This variable is an unordered factor where: 1 = both candidates are democrats 2 = both candidates are republicans 3 = candidate ‘A’ is a democrat and candidate ‘B’ is a republican 4 = candidate ‘A’ is a republican and candidate ‘B’ is a democrat
If this only needs to be a binary choice (0=partisan, 1 = bipartisan), then this variable can be recoded such that if value is 1 or 2, then = 0; if value is 3 or 4, then = 1.
TESS_Data_New <- TESS_Data
TESS_Data_New <- TESS_Data %>%
mutate(Q9_Partisan_Comp = case_when(
Q9A_Att7Party == 1 & Q9B_Att7Party == 1 ~ 1,
Q9A_Att7Party == 2 & Q9B_Att7Party == 2 ~ 2,
Q9A_Att7Party == 1 & Q9B_Att7Party == 2 ~ 3,
Q9A_Att7Party == 2 & Q9B_Att7Party == 1 ~ 4
))
TESS_Data_New_2<- TESS_Data_New %>%
mutate(Q10_Partisan_Comp = case_when(
Q10A_Att7Party == 1 & Q10B_Att7Party == 1 ~ 1,
Q10A_Att7Party == 2 & Q10B_Att7Party == 2 ~ 2,
Q10A_Att7Party == 1 & Q10B_Att7Party == 2 ~ 3,
Q10A_Att7Party == 2 & Q10B_Att7Party == 1 ~ 4
))
TESS_Data_New_3 <- TESS_Data_New_2 %>%
mutate(Q11_Partisan_Comp = case_when(
Q11A_Att7Party == 1 & Q11B_Att7Party == 1 ~ 1,
Q11A_Att7Party == 2 & Q11B_Att7Party == 2 ~ 2,
Q11A_Att7Party == 1 & Q11B_Att7Party == 2 ~ 3,
Q11A_Att7Party == 2 & Q11B_Att7Party == 1 ~ 4
))
TESS_Data_New_4 <- TESS_Data_New_3 %>%
mutate(Q12_Partisan_Comp = case_when(
Q12A_Att7Party == 1 & Q12B_Att7Party == 1 ~ 1,
Q12A_Att7Party == 2 & Q12B_Att7Party == 2 ~ 2,
Q12A_Att7Party == 1 & Q12B_Att7Party == 2 ~ 3,
Q12A_Att7Party == 2 & Q12B_Att7Party == 1 ~ 4
))
TESS_Data_New_5 <- TESS_Data_New_4 %>%
mutate(Q13_Partisan_Comp = case_when(
Q13A_Att7Party == 1 & Q13B_Att7Party == 1 ~ 1,
Q13A_Att7Party == 2 & Q13B_Att7Party == 2 ~ 2,
Q13A_Att7Party == 1 & Q13B_Att7Party == 2 ~ 3,
Q13A_Att7Party == 2 & Q13B_Att7Party == 1 ~ 4
))
TESS_Data_New_6 <- TESS_Data_New_5 %>%
mutate(Q14_Partisan_Comp = case_when(
Q14A_Att7Party == 1 & Q14B_Att7Party == 1 ~ 1,
Q14A_Att7Party == 2 & Q14B_Att7Party == 2 ~ 2,
Q14A_Att7Party == 1 & Q14B_Att7Party == 2 ~ 3,
Q14A_Att7Party == 2 & Q14B_Att7Party == 1 ~ 4
))
TESS_Data_New_7 <- TESS_Data_New_6 %>%
mutate(Q15_Partisan_Comp = case_when(
Q15A_Att7Party == 1 & Q15B_Att7Party == 1 ~ 1,
Q15A_Att7Party == 2 & Q15B_Att7Party == 2 ~ 2,
Q15A_Att7Party == 1 & Q15B_Att7Party == 2 ~ 3,
Q15A_Att7Party == 2 & Q15B_Att7Party == 1 ~ 4
))
TESS_Data_New_8 <- TESS_Data_New_7 %>%
mutate(Q16_Partisan_Comp = case_when(
Q16A_Att7Party == 1 & Q16B_Att7Party == 1 ~ 1,
Q16A_Att7Party == 2 & Q16B_Att7Party == 2 ~ 2,
Q16A_Att7Party == 1 & Q16B_Att7Party == 2 ~ 3,
Q16A_Att7Party == 2 & Q16B_Att7Party == 1 ~ 4
))
TESS_Data_Final <- TESS_Data_New_8In order to makes sure the recoding matches the original data we can compare the frequencies of the 4 categories of the new variable with a crosstabs from the original variables.
table(TESS_Data_Final$Q9_Partisan_Comp)##
## 1 2 3 4
## 351 322 302 299crosstab <- table(TESS_Data_New$Q9A_Att7Party, TESS_Data_New$Q9B_Att7Party)
print(crosstab)##
## 1 2
## 1 351 302
## 2 299 322table(TESS_Data_Final$Q10_Partisan_Comp)##
## 1 2 3 4
## 358 318 293 305crosstab <- table(TESS_Data_New$Q10A_Att7Party, TESS_Data_New$Q10B_Att7Party)
print(crosstab)##
## 1 2
## 1 358 293
## 2 305 318table(TESS_Data_Final$Q11_Partisan_Comp)##
## 1 2 3 4
## 354 313 298 309crosstab <- table(TESS_Data_New$Q11A_Att7Party, TESS_Data_New$Q11B_Att7Party)
print(crosstab)##
## 1 2
## 1 354 298
## 2 309 313table(TESS_Data_Final$Q12_Partisan_Comp)##
## 1 2 3 4
## 354 324 296 300crosstab <- table(TESS_Data_New$Q12A_Att7Party, TESS_Data_New$Q12B_Att7Party)
print(crosstab)##
## 1 2
## 1 354 296
## 2 300 324table(TESS_Data_Final$Q13_Partisan_Comp)##
## 1 2 3 4
## 357 320 292 305crosstab <- table(TESS_Data_New$Q13A_Att7Party, TESS_Data_New$Q13B_Att7Party)
print(crosstab)##
## 1 2
## 1 357 292
## 2 305 320table(TESS_Data_Final$Q14_Partisan_Comp)##
## 1 2 3 4
## 349 321 296 308crosstab <- table(TESS_Data_New$Q14A_Att7Party, TESS_Data_New$Q14B_Att7Party)
print(crosstab)##
## 1 2
## 1 349 296
## 2 308 321table(TESS_Data_Final$Q15_Partisan_Comp)##
## 1 2 3 4
## 355 318 296 305crosstab <- table(TESS_Data_New$Q15A_Att7Party, TESS_Data_New$Q15B_Att7Party)
print(crosstab)##
## 1 2
## 1 355 296
## 2 305 318table(TESS_Data_Final$Q16_Partisan_Comp)##
## 1 2 3 4
## 356 318 300 300crosstab <- table(TESS_Data_New$Q16A_Att7Party, TESS_Data_New$Q16B_Att7Party)
print(crosstab)##
## 1 2
## 1 356 300
## 2 300 318Since the cell frequencies for the new variables and the original variables match we can make the new variables unordered facors that can be used in the cjoint design file and as a predictor/control in the AMCE model
TESS_Data_Final$Q9_Partisan_Comp <-factor(TESS_Data_Final$Q9_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q10_Partisan_Comp <-factor(TESS_Data_Final$Q10_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q11_Partisan_Comp <-factor(TESS_Data_Final$Q11_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q12_Partisan_Comp <-factor(TESS_Data_Final$Q12_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q13_Partisan_Comp <-factor(TESS_Data_Final$Q13_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q14_Partisan_Comp <-factor(TESS_Data_Final$Q14_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q15_Partisan_Comp <-factor(TESS_Data_Final$Q15_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))
TESS_Data_Final$Q16_Partisan_Comp <-factor(TESS_Data_Final$Q16_Partisan_Comp,
levels = c(1,2,3,4),
labels = c("Both Candidates Democrats",
"Both Candidates Republican",
"Democrat A Republican B",
"Republican A Democrat B" ))Finally we can write the new file to both CSV and SPSS formats
write.csv(TESS_Data_Final,"TESS_Data_Final.csv")
write_sav(TESS_Data_Final,"TESS_Data_Final.sav")