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Oney,Ardag_2021_TS_replication_syntax.Rmd
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Oney,Ardag_2021_TS_replication_syntax.Rmd
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---
title: "TR Political attitudes at critical junctures"
output:
word_document: default
html_document:
df_print: paged
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```
# STUDY 1: ISSP Citizenship
## prep the variables of interest
ISSP Citizenship Module 2 is publicly available @ https://www.gesis.org/issp/modules/issp-modules-by-topic/citizenship/2014/
```{r}
library(haven)
library(tidyverse)
TRcitz <- read_stata ("ZA6670_v2-0-0.dta") %>%
as_tibble() %>%
select(C_ALPHAN,
V5, V6, V7, V8, V9, V10, V11, V12, V13,
V32, V33, V34, V35, V36, V37, V38, V39, V40, V62,
SEX, AGE, EDUCYRS, TR_DEGR,
TR_RINC, TR_INC,
TR_RELIG, TR_ETHN1,
URBRURAL,
TR_PRTY, V48) %>%
filter(C_ALPHAN %in% c("TR")) %>%
rename(AlwVot=V5, NevaTax=V6, ObLaw=V7, WaAcGov=V8, AcSoPoAs=V9, UndReaOP=V10,
EnvFri=V11, HlpLpriC=V12, HlpLpriW=V13,
SaStdLiv=V32, GovProMinRig=V33, PubParDM=V34, CivDis=V35, GovResDemRig=V36, PeLoCitRrev=V37,
LTRrtVot=V38, CitnoVot=V39, HealCar=V40, DemT=V62,
gender=SEX, age=AGE, edu=EDUCYRS, CS_eduCat=TR_DEGR,
per_income=TR_RINC, hh_income=TR_INC,
Religion=TR_RELIG, Ethnicity=TR_ETHN1,
UrbanRural=URBRURAL,
GroupVote=TR_PRTY, PO_LR=V48) %>%
mutate(GroupVoteBIN = GroupVote)
colnames(TRcitz)
library(psych)
describe(TRcitz[,-1])
```
### define missing values
```{r}
library(naniar)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("AlwVot","NevaTax","ObLaw",
"WaAcGov","AcSoPoAs","UndReaOP","EnvFri","HlpLpriC","HlpLpriW",
"SaStdLiv","GovProMinRig","PubParDM",
"CivDis","GovResDemRig","PeLoCitRrev","LTRrtVot","CitnoVot","HealCar"),
condition = ~.x == 8)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("AlwVot","NevaTax","ObLaw",
"WaAcGov","AcSoPoAs","UndReaOP","EnvFri","HlpLpriC","HlpLpriW",
"SaStdLiv","GovProMinRig","PubParDM",
"CivDis","GovResDemRig","PeLoCitRrev","LTRrtVot","CitnoVot","HealCar",
"gender"),
condition = ~.x == 9)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("edu", "PO_LR"),
condition = ~.x == 98)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("DemT", "GroupVote", "GroupVoteBIN","CS_eduCat", "PO_LR"),
condition = ~.x == 99)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("GroupVote", "GroupVoteBIN"),
condition = ~.x == 0)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("age"),
condition = ~.x == 999)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("per_income"),
condition = ~.x == 999999)
TRcitz <- TRcitz %>%
replace_with_na_at(.vars = c("hh_income"),
condition = ~.x == 999999)
```
### voted for AKP vs. non-AKP (any other party)
```{r}
TRcitz$GroupVoteBIN <- car::recode(TRcitz$GroupVoteBIN,"1=1; 2=0; 3=0; 4=0; 5=0; 6=0; 7=0; 94=0; 96=0")
library(janitor)
tabyl(TRcitz$GroupVoteBIN) # non-AKP 0.43 AKP 0.57
# 1=AKP 3=BDP 4=CHP 6=MHP 100=other
TRcitz$GroupVote <- car::recode(TRcitz$GroupVote,"1=1; 3=3; 4=4; 6=6; 2=100; 5=100; 7=100; 94=100; 96=100")
tabyl(TRcitz$GroupVote) # AKP 0.57 BDP 0.10 CHP 0.21 MHP 0.09 other 0.03
TRcitz$GroupVoteBIN <- factor(TRcitz$GroupVoteBIN, levels=c(0,1), labels = c("nonAKPv", "AKPv"))
TRcitz$GroupVote <- factor(TRcitz$GroupVote, levels=c(1,3,4,6,100), labels = c("AKP","BDP","CHP","MHP","Other"))
```
### socio-demographics
```{r}
TRcitz$ageI <- cut(TRcitz$age, breaks=c(20,30,40,50,60, 70, 80, Inf),
labels=c("18-29", "30-39", "40-49", "50-59", "60-69", "70-79", ">80"))
TRcitz$gender <- factor(TRcitz$gender, levels = c(1,2), labels = c("Male", "Female"))
TRcitz$CS_eduCat <- factor(TRcitz$CS_eduCat, levels = c(1,2,3,4,5,6,7),
labels = c("NoFormal", "Primary", "jHigh", "High", "IncompleteUni", "Uni", "postGrad"))
TRcitz$CS_eduCat <- fct_collapse(TRcitz$CS_eduCat,
NoFormal = "NoFormal",
somePrimarySchool = "Primary",
someHighSchool = c("jHigh", "High", "IncompleteUni"),
UniHigher = c("Uni", "postGrad"))
tabyl(TRcitz$CS_eduCat)
TRcitz$per_incomeCat <- cut(TRcitz$per_income,
breaks=c(-Inf, 1000, 3000, Inf),
labels=c("<1000TL","1000-3000TL",">3000TL")) # define personal income categories
TRcitz$hh_incomeCat <- cut(TRcitz$hh_income,
breaks=c(-Inf, 1500, 3000, Inf),
labels=c("<1500TL","1500-3000TL",">3000TL")) # define household income categories
TRcitz$Religion <- factor(TRcitz$Religion, levels = c(690, 999, 0, 950, 300, 500),
labels = c("Muslim", "Other", "Other", "Other", "Other","Other"))
TRcitz$Ethnicity <- factor(TRcitz$Ethnicity, levels = c(2, 6, 4, 99, 1, 3, 95),
labels = c("Bulgarian", "Turk", "Kurd", "No answer", "Arab","Circassian", "Other"))
TRcitz$UrbanRural <- factor(TRcitz$UrbanRural, levels = c(1,2,3,4,5),
labels= c("A big city", "The suburbs or outskirts of a",
"A town or a small city",
"A country village", "A farm or home in the country"))
describe(TRcitz[,-c(1)])
```
### sample characteristics
```{r}
EDU_bp <- TRcitz %>% dplyr::select(CS_eduCat) %>% drop_na() %>%
ggplot(., aes(x=CS_eduCat)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Education") +
coord_flip()
INC_bp <- TRcitz %>% dplyr::select(hh_incomeCat) %>% drop_na() %>%
ggplot(., aes(x=hh_incomeCat)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Household Income") +
coord_flip()
UR_bp <- TRcitz %>% dplyr::select(UrbanRural) %>% ggplot(., aes(x=UrbanRural)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Urban vs. Rural") +
coord_flip()
ETH_bp <- TRcitz %>% dplyr::select(Ethnicity) %>% drop_na() %>%
ggplot(., aes(x=Ethnicity)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Ethnicity") +
coord_flip()
REL_bp <- TRcitz %>% dplyr::select(Religion) %>% drop_na() %>%
ggplot(., aes(x=Religion)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Religion") +
coord_flip()
VR_bp <- TRcitz %>% dplyr::select(GroupVote) %>% drop_na() %>%
ggplot(., aes(x=GroupVote)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..), label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Vote Recall") +
coord_flip()
LR_bp <- TRcitz %>% dplyr::select(PO_LR) %>% drop_na() %>%
ggplot(., aes(x=as_factor(PO_LR))) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
scale_x_discrete(labels = c("Left","1","2","3","4","5","6","7","8","9","Right")) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black", angle=45) +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Left-Right")
GE_bp <- TRcitz %>% dplyr::select(gender) %>% drop_na() %>%
ggplot(., aes(x=gender)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Gender") +
coord_flip()
AG_bp <- TRcitz %>% dplyr::select(ageI) %>% drop_na() %>%
ggplot(., aes(x=ageI)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Age") +
coord_flip()
library(grid)
library(gridExtra)
grid.arrange(GE_bp, AG_bp, ETH_bp,
REL_bp, EDU_bp, INC_bp,
UR_bp, LR_bp, VR_bp,
nrow = 3, ncol = 3,
top = "Sample characteristics in Study 1",
bottom = textGrob("Source: ISSP Citizenship Module 3
\nhttps://www.gesis.org/issp/modules/issp-modules-by-topic/citizenship/2014/
\nData Collection: 2015",
gp = gpar(fontface = 3, fontsize = 10), just = "center"))
rm(list=setdiff(ls(), "TRcitz"))
```
### descriptive statistics for TR citz
```{r}
TRcitz_ds <- describe(TRcitz[,-c(1)])
TRcitz_ds <- round(TRcitz_ds,2)
library(knitr)
TRcitz_ds %>% kable()
rm(TRcitz_ds)
```
## STUDY 1 ISSP citizenship data analyses
### explore the dimensions
#### conceptions of good citizen
```{r}
paCIT <- fa.parallel (TRcitz [2:10], fm="ml", fa="fa", main = " PA Conceptions of Good Citizenship") # parallel analysis
paCIT # suggests that the number of factors = 3
vssCIT <- vss (TRcitz [2:10], n = 9, fm="mle", rotate = "oblimin", title = "VSS Conceptions of Good Citizenship") # Very simple structure
vssCIT
cormatCIT <- corFiml(TRcitz [,2:10], covar = F, show = F) # correlation matrix
CIT2d <- fa(r=cormatCIT, nfactors=2, fm="ml", rotate="oblimin") # efa 2 dimensions
print (CIT2d, cut=0.3, sort = T)
fa.diagram (CIT2d)
CIT3d <- fa(r=cormatCIT, nfactors=3, fm="ml", rotate="oblimin") # efa 3 dimensions
print (CIT3d, cut=0.3, sort = T)
fa.diagram (CIT3d)
CIT4d <- fa(r=cormatCIT, nfactors=4, fm="ml", rotate="oblimin") # efa 4 dimensions
print (CIT4d, cut=0.3, sort = T)
fa.diagram (CIT4d)
# confirmatory analysis
library(lavaan)
modelCIT <- '
hlpCit =~ HlpLpriC + HlpLpriW
dtyCit =~ NevaTax + ObLaw + AlwVot
actCit =~ AcSoPoAs + UndReaOP + WaAcGov + EnvFri
'
fitCIT <- cfa (modelCIT, data=TRcitz, estimator="mlr", std.ov=T, std.lv=T, mimic="mplus", missing="fiml")
summary(fitCIT, fit.measures=T, standardized=T, rsquare=T)
library(semPlot)
semPaths(fitCIT, "mod", "std", intercepts = F, edge.label.cex=1.1, layout="tree3", rotation = 4, curve = 2)
library(semTools)
round(reliability(fitCIT),2)
CIT_fs <- lavPredict(fitCIT, type = "lv", method = "EBM", label = T, fsm = F)
CIT_fs <- as.data.frame(CIT_fs)
describe(CIT_fs)
hist(CIT_fs)
TRcitz$hlpCit <- scales::rescale(CIT_fs$hlpCit, to = c(0, 1), from = range(CIT_fs$hlpCit, na.rm = F, finite = T))
TRcitz$dtyCit <- scales::rescale(CIT_fs$dtyCit, to = c(0, 1), from = range(CIT_fs$dtyCit, na.rm = F, finite = T))
TRcitz$actCit <- scales::rescale(CIT_fs$actCit, to = c(0, 1), from = range(CIT_fs$actCit, na.rm = F, finite = T))
colnames(TRcitz)
library(ggridges)
ggr_AC <- TRcitz %>% select(actCit, GroupVoteBIN) %>%
ggplot(aes(x=actCit, y=GroupVoteBIN, fill = GroupVoteBIN)) + geom_density_ridges(alpha=.1) + theme_bw() +
xlab("Normalized Factor Scores") +
ylab("") +
ggtitle("Active Citizen") + theme(legend.position = "none")
ggr_DC <- TRcitz %>% select(dtyCit, GroupVoteBIN) %>%
ggplot(aes(x=dtyCit, y=GroupVoteBIN, fill = GroupVoteBIN)) + geom_density_ridges(alpha=.1) + theme_bw() +
xlab("Normalized Factor Scores") +
ylab("") +
ggtitle("Dutiful Citizen") + theme(legend.position = "none")
ggr_HC <- TRcitz %>% select(hlpCit, GroupVoteBIN) %>%
ggplot(aes(x=hlpCit, y=GroupVoteBIN, fill = GroupVoteBIN)) + geom_density_ridges(alpha=.1) + theme_bw() +
xlab("Normalized Factor Scores") +
ylab("") +
ggtitle("Helpful Citizen")
grid.arrange(ggr_AC,ggr_DC,ggr_HC,
nrow = 2, ncol = 2,
top = "Ridge Plots",
left= "VoteRecall")
```
#### conceptions of democracy
```{r}
paDEM <- fa.parallel (TRcitz [,11:19], fm="ml", fa="fa", main = " PA Conceptions of Democracy") # parallel analysis
paDEM # suggests that the number of factors = 3
vssDEM <- vss (TRcitz [,11:19], n = 9, fm="mle", rotate = "oblimin", title = "VSS Conceptions of Democracy") # Very simple structure
vssDEM
cormatDEM <- corFiml(TRcitz [,11:19], covar = F, show = F) # correlation matrix
DEM2d <- fa(r=cormatDEM, nfactors=2, fm="ml", rotate="oblimin") # efa 2 dimensions
print (DEM2d, cut=0.3, sort = T)
fa.diagram (DEM2d)
DEM3d <- fa(r=cormatDEM, nfactors=3, fm="ml", rotate="oblimin") # efa 3 dimensions
print (DEM3d, cut=0.3, sort = T)
fa.diagram (DEM3d)
DEM4d <- fa(r=cormatDEM, nfactors=4, fm="ml", rotate="oblimin") # efa 4 dimensions
print (DEM4d, cut=0.3, sort = T)
fa.diagram (DEM4d)
# confirmatory analysis
modelDEM2d <- '
DEMsd =~ GovProMinRig + PubParDM + SaStdLiv + GovResDemRig + HealCar
DEMpr =~ LTRrtVot + PeLoCitRrev + CivDis + CitnoVot
'
fitDEM2d <- cfa (modelDEM2d, data=TRcitz, estimator="mlr", std.ov=T, std.lv=T, mimic="mplus", missing="fiml")
summary(fitDEM2d, fit.measures=T, standardized=T, rsquare=T)
semPaths(fitDEM2d, "mod", "std", intercepts = F, edge.label.cex=1.1, layout="tree3", rotation = 4)
round(reliability(fitDEM2d),2)
# goodness of fit statistics are aaceptable, reliabilities are not bad
DEM_fs <- lavPredict(fitDEM2d, type = "lv", method = "EBM", label = T, fsm = F)
DEM_fs <- as.data.frame(DEM_fs)
describe(DEM_fs)
hist(DEM_fs)
TRcitz$DEMsd <- scales::rescale(DEM_fs$DEMsd, to = c(0, 1), from = range(DEM_fs$DEMsd, na.rm = F, finite = T))
TRcitz$DEMpr <- scales::rescale(DEM_fs$DEMpr, to = c(0, 1), from = range(DEM_fs$DEMpr, na.rm = F, finite = T))
ggr_DEp <- TRcitz %>% select(DEMpr, GroupVoteBIN) %>%
ggplot(aes(x=DEMpr, y=GroupVoteBIN, fill = GroupVoteBIN)) + geom_density_ridges(alpha=.1) + theme_bw() +
xlab("Normalized Factor Scores") +
xlim(0:1) +
ylab("") +
ggtitle("Civic Engagement") + theme(legend.position = "none")
ggr_DEs <- TRcitz %>% select(DEMsd, GroupVoteBIN) %>%
ggplot(aes(x=DEMsd, y=GroupVoteBIN, fill = GroupVoteBIN)) + geom_density_ridges(alpha=.1) + theme_bw() +
xlab("Normalized Factor Scores") +
xlim(0:1) +
ylab("") +
ggtitle("State's Duties")
grid.arrange(ggr_DEp,ggr_DEs,
nrow = 1, ncol = 2,
top = "Ridge Plots",
left= "VoteRecall")
colnames(TRcitz)
LC_ds <- describe(TRcitz[,35:39])
LC_ds %>% kable(digits = 2)
rm(list=setdiff(ls(), "TRcitz"))
```
### logistic regressions
```{r}
logit_sociodem <- glm(GroupVoteBIN ~ gender + age + hh_incomeCat + CS_eduCat,
data = TRcitz, family = binomial(link="logit"))
summary(logit_sociodem)
fmsb::NagelkerkeR2(logit_sociodem)
logit_citizen <- glm(GroupVoteBIN ~ hlpCit + dtyCit + actCit + gender + age + hh_incomeCat + CS_eduCat,
data = TRcitz, family = binomial(link="logit"))
summary(logit_citizen)
fmsb::NagelkerkeR2(logit_citizen)
# marginals
library(margins)
mar_cit <- margins(logit_citizen)
sum_mar_cit <-summary(mar_cit)
sum_mar_cit %>% kable(digits = 3)
logit_democracy <- glm(GroupVoteBIN ~ DEMsd + DEMpr + gender + age + hh_incomeCat + CS_eduCat,
data = TRcitz, family = binomial(link="logit"))
summary(logit_democracy)
fmsb::NagelkerkeR2(logit_democracy)
mar_dem <- margins(logit_democracy)
sum_mar_dem <- summary(mar_dem)
sum_mar_dem %>% kable(digits = 3)
logit_all <- glm(GroupVoteBIN ~ hlpCit + dtyCit + actCit + DEMsd + DEMpr + DemT + gender + age + hh_incomeCat + CS_eduCat,
data = TRcitz, family = binomial(link="logit"))
summary(logit_all)
fmsb::NagelkerkeR2(logit_all)
mar_all <- margins(logit_all)
sum_mar_all <-summary(mar_all)
sum_mar_all %>% kable(digits = 3)
```
#### regression tables
```{r}
library(strengejacke)
tab_model(logit_sociodem,logit_citizen,logit_democracy,
show.se = T,
transform = NULL,
pred.labels = c("Intercept",
"Gender",
"Age Group",
"Mid-Level Income", "High-Level Income",
"Some primary school completed",
"Some high school completed",
"University degree & higher",
"Helpful Citizen", "Dutiful Citizen", "Active Citizen",
"State's Duties", "Political Civic Engagement"),
dv.labels = c("Socio-demographics model","Citizenship model", "Democracy model"),
string.pred = "Coefficient",
string.ci = "CI (95%)",
string.p = "P-Value")
```
#### Marginal effects plots
```{r}
theme_set(theme_sjplot())
p_citz <- plot_model(logit_citizen, type = "eff", terms = "actCit [all]") +
labs(title = "Probability of voting for the AKP by being an active citizen",
tag = "A",
x = "Active Citizen (Normalized Factor Scores)", y = "")
p_dem <- plot_model(logit_democracy, type = "eff", terms = "DEMpr [all]") +
labs(title = "Probability of voting for the AKP by civic engagement",
tag = "B",
x = "Civic Engagement (Normalized Factor Scores)", y = "")
grid.arrange(p_citz, p_dem, ncol = 2, nrow=1,
left="Predicted % (AKP Vote)",
bottom="Note: All other variables held at their observed values
Source: ISSP Citizenship Module II") # plot side by side
```
```{r}
rm(list=ls(all.names = T)) # clear memory
```
# STUDY 2A: PEW Global Attitudes 2015
## prep the variables of interest
PEW Global Attitudes 2015 publicly available @ http://www.pewglobal.org/datasets/2015/
converted from .sav to .dta; file name is PewResearchGlobalAttitudesSpring2015DatasetforWebFINAL.dta
```{r}
TRpew <- read_stata ("PewResearchGlobalAttitudesSpring2015DatasetforWebFINAL.dta") %>%
as_tibble() %>%
select(COUNTRY,
Q9, Q11,
Q182TUR,
Q78TUR,
Q145, Q146, Q163B, Q163TUR, Q165TUR, Q165TURB,
Q219TUR, Q220TUR) %>%
filter(COUNTRY %in% c("34")) %>%
rename(DemFG_SL = Q9, SatWLvODem = Q11,
gender=Q145, age=Q146, edu=Q163B, CS_eduCat=Q163TUR,
hh_income=Q165TUR, hh_incomeBcat = Q165TURB,
Religion=Q78TUR,
UR=Q219TUR, UrbanRural=Q220TUR,
GroupVote=Q182TUR) %>%
mutate(GroupVoteBIN = GroupVote)
colnames(TRpew)
describe(TRpew)
```
### define missing values
```{r}
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("DemFG_SL","SatWLvODem"),
condition = ~.x == 8)
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("DemFG_SL","SatWLvODem"),
condition = ~.x == 9)
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("GroupVote", "GroupVoteBIN"),
condition = ~.x == 96)
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("GroupVote", "GroupVoteBIN"),
condition = ~.x == 97)
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("GroupVote", "GroupVoteBIN", "edu", "hh_income", "hh_incomeBcat"),
condition = ~.x == 98)
TRpew <- TRpew %>%
replace_with_na_at(.vars = c("GroupVote", "GroupVoteBIN", "age", "edu", "CS_eduCat", "hh_income", "hh_incomeBcat", "Religion"),
condition = ~.x == 99)
describe(TRpew [,-1])
```
### voted for AKP vs. non-AKP (any other party)
```{r}
TRpew$GroupVoteBIN <- car::recode(TRpew$GroupVoteBIN,"1=1; 2=0; 3=0; 4=0; 5=0; 6=0; 7=0; 8=0; 9=0; 10=0;
11=0; 12=0; 13=0; 14=0; 15=0; 16=0; 17=0; 18=0; 19=0; 20=0; 21=0; 22=0;
23=0; 24=0; 25=0; 26=0; 27=0")
TRpew$GroupVoteBIN <- factor(TRpew$GroupVoteBIN, levels=c(0,1), labels = c("nonAKPv", "AKPv"))
tabyl(TRpew$GroupVoteBIN)
# 1=AKP 15=HDP 5=CHP 2=MHP 100=other
TRpew$GroupVote <- car::recode(TRpew$GroupVote,"1=1; 19=2; 5=3; 15=4;
2=100; 3=100; 4=100; 6=100; 7=100; 8=100; 9=100; 10=100; 11=100; 12=100; 13=100; 14=100;
16=100; 17=100; 18=100; 20=100; 21=100; 22=100; 23=100; 24=100; 2100=100; 26=100; 27=100")
TRpew$GroupVote <- factor(TRpew$GroupVote, levels = c(1,2,3,4,100), labels = c("AKP","MHP","CHP","HDP","Other"))
tabyl(TRpew$GroupVote) # AKP 0.48 MHP 0.11 CHP 0.25 HDP 0.13 other 0.04
```
### socio-demographics
```{r}
TRpew$ageI <- cut(TRpew$age, breaks=c(20,30,40,50,60, 70, 80, Inf),
labels=c("18-29", "30-39", "40-49", "50-59", "60-69", "70-79", ">80"))
round(prop.table(table(TRpew$ageI)),2)
TRpew$gender <- factor(TRpew$gender, labels = c("Male", "Female"))
round(prop.table(table(TRpew$gender)),2) # M 0.53 F 0.47
round(prop.table(table(TRpew$CS_eduCat)),2)
TRpew$CS_eduCat <- factor(TRpew$CS_eduCat, levels = c(1:11), labels = c("NoFormal","IncompletePri","Primary5", "Primary8",
"Complete junior high school","Complete vocational school at junior high",
"Complete high school","Complete vocational school at high school",
"Uni degree", "Graduate degree", "PhD"))
TRpew$CS_eduCat <- fct_collapse(TRpew$CS_eduCat,
NoFormal = c("NoFormal","IncompletePri"),
somePrimarySchool = c("Primary5", "Primary8"),
someHighSchool = c("Complete junior high school","Complete vocational school at junior high",
"Complete high school","Complete vocational school at high school"),
UniHigher = c("Uni degree", "Graduate degree", "PhD"))
round(prop.table(table(TRpew$CS_eduCat)),2)
tabyl(TRpew$CS_eduCat)
TRpew$hh_incomeCat <- cut(TRpew$hh_income,
breaks=c(-Inf, 5, 9, Inf),
labels=c("low","medium","high")) # define household income categories
round(prop.table(table(TRpew$hh_incomeCat)),2) # 0.18 low 0.76 medium 0.06 high //roughly correspond to 1000 3000 divide
tabyl(TRpew$hh_incomeCat)
TRpew$hh_incomeBcat <- factor(TRpew$hh_incomeBcat, labels = c("<1000", ">1000")) # define household income categories alternative
round(prop.table(table(TRpew$hh_incomeBcat)),2) # <1000 0.13 >1000 0.87
TRpew$Religion <- factor(TRpew$Religion, levels = c(1,3,4,5,6),
labels = c("Muslim","Atheist","Agnostic","Nothing in particular","Something else"))
TRpew$Religion <- fct_collapse(TRpew$Religion,
Muslim = "Muslim",
Other = c("Atheist","Agnostic","Nothing in particular","Something else"))
tabyl(TRpew$Religion)
TRpew$UrbanRural <- factor(TRpew$UrbanRural, levels = c(1,2,3), labels = c("Urban center","Urban periphery","Rural"))
tabyl(TRpew$UrbanRural)
```
### sample characteristics
```{r}
GE_bp <- TRpew %>% select(gender) %>% drop_na() %>%
ggplot(., aes(x=gender)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Gender") +
coord_flip()
AG_bp <- TRpew %>% select(ageI) %>% drop_na() %>%
ggplot(., aes(x=ageI)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Age") +
coord_flip()
REL_bp <- TRpew %>% select(Religion) %>% drop_na() %>%
ggplot(., aes(x=Religion)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Religion") +
coord_flip()
EDU_bp <- TRpew %>% dplyr::select(CS_eduCat) %>% drop_na() %>%
ggplot(., aes(x=CS_eduCat)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Education") +
coord_flip()
INC_bp <- TRpew %>% dplyr::select(hh_incomeCat) %>% drop_na() %>%
ggplot(., aes(x=hh_incomeCat)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Household Income") +
coord_flip()
UR_bp <- TRpew %>% dplyr::select(UrbanRural) %>% ggplot(., aes(x=UrbanRural)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Urban vs. Rural") +
coord_flip()
VR_bp <- TRpew %>% dplyr::select(GroupVote) %>% drop_na() %>%
ggplot(., aes(x=GroupVote)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Feeling close to") +
coord_flip()
grid.arrange(GE_bp, AG_bp, REL_bp,
EDU_bp, INC_bp, UR_bp,
VR_bp,
nrow = 3, ncol = 3,
top = "Sample characteristics in Study 2a",
bottom = textGrob("Source: Pew Global Attitudes 2015
\nhttps://www.pewresearch.org/global/datasets/2015/
\nData Collection: 2015",
gp = gpar(fontface = 3, fontsize = 10), just = "center"))
rm(list=setdiff(ls(), "TRpew"))
```
### reverse code the following items
```{r}
round(prop.table(table(TRpew$SatWLvODem)),2)
TRpew$SatWLvODem <- car::recode(TRpew$SatWLvODem, "1=4; 2=3; 3=2; 4=1")
TRpew$SatWLvODem <- factor(TRpew$SatWLvODem, labels = c("Not at all satisfied","Not too satisfied", "Somewhat satisfied", "Very satisfied"))
round(prop.table(table(TRpew$SatWLvODem)),2)
tabyl(TRpew$SatWLvODem)
round(prop.table(table(TRpew$DemFG_SL)),2)
TRpew$DemFG_SL <- factor(TRpew$DemFG_SL, labels = c("Democratic form of government","Strong leader"))
round(prop.table(table(TRpew$DemFG_SL)),2)
tabyl(TRpew$DemFG_SL)
```
### cross-tabulation // descriptive statistics for response categories
```{r}
TRpew %>%
tabyl(DemFG_SL, GroupVoteBIN) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 2) %>%
adorn_ns() %>%
adorn_title("combined") %>%
kable()
TRpew %>%
tabyl(SatWLvODem, GroupVoteBIN) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 2) %>%
adorn_ns() %>%
adorn_title("combined") %>%
kable()
describe(TRpew [,-1])
```
## STUDY 2A PEW data analyses
### logistic regressions
```{r}
logit_vote_socdem <- glm(GroupVoteBIN ~ gender + age + hh_incomeCat + CS_eduCat,
data = TRpew, family = binomial(link="logit"))
summary(logit_vote_socdem)
fmsb::NagelkerkeR2(logit_vote_socdem)
logit_vote <- glm(GroupVoteBIN ~ DemFG_SL + SatWLvODem + gender + age + hh_incomeCat + CS_eduCat,
data = TRpew, family = binomial(link="logit"))
summary(logit_vote)
fmsb::NagelkerkeR2(logit_vote)
mar_vote <- margins(logit_vote)
mar_vote_sum <- summary(mar_vote)
mar_vote_sum %>% kable(digits = 3)
```
#### regression tables
```{r}
tab_model(logit_vote_socdem, logit_vote,
show.se = T,
transform = NULL,
pred.labels = c("Intercept",
"Gender","Age",
"Medium-level Income", "High-level Income",
"Some primary school completed",
"Some high school completed",
"University degree & higher",
"Preferring a strong leader (Authoritarianism)",
"Being not too satisfied with the way democracy is working in TR",
"Being somewhat satisfied with the way democracy is working in TR",
"Being very satisfied with the way democracy is working in TR"),
dv.labels = c("Socio-demographics model","... [full] model"),
string.pred = "Coefficient",
string.ci = "CI (95%)",
string.p = "P-Value")
```
#### marginal effects plots
```{r}
fit_pred_dem <- ggeffect(logit_vote, terms = "SatWLvODem")
p_demS <- plot(fit_pred_dem) + labs(title = "Probability of feeling closer to AKP by Satisfaction with Democracy",
tag = "A",
x = "Level of Satisfaction with Democracy", y = "")
fit_pred_auth <- ggeffect(logit_vote, terms = "DemFG_SL")
p_auth <- plot(fit_pred_auth) + labs(title = "Probability of feeling closer to AKP by Authoritarianism",
tag = "B",
x = "Democracy vs. Strong Leader", y = "")
grid.arrange(p_demS, p_auth, ncol = 2, nrow=1,
left="Predicted % (Feeling closer to AKP)",
bottom="Note: All other variables held at their observed values
Source: Pew Global Attitudes 2015") # plot side by side
```
```{r}
rm(list=ls(all.names = T)) # clear memory
```
# STUDY 2B: Comparative Election Studies 4 2015
## prep the variables of interest
CES4 is publicly available @ https://cses.org/data-download/module-4-2011-2016/
```{r}
TRces <- read_stata ("cses4.dta") %>%
as_tibble() %>%
select(D1006_NAM,
D3006_LH_PL,
D3017,
D2002, D2001_Y, D2003, D2020,
D2026,
D2031,
D3014) %>%
filter(D1006_NAM %in% c("Turkey")) %>%
rename(SatWLvODem = D3017,
gender=D2002, age=D2001_Y, edu=D2003,
hh_income=D2020,
Religion=D2026,
UrbanRural=D2031,
PO_LR=D3014,
GroupVote=D3006_LH_PL) %>%
mutate(GroupVoteBIN = GroupVote)
colnames(TRces)
describe(TRces[,-1])
```
### define missing values
```{r}
TRces <- TRces %>%
replace_with_na(replace = list(GroupVote = c(92,97,98,99),
GroupVoteBIN = c(92,97,98,99),
PO_LR = c(95,97,98,99),
SatWLvODem = c(9),
age = c(9997,9998),
edu = c(97),
hh_income = c(9),
Religion = c(9997)))
```
### voted for AKP vs. non-AKP (any other party)
```{r}
TRces$GroupVoteBIN <- car::recode(TRces$GroupVoteBIN,"1=1; 2=0; 3=0; 4=0; 5=0; 90=0")
TRces$GroupVoteBIN <- factor(TRces$GroupVoteBIN, levels=c(0,1), labels = c("nonAKPv", "AKPv"))
tabyl(TRces$GroupVoteBIN)
# 1=AKP 2=HDP 3=CHP 4=MHP 5=SP 90=other
TRces$GroupVote <- car::recode(TRces$GroupVote,"1=1; 4=2; 3=3; 2=4;
5=100; 90=100")
TRces$GroupVote <- factor(TRces$GroupVote, levels = c(1,2,3,4,100), labels = c("AKP","MHP","CHP","HDP","Other"))
tabyl(TRces$GroupVote) # AKP 0.48 MHP 0.11 CHP 0.25 HDP 0.13 other 0.04
```
### socio-demographics
```{r}
TRces$age <- 2015-TRces$age
TRces$ageI <- cut(TRces$age, breaks=c(20,30,40,50,60, 70, 80, Inf),
labels=c("18-29", "30-39", "40-49", "50-59", "60-69", "70-79", ">80"))
round(prop.table(table(TRces$ageI)),2)
TRces$gender <- factor(TRces$gender, labels = c("Male", "Female"))
round(prop.table(table(TRces$gender)),2) # M 0.51 F 0.49
round(prop.table(table(TRces$edu)),2)
TRces$edu <- factor(TRces$edu, levels = c(2,3,4,7,8,9,96),
labels = c("PRIMARY","LOWER SECONDARY","UPPER SECONDARY",
"BACHELOR OR EQUIVAL","MASTER OR EQUIVALEN","DOCTORAL OR EQUIVAL",
"NO EDUCATION"))
TRces$edu <- fct_collapse(TRces$edu,
NoFormal = c("NO EDUCATION"),
somePrimarySchool = c("PRIMARY"),
someHighSchool = c("LOWER SECONDARY","UPPER SECONDARY"),
UniHigher = c("BACHELOR OR EQUIVAL","MASTER OR EQUIVALEN","DOCTORAL OR EQUIVAL"))
round(prop.table(table(TRces$edu)),2)
tabyl(TRces$edu)
TRces$edu <- relevel(TRces$edu, "NoFormal")
TRces$hh_income <- cut(TRces$hh_income,
breaks=c(-Inf, 2, 4, Inf),
labels=c("low","medium","high")) # define household income categories
round(prop.table(table(TRces$hh_income)),2) # 0.18 low 0.76 medium 0.06 high //roughly correspond to 1000 3000 divide
tabyl(TRces$hh_income)
TRces$Religion <- factor(TRces$Religion, levels = c(3000,8300),
labels = c("Muslim","None"))
tabyl(TRces$Religion)
TRces$UrbanRural <- factor(TRces$UrbanRural, levels = c(1,2,3,4), labels = c("Rural area or village","Small or middle sized town",
"Suburbs of large town or city", "Large town or city"))
tabyl(TRces$UrbanRural)
```
### sample characteristics
```{r}
GE_bp <- TRces %>% select(gender) %>% drop_na() %>%
ggplot(., aes(x=gender)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Gender") +
coord_flip()
AG_bp <- TRces %>% select(ageI) %>% drop_na() %>%
ggplot(., aes(x=ageI)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Age") +
coord_flip()
REL_bp <- TRces %>% select(Religion) %>% drop_na() %>%
ggplot(., aes(x=Religion)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Religion") +
coord_flip()
EDU_bp <- TRces %>% dplyr::select(edu) %>% drop_na() %>%
ggplot(., aes(x=edu)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Education") +
coord_flip()
INC_bp <- TRces %>% dplyr::select(hh_income) %>% drop_na() %>%
ggplot(., aes(x=hh_income)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Household Income") +
coord_flip()
UR_bp <- TRces %>% dplyr::select(UrbanRural) %>% ggplot(., aes(x=UrbanRural)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Urban vs. Rural") +
coord_flip()
LR_bp <- TRces %>% dplyr::select(PO_LR) %>% drop_na() %>%
ggplot(., aes(x=as_factor(PO_LR))) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
scale_x_discrete(labels = c("Left","1","2","3","4","5","6","7","8","9","Right")) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black", angle=45) +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Left-Right")
VR_bp <- TRces %>% dplyr::select(GroupVote) %>% drop_na() %>%
ggplot(., aes(x=GroupVote)) +
geom_bar(aes(y = (..count..)),fill="grey", alpha=0.6) +
geom_text(aes(y = (..count..) ,label = ifelse((..count..)==0,"",scales::percent((..count..)/sum(..count..)))), stat="count",colour="black") +
theme_bw() +
xlab("") +
ylab("Frequency") +
labs(title="Vote Recall") +
coord_flip()
grid.arrange(GE_bp, AG_bp, REL_bp,
EDU_bp, INC_bp, UR_bp,
LR_bp,VR_bp,
nrow = 3, ncol = 3,
top = "Sample characteristics in Study 2b",
bottom = textGrob("Source: Comparative Election Studies 4 2015
\nhttps://cses.org/data-download/module-4-2011-2016/
\nData Collection: 2015",
gp = gpar(fontface = 3, fontsize = 10), just = "center"))
rm(list=setdiff(ls(), "TRces"))
```
### reverse code the following items
```{r}
round(prop.table(table(TRces$SatWLvODem)),2)
TRces$SatWLvODem <- car::recode(TRces$SatWLvODem, "1=4; 2=3; 4=2; 5=1")
TRces$SatWLvODem <- factor(TRces$SatWLvODem, labels = c("Not at all satisfied","Not too satisfied", "Somewhat satisfied", "Very satisfied"))
round(prop.table(table(TRces$SatWLvODem)),2)
tabyl(TRces$SatWLvODem)
```
### cross-tabulation // descriptive statistics for response categories
```{r}
TRces %>%
tabyl(SatWLvODem, GroupVoteBIN) %>%
adorn_totals(c("row", "col")) %>%
adorn_percentages("row") %>%
adorn_pct_formatting(rounding = "half up", digits = 2) %>%
adorn_ns() %>%
adorn_title("combined") %>%
kable()
describe(TRces [,-1])
```
## STUDY 2B CES data analyses
### logistic regressions