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Participation

Note: Variables are continuous, but grouped by quantiles for

purposes of exposition.

Participation

Bivariate Regression: Contestation and

Participation

Crosstab: Rule of Law and Participation

Note: Variables are continuous, but grouped by quantiles for

purposes of exposition.

Crosstab: Participation and Contestation controlling for Rule of Law

Contestation (quantiles)

Participation

(quantiles)

Crosstab: Presidential Vote (2008) and Liberalism, controlling for Presidential Vote (2004)

Rule of Law

Participation

Rule of

Law

Contestation

Pearson r = .90

Participation

Contestation

Participation

Pearson r = .91

Rule of

Law

Multivariate Regression: Participation, controlling for Contestation and Rule of Law

R Code:

library(foreign)

#Choose the file `class_qog.dta\'

myFile <- file.choose()

dat <- read.dta(myFile,header=TRUE)

attach(dat)

q <- quantile(dat$cam_contest, c(0, .25, .50, .75, 1), na.rm=T)

contestq <- cut(dat$cam_contest, q, include.lowest=T)

levels(contestq) <- c("1st", "2nd", "3rd", "4th")

q <- quantile(dat$bti_pp, c(0, .25, .50, .75, 1), na.rm=T)

dat$ppq <- cut(dat$bti_pp, q, include.lowest=T)

levels(dat$ppq) <- c("1st", "2nd", "3rd", "4th")

q <- quantile(dat$bti_rol, c(0, .25, .50, .75, 1), na.rm=T)

dat$rolq <- cut(dat$bti_rol, q, include.lowest=T)

levels(dat$rolq) <- c("1st", "2nd", "3rd", "4th")

attach(dat)

#Making the crosstabs table

.Table <- xtabs(~ppq+contestq, data=dat)

.Table

#Correlation Tables

cor(dat[,c("bti_pp","cam_contest")], use="complete.obs")

cor(dat[,c("bti_pp","cam_contest")], use="complete.obs", method="spearman")

mod<-lm(bti_pp~cam_contest, data=dat)

#I did not standardize the coefficients, but that can be done like this:

sdev.cont<-sd(cam_contest, na.rm=T)

sdev.part<-sd(bti_pp, na.rm=T)

std.b.cont <- mod$coefficients[2] * (sdev.cont / sdev.part)

scatterplot(fh_polity2~cam_contest | rolq, reg.line=lm, smooth=TRUE, ylab="Democracy (Polity) Score", xlab="Level of Contestation", main="Contestation and Democracy \n (by rule of law quantiles)", spread=TRUE, boxplots=\'xy\', span=0.5, by.groups=TRUE, data=dat)

#Participation and Rule of Law

#Making the crosstabs table

.Table <- xtabs(~ppq+rolq, data=dat)

.Table

#Correlation Tables

cor(dat[,c("bti_pp","bti_rol")], use="complete.obs")

cor(dat[,c("bti_pp","bti_rol")], use="complete.obs", method="spearman")

.Table1 <- xtabs(~ppq+rolq+contestq, data=dat)

.Table1

.Table2 <- xtabs(~ppq+contestq+rolq, data=dat)

.Table2

cor(dat[,c("bti_pp","bti_rol", "cam_contest")], use="complete.obs")

cor(dat[,c("bti_pp","bti_rol", "cam_contest")], use="complete.obs", method="spearman")

xyplot(bti_pp~cam_contest | rolq, pch=16, ylab="Participation", xlab="Contestation", main="Participation and Contestation \n by Rule of Law Quantiles",

auto.key=list(border=TRUE),

par.settings = simpleTheme(pch=16), scales=list(x=list(relation=\'same\'),

y=list(relation=\'same\')),

data=dat)

xyplot(bti_pp ~ bti_rol | contestq, pch=16, ylab="Participation", xlab="Rule of Law", main="Participation and Rule of Law \n by Contestation Quantiles",

auto.key=list(border=TRUE),

par.settings = simpleTheme(pch=16), scales=list(x=list(relation=\'same\'),

y=list(relation=\'same\')),

data=dat)

#Putting them together

#Plot 1

par(mfrow=c(1,2))

scatterplot(bti_pp~cam_contest | rolq, reg.line=lm, smooth=TRUE, ylab="Participation", xlab="Level of Contestation", main="Participation and Contesation \n (by rule of law quantiles)", spread=TRUE, boxplots=\'xy\', span=0.5, by.groups=TRUE, data=dat)

#Plot 2

scatterplot(bti_pp~bti_rol | contestq, reg.line=lm, smooth=TRUE, ylab="Participation", xlab="Rule of Law", main="Participation and Rule of Law \n (by Contestation quantiles)", spread=TRUE, boxplots=\'xy\', span=0.5, by.groups=TRUE, data=dat)

#Positive Relationship

[code omitted - uses a different dataset]

#Negative Relationship

scatterplot(wdi_mort~log(gle_gdp) , reg.line=lm, smooth=TRUE, ylab="Infant Mortality", xlab="GDP Per Capita(logged)", main="Infant Mortality and GDP", col="black", spread=TRUE, boxplots=\'xy\', span=0.5, by.groups=TRUE, data=dat)

#Insignificant Relationship

[code omitted - uses different dataset]

#Linear Regression

summary(lm(bti_pp~cam_contest+bti_rol))

Stata Code:

tabulate ppq contestq2, cchi2 chi2 column

cor bti_pp cam_contest cam_contest

reg bti_pp cam_contest

twoway (lfitci bti_pp cam_contest) (scatter bti_pp cam_contest)

tabulate ppq rolq, cchi2 chi2 column

cor bti_pp bti_rol cam_contest

reg bti_pp bti_rol

twoway (lfitci bti_pp cam_contest) (scatter Bti_pp cam_contest)

by rolq2, sort : tabulate ppq contestq2, cchi2 chi2 column

by contestq, sort: tabulate ppq rolq, cchi2 chi2 column

cor bti_pp cam_contest bti_rol

twoway (lfitci bti_pp cam_contest) (scatter bti_pp cam_contest), by(rolq2)

twoway (lfitci bti_pp bti_rol) (scatter bti_pp bti_rol), by(contestq)

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