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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.

Correlation: Rule of Law and Participation

Bivariate Regression: Participation and Rule of Law

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

Correlations Contestation

Putting Them Together Contestation

Plot showing positive covariance Contestation

Plot showing negative covariance Contestation

Plot showing no covariance Contestation

Pearson r = Contestation.83

Rule of

Law

Contestation

Pearson r = .90

Participation

Contestation

Participation

Pearson r = .91

Rule of

Law

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

Appendix: Syntax Contestation

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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