Bivariate Regression

1. Bivariate Regression Political Participation Political Contestation

2. Bivariate Regression Rule of Law Political Participation

3. Trivariate Regression Rule of Law Political Participation Political Contestation

4. Crosstab: Contestation and Participation Note: Variables are continuous, but grouped by quantiles for purposes of exposition.

5. Correlation: Contestation and Participation

6. Bivariate Regression: Contestation and Participation

7. Crosstab: Rule of Law and Participation Note: Variables are continuous, but grouped by quantiles for purposes of exposition.

8. Correlation: Rule of Law and Participation

9. Bivariate Regression: Participation and Rule of Law

10. Crosstab: Participation and Contestation controlling for Rule of Law Contestation (quantiles) Participation (quantiles)

11. Correlation: Participation and Contestation, Controlling for Rule of Law

12. Crosstab: Presidential Vote (2008) and Liberalism, controlling for Presidential Vote (2004) Rule of Law Participation

13. Correlation: Participation and Rule of Law, Controlling for Contestation

14. Correlations

15. Explaining Venn Diagrams Variance Participation

16. Explaining Venn Diagrams Variance Contestation

17. Explaining Venn Diagrams Variance Rule of Law

18. Putting Them Together

19. Plot showing positive covariance

20. Plot showing negative covariance

21. Plot showing no covariance

22. Pearson r = .83 Rule of Law Contestation Pearson r = .90 Participation Contestation Participation Pearson r = .91 Rule of Law

23. Participation Rule of Law Contestation

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

25. Appendix: Syntax 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)