The model

We are now ready to conduct our analysis. We will use the lm() function.

library(lme4)
model.1<-lm(immi.jobs~ born.country+respect.inst+country.ancestry+speak.lang+share.cultr+left_right+education+gender+age,data=sub.sample)
summary(model.1)
## 
## Call:
## lm(formula = immi.jobs ~ born.country + respect.inst + country.ancestry + 
##     speak.lang + share.cultr + left_right + education + gender + 
##     age, data = sub.sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.1341 -1.6810 -0.1593  1.6917  7.8835 
## 
## Coefficients:
##                    Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.2363291  0.4307600   2.870 0.004157 ** 
## born.country      0.5846284  0.0780512   7.490 1.13e-13 ***
## respect.inst     -0.0589700  0.1056487  -0.558 0.576805    
## country.ancestry  0.4367903  0.0854104   5.114 3.53e-07 ***
## speak.lang        0.3575283  0.1150266   3.108 0.001915 ** 
## share.cultr       0.3870998  0.1052285   3.679 0.000242 ***
## left_right        0.0354641  0.0313352   1.132 0.257901    
## education        -0.0017546  0.0002726  -6.436 1.62e-10 ***
## gender           -0.3156420  0.1180030  -2.675 0.007551 ** 
## age               0.0014324  0.0036699   0.390 0.696364    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.355 on 1614 degrees of freedom
##   (164 observations deleted due to missingness)
## Multiple R-squared:  0.2354, Adjusted R-squared:  0.2311 
## F-statistic: 55.21 on 9 and 1614 DF,  p-value: < 2.2e-16
library(dotwhisker)
dwplot(list(model.1), vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2)) %>% # plot line at zero _behind_ coefs
    relabel_predictors(c(immi.jobs = "Immigrants take jobs",                       
                         born.country = "Born in country", 
                         respect.inst = "Respect institutions", 
                         country.ancestry = "Country ancestry", 
                         speak.lang = "Speak language", 
                         share.cultr = "Share culture",
                         left_right="Left-Right",
                         education="Education",
                         gender="Gender",
                         age="Age")) +
     theme_classic() + xlab("Coefficient Estimate") + ylab("") +
     geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
     ggtitle("Predicting anti-immigration attitudes") +
     theme(plot.title = element_text(face="bold"),
           legend.position = c(0.01, 0.03),
           legend.justification = c(0, 0), 
           legend.background = element_rect(colour="grey80"),
           legend.title = element_blank())

From our analysis we can see that three of our variables are not statistically significant, namely age, respect towards institutions, and left-right. Let’s construct another model but this time we will exclude the three variables that are not statistically significant.

model.2<-lm(immi.jobs~ born.country+country.ancestry+speak.lang+share.cultr+education+gender,data=sub.sample)
summary(model.2)
## 
## Call:
## lm(formula = immi.jobs ~ born.country + country.ancestry + speak.lang + 
##     share.cultr + education + gender, data = sub.sample)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -6.2424 -1.7014 -0.1612  1.7130  7.7064 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       1.274658   0.356722   3.573 0.000362 ***
## born.country      0.581489   0.075558   7.696 2.34e-14 ***
## country.ancestry  0.502525   0.082420   6.097 1.33e-09 ***
## speak.lang        0.350976   0.109172   3.215 0.001329 ** 
## share.cultr       0.381239   0.098778   3.860 0.000118 ***
## education        -0.001789   0.000258  -6.932 5.80e-12 ***
## gender           -0.278855   0.114744  -2.430 0.015189 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.377 on 1742 degrees of freedom
##   (39 observations deleted due to missingness)
## Multiple R-squared:  0.2364, Adjusted R-squared:  0.2338 
## F-statistic: 89.88 on 6 and 1742 DF,  p-value: < 2.2e-16

We can now plot both models and compare the results.

dwplot(list(model.1, model.2), vline = geom_vline(xintercept = 0, colour = "grey60", linetype = 2)) %>% # plot line at zero _behind_ coefs
    relabel_predictors(c(immi.jobs = "Immigrants take jobs",                       
                         born.country = "Born in country", 
                         respect.inst = "Respect institutions", 
                         country.ancestry = "Country ancestry", 
                         speak.lang = "Speak language", 
                         share.cultr = "Share culture",
                         left_right="Left-Right",
                         education="Education",
                         gender="Gender",
                         age="Age")) +
     theme_classic() + xlab("Coefficient Estimate") + ylab("") +
     geom_vline(xintercept = 0, colour = "grey60", linetype = 2) +
     ggtitle("Predicting anti-immigration attitudes") +
     theme(plot.title = element_text(face="bold"),
           legend.position = c(0.01, 0.03),
           legend.justification = c(0, 0), 
           legend.background = element_rect(colour="grey80"),
           legend.title = element_blank())