6.3 At-Home Exercises

This week, we’ll take another look at the Kestilä (2006) results. During this practical, you will conduct an SEM to replicate the regression analysis of the Finnish data that you conducted in the Week 4 In-Class Exercises.

6.3.1

Load the Finnish subsample of ESS data.

  • The relevant data are contained in the [ess_finland.rds][fin_data] file.
    • These are the processed Finnish subsample data from the Week 4 exercises.

Note: Unless otherwise noted, all the following analyses use these data.

Click to show code 
ess_fin <- readRDS("ess_finland.rds")

We need to do a little data processing before we can fit the regression model. At the moment, lavaan will not automatically convert a factor variable into dummy codes. So, we need to create explicit dummy codes for the two factors we’ll use as predictors in our regression analysis: sex and political orientation.

6.3.2

Convert the sex and political interest factors into dummy codes.

Click to show code 
library(dplyr)

## Create a dummy codes by broadcasting a logical test on the factor levels:
ess_fin <- mutate(ess_fin,
                  female = ifelse(sex == "Female", 1, 0),
                  hi_pol_interest = ifelse(polintr_bin == "High Interest", 1, 0)
                  )

## Check the results:
with(ess_fin, table(dummy = female, factor = sex))
##      factor
## dummy Male Female
##     0  960      0
##     1    0   1040
with(ess_fin, table(dummy = hi_pol_interest, factor = polintr_bin))
##      factor
## dummy Low Interest High Interest
##     0         1070             0
##     1            0           929
Click for explanation

In R, we have several ways of converting a factor into an appropriate set of dummy codes.

  • We could use the dplyr::recode() function as we did last week.
  • We can use the model.matrix() function to define a design matrix based on the inherent contrast attribute of the factor.
    • Missing data will cause problems here.
  • We can us as.numeric() to revert the factor to its underlying numeric representation {Male = 1, Female = 2} and use arithmetic to convert {1, 2} \(\rightarrow\) {0, 1}.

When our factor only has two levels, though, the ifelse() function is the simplest way.

We are now ready to estimate our latent regression model. Specifically, we want to combine the three OLS regression models that you ran in 4.4.16 into a single SEM that we will estimate in lavaan.

The following path diagram shows the intended theoretical model.