16 Exercise 6: Test your knowledge

In this Exercise, we will work with the disaster data set. It’s a data set of a communication science experiment that was conducted by Chapman and Lickel in 2016 and comes pre-installed with processR. The data set contains 211 observations of 5 variables and investigates how framing of climate change disasters affects justifications for giving or withholding aid to disaster victims. There are three variables of interest to us in this data set:

  1. frame: article was framed as a naturally caused disaster (0) or article was framed as a climate change caused disaster (1)

  2. skeptic: climate change skepticism, higher scores indicate greater skepticism about the reality of climate change (scale: 1-9)

  3. justify: strength of a participant’s justifications for withholding aid, higher scores indicate a stronger sense that helping out the victims is not justified (scale: 1-7)

Let’s load processR and save the disaster data to a source object called data:

# installing/loading the package:
if(!require(processR)) {
  install.packages("processR"); 
  require(processR)
} #load / install+load processR

data <- processR::disaster

Framing theory suggests that the way a topic is presented to the audience (referred to as “frame”) affects how individuals interpret that information and thus evaluate the topic.

Research Question: Does framing a disaster as caused by climate change, rather than leaving the cause undetermined, affect people’s justification for not helping, and does this framing effect depend on people’s skepticism about climate change?

16.1 Task 1

What variables are X, Y, and W in this scenario? Write it down.

16.2 Task 2

Investigate the relationship between X and Y with a plot. Next, make a visual check if this effect depends on different levels of W. Finally, evaluate the figures.

16.3 Task 3

Run a linear regression model that includes an interaction term of X and M. Note down the regression equation and interpret the coefficients.

16.4 Task 4

Mean-center the climate change skepticism variable and run the model again. How does the interpretation of b1 change?

16.5 Task 5

Probe the interaction.

16.6 Task 6

Repeat the analysis using processR (i.e., the process function). Look only at the processR output and note down all the coefficients.