A researcher is interested in the relationship between attitudes on corruption and generalized trust in the United States. Using data from the 2017 wave of the World Values Survey, she estimates a linear regression model in which the dependent variable is an indicator of agreement with the statement:
“Tell me your views on corruption—when people pay a bribe, give a gift or do a favor to other people in order to get the things they need done or the services they need. How would you place your views on corruption in your country on a 10-point scale where ‘1’ means ‘there is no corruption in my country’ and ‘10’ means ‘there is abundant corruption in my country’. If your views are somewhat mixed, choose the appropriate number in between.”
An indicator of generalized trust is the main independent variable, which is a response to the following yes or no question, with ‘1’ indicating ‘most people can be trusted’:
“Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?”
The researcher controls for:
• Political ideology: 1 = left; 10 = right
• Political interest: 1 = Not at all interested; 4 = Very interested
• Gender: 0 = Male; 1 = Female
• Age in years
• Highest education attainment as three dummy variables: secondary education, College, and postgraduate.
Summary statistics for the variables included in the model can be found in Table 1.
The researcher runs the regression model in R and obtains the output which is summarised in Table 2. A selection of postestimation diagnostics are provided in Table 3 and the two figures.
Interpret this statistical analysis. Discuss substantive effects, statistical significance, model specification, model fit and, where possible, regression assumptions. Provide suggestions as to how the model might be improved. Maximum 800 words.
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