Randomization - Covariate Balancing - Null Hypothesis - IT Assignment Help

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Questions
1. Randomization is a powerful tool for causal inference because it ensures experimental conditions are, on average, similar (i.e., ‘balanced’) on pre-treatment variables. This is referred to as ‘covariate balancing.’ Demonstrate whether or not treatment and control groups are balanced with respect to three covariates: age, income, and ideology. Based on your analysis, how satisfied are you that covariate balancing worked as intended in this study?

2. Create a variable called attitude_change, which measures the change in attitudes following the treatment. This treatment effect is a difference score computed by subtracting respondents’ attitudes toward\ immigration measured after the treatment from their attitudes toward immigration before the treatment. Based on the data, did the intervention succeed in changing attitudes? If so, by how much and in what direction? Briefly, discuss the substantive significance of the treatment effect.

3. Now that you have estimated the effect of the treatment, conduct a statistical test of the null hypothesis that the mean difference in attitude change between the treatment and control condition is 0. To conduct this test, you need to use the t.test() function (assume equal variances2 ). Report the t-statistic, along with its associated probability value (i.e., p-value). Briefly, explain your decision whether or not to accept the null hypothesis that the mean difference in the change in attitudes between treatment and control condition is 0.

4. Run a bivariate linear regression with treatment as your explanatory variable and the measure of the treatment effect you coded from question 2 as your dependent variable. What is the interpretation of this coefficient? How do the findings from this analysis compare to the findings from your independent samples t-test from question 3? Briefly, explain your findings.

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