Question 1
In this dataset, we asked employees to provide responses to a series of scales at two time periods. In between these, we asked their supervisors to report some additional variables (which we won’t worry about for now). We are interested in predicting Time 2 Trust in Supervisor from Time 1 individual affect (Positive and Negative) and Time 1 leader behaviors (Received Consideration; Received Initiating Structure). We are also interested for controlling for supervisor and subordinate gender and age.

1. Create a correlation table that includes means and standard deviations for the variables mentioned above. Discuss the correlations of your dependent variable to the potential independent variables and controls. Explain what these correlations mean.
2. Describe your sample in terms of subordinate and supervisor age and gender. Use listwise deletion, assuming you might use all the variables described above (hint: this is easier if you do this from the correlation analysis, using options to set deletion type and to ask for descriptives.
3. From the previous two analyses, what variables might you be interested in utilizing as controls?
4. Run two bivariate regression models.
1. Explain what the Bs (regression coefficients) mean.
2. Compare the standardized betas to the bivariate correlations. If there are any differences, why is that the case?
3. Compare standardized betas to the R and R-square for the model. What does this tell us?

Question 2
We are still interested in predicting Intentions to Quit from individual affect (Positive and Negative) and leader behaviors (Abusive Supervision; Received Consideration; Received Initiating Structure) using the controls of supervisor and subordinate gender and age. This time we’ll run some more advanced analyses.
1. Go back to your correlation table. This time, discuss the correlations among the independent variables and controls. Any red flags pop up? What should you be looking for?
2. Run a multivariate regression model including the controls and leader behaviors.
1. Explain what the Bs (regression coefficients) mean.
2. Compare the standardized betas to the bivariate correlations. If there are any differences, why is that the case?
3. Compare standardized betas to the R and R-square for the model. What does this tell us now?
3. Run the model with and without controls. Are there any changes? Based on this, would you say the controls are necessary or not?
4. Using multivariate regression analyses, indicate leader behaviors explains variance in trust in supervisor above the control variables. How much additional variance does this explain? Which leadership behaviors are driving this effect?

Question 1 In this dataset, we asked employees to provide response...