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under Problem Sets (Problem Set 4) is the assignment and an SPSS dataset (filename: Testosterone.sav). This is a slightly modified subset of an actual dataset examining the influence of leadership on Testosterone levels. The variables in this dataset are as follows:

Subject = ID number assigned to participant.

Condition = Participants were either told they would be put into a leadership position later in the study or a subordinate position.

Gender = Participants’ reported sex.

Pre_T = Testosterone measurements before participants were told what position they would take later in the study.

Post_T = Testosterone measurements after participants were told what position they would take later in the study.

Semester = What semester the participants were run in.

We predicted that Pre_T would account for a significant amount of variance in Post_T. However, we also predicted that Condition and Gender and would account for a significant amount of variance over and above Pre_T.

Use this prediction for the following questions. Again, be sure to provide syntax and output.

1. Run a hierarchical regression testing the hypothesis. What percentage of additional variance is accounted for by adding in both Condition and Gender? Is this additional variance accounted for significant? Report the p-value.

2. Report the squared partial correlations for both Condition and Gender. In words, what do these values represent?

3. Report the squared semipartial correlations for both Condition and Gender. In words, what do these values represent?

4. What are the zero-order correlations between each of your predictor variables and the dependent variable? Is their evidence of suppression? How do you know?

5. Run a multiple regression (not hierarchical) predicting Post T from Pre T, condition, and gender. Is there any evidence of non-linearity? Is there any evidence of heteroscedasticity? Is there evidence for non-normality? In each case, explain why you think there is or is not evidence for violations of each of these assumptions.

6. This study was conducted over two semesters. I’m worried that participants in the first semester may have been treated somewhat differently than students in the second semester. That is, there might be a violation of independence because semester might have an effect on my DV. Should I be worried? Why or why not?

7. Check for high leverage and high distance. Are there any subjects with particularly high leverage or distance (according to our rules of thumb)? If so, what are their subject numbers?

8. Are there any subjects who have an unusually high influence on the overall regression equation? If so, what are their subject numbers?

9. Are there any subjects who have an unusually high influence on the regression coefficient for condition? How about influence on the regression coefficient for gender? The regression coefficient for pre_T?  If so, what are their subject numbers?