1. Chapter 3 The Ethics and Politics of Social Research

2. Ethical Issues in Social Research 1.Voluntary participation 2.No harm to participants 3.Anonymity and confidentiality

3. Ethical Issues in Social Research 4.Deception must be justified by compelling scientific concerns. 5.Researchers must be honest about their findings and research.

4. Ethical Issues in Social Research Institutional Review Boards Review research proposals involving humans so they can guarantee the rights and interests are protected. Professional Codes of Ethics Most professional associations have formal codes of conduct that describe acceptable and unacceptable professional behavior.

5. Ethical Controversy: Laud Humphreys (1970) Study of homosexual behavior in public restrooms. Lied to participants by telling them he was a voyeur-participant. Traced participants to their home and interviewed them under false pretenses.

6. Ethical Controversy: Stanley Milgram (1960) Study of human obedience. Subjects had role of "teacher" and administered a shock to "pupils". Pupils were actually part of the experiment.

7. Politics in Perspective 1.Science is not untouched by politics. 2.Science proceeds in the midst of political controversy and hostility. Examples: Global Warming; Autism research

8. Extra Credit Collaborative Institutional Training Initiative www.citiprogram.org Register your information (free for faculty, staff and students of member institutions) Take the modules for the Social Sciences Take the quizzes for each module (must score a 70% to pass each module). Print CITI Certificate and hand in at the beginning of class, December 8 th Replace 1 homework grade or 5% on 1 exam

9. MLB spending and performance example (Hoover & Donovan 2001): Y [team finish] =  +  X [spending] Expressing the model in words: values of the Y variable (team finish: 1 st place, 2 nd place, etc.) are a function of some constant (  ), plus some amount of the X variable (spending). How much change in the Y variable (team finish) is associated with a change in the X variable (spending). The answer lies in β (beta), a.k.a the regression coefficient. In the baseball example, it would be the amount of improvement in team finish associated with an additional $1 million in spending on players’ salaries.

10. Hoover and Donovan using 1999 MLB season data and a bivariate regression found: Team finish = 4.4 – 0.03 x spending (in $millions) Interpretation: The beta (a.k.a the slope) suggests the relationship between spending and team finish was –0.03. Or, for each million dollars that a team spends, there is only a 3 percent change in division position. These results show that a team spending $80 million on players will finish close to second place. We can also show that any given team would have to spend almost $34 million more to improve its team finish by one position (-0.03 x $34 million = 1.02). The correlation was -0.39 which means that spending explains only 15 percent of variation in the team’s finish (r-squared =.15 = - 0.39 x -0.39).

11. Another Baseball Example Testing Causality Between Team Performance and Payroll : The Cases of Major League Baseball and English Soccer By Stephen Hall, Stefan Szymanski and Andrew S. Zimbalist Journal of Sports Economics 2002

14. Multiple Regression Multiple regression contains a single dependent variable and two or more independent variables. Multiple regression is particularly appropriate when the causes (independent variables) are inter- correlated, which again is usually the case.

15. Multivariate Regression is a powerful tool to examine how multiple factors (independent variables) influence a dependent variable. It differs from bivariate regression in that it can identify the independent effect a variable has on a dependent variable by holding all other variables constant? What other variables would we include in the baseball model to predict winning %?

16. Y X1 X2

17. X1 Y X2 c

18. In figure 1 the fact that X1 and X2 do not overlap means that they are not correlated, but each is correlated with Y. This is great and means we don’t need sophisticated analysis, just two separate bivariate regressions. In figure 2, X1 and X2 are correlated. The area C is created by the correlation between X1 and X2; c represents the proportion of the variance in Y that is shared jointly with X1 and X2. How do we deal with C? We can’t count it twice or we will get a variation that is greater than 100%. Multivariate Regression