1. Chapter 9 Assessing Studies Based on Multiple Regression

2. 2 Assessing Studies Based on Multiple Regression (SW Chapter 9)

3. 3 Is there a systematic way to assess regression studies?

4. 4 A Framework for Assessing Statistical Studies: Internal and External Validity (SW Section 9.1)

5. 5 Threats to External Validity of Multiple Regression Studies

6. 6 Threats to Internal Validity of Multiple Regression Analysis (SW Section 9.2)

7. 7 1. Omitted variable bias

8. 8 Potential solutions to omitted variable bias

9. 9 2. Wrong functional form

10. 10 3. Errors-in-variables bias

11. 11 In general, measurement error in a regressor results in “errors-in-variables” bias.

12. 12

13. 13 “Errors-in-variables” bias, ctd.

14. 14 Potential solutions to errors-in-variables bias

15. 15 4. Sample selection bias

16. 16 Example #1: Mutual funds

17. 17 Sample selection bias induces correlation between a regressor and the error term.

18. 18 Example #2: returns to education

19. 19 Potential solutions to sample selection bias

20. 20 5. Simultaneous causality bias

21. 21 Simultaneous causality bias in equations

22. 22 Potential solutions to simultaneous causality bias

23. 23 Internal and External Validity When the Regression is Used for Forecasting (SW Section 9.3)

24. 24 Applying External and Internal Validity: Test Scores and Class Size (SW Section 9.4)

25. 25 Check of external validity

26. 26 The Massachusetts data: summary statistics

27. 27

28. 28

29. 29

30. 30 Predicted effects for a class size reduction of 2

31. 31

32. 32 Summary of Findings for Massachusetts

33. 33 Comparison of estimated class size effects: CA vs. MA

34. 34 Summary: Comparison of California and Massachusetts Regression Analyses

35. 35 Step back: what are the remaining threats to internal validity in the test score/class size example?

36. 36 Omitted variable bias, ctd.

37. 37

38. 38

39. 39 Additional example for class discussion

40. 40 America’s Most Wanted: Threats to Internal and External Validity