2. Practice Odometers measure automobile mileage. Suppose 12 cars drove exactly 10 miles and the following mileage figures were recorded. Determine if, on average, the odometers were accurate (Alpha =.05). 9.8, 10.1, 10.3, 10.2, 9.9, 10.4, 10.0, 9.9, 10.3, 10.0, 10.1, 10.2

3. One-sample t-test H1 = Mean not equal to 10 H0 = Mean = 10 t critical (11) = 2.201 t obs = 1.86 The odometers did not record a siginficantly different distance than what was driven.

5. Study You are interested in if people like Pepsi and Coke differently. To examine this you give: 20 people regular Pepsi 20 people regular Coke You then ask them to rate how much they liked the soda (1 = do not like at all, 5 = like a lot).

6. What kind of statistic could you use? Two-sample t-test An ANOVA

7. But what if.... In addition to brand type you were also interested in examining diet vs. regular soda. To examine this you give: 20 people regular Pepsi 20 people regular Coke 20 people diet Pepsi 20 people diet Coke You then ask them to rate how much they liked the soda (1 = do not like at all, 5 = like a lot).

8. Factorial Design Research design that involves 2 or more Independent Variables –Involves all combinations of at least 2 values of 2 or more IVs

9. Factorial Design Coke Regular Pepsi Diet 2 X 2 Factorial Design Diet Pepsi Diet Coke Regular CokeRegular Pepsi

10. Factorial Design: Influences on Ratings of Attractiveness Does individuals’ gender or age influence their ratings of a woman’s attractiveness?

11. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult

12. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 2 X 2 Factorial Design

13. Factorial Design: Influences on Ratings of Attractiveness Ethnicity Gender Male Female Euro-AmericanAfrican AmericanMexican American

14. Factorial Design: Influences on Ratings of Attractiveness Ethnicity Gender Male Female Euro-AmericanAfrican American 2 X 3 Factorial Design Mexican American

15. Factorial Design: Influences on Ratings of Attractiveness Ethnicity Gender Male Female Euro-AmerAfrican AmerMexican Amer Age Adults Adolescents

16. Factorial Design: Influences on Ratings of Attractiveness Ethnicity Gender Male Female Euro-AmerAfrican Amer 2 X 2 X 3 Factorial Design Mexican Amer Age Adults Adolescents

17. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 2 X 2 Factorial Design

18. Factorial Design: Influences on Ratings of Attractiveness Rate the attractiveness of the woman in this picture on a scale from 1-10 (10 is most attractive)

19. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 2 X 2 Factorial Design Average score of 8 Average score of 10 Average score of 9 Average score of 4

20. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 8.5 7 Average score of 8 Average score of 10 Average score of 9 Average score of 4 9 6.5

21. Factorial Design: Main Effects Main effects are the effects of one independent variable in an experiment (averaged over all levels of another independent variable)

22. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 8.5 7 Average score of 8 Average score of 10 Average score of 9 Average score of 4 9 6.5

23. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 8.5 7 Average score of 8 Average score of 10 Average score of 9 Average score of 4 9 6.5

24. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 8.5 7 Average score of 8 Average score of 10 Average score of 9 Average score of 4 9 6.5

25. Factorial Design: Interactions When the effect of one independent variable depends on the level of another independent variable

26. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 8.5 7 Average score of 8 Average score of 10 Average score of 9 Average score of 4 9 6.5

27. Factorial Design: Influences on Ratings of Attractiveness MaleFemale Males Females Age AdolescentsAdults

28. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 2 X 2 Factorial Design Average score of 8 Average score of 10 Average score of 10 Average score of 8

29. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 9 Average score of 8 Average score of 10 Average score of 10 Average score of 8 9 9

30. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 9 Average score of 8 Average score of 10 Average score of 10 Average score of 8 9 9

31. Factorial Design: Influences on Ratings of Attractiveness MaleFemale Males Females Age AdolescentsAdults

32. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 2 X 2 Factorial Design Average score of 8 Average score of 10 Average score of 6 Average score of 8

33. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 7 9 Average score of 8 Average score of 10 Average score of 6 Average score of 8 9 7

34. Factorial Design: Influences on Ratings of Attractiveness Age Gender Male Female Adolescent Adult 7 9 Average score of 8 Average score of 10 Average score of 6 Average score of 8 9 7

35. Factorial Design: Influences on Ratings of Attractiveness NO Interaction MaleFemale Males Females Age AdolescentsAdults

36. Factorial Design: Another Example A researcher is interested in studying the effects of relationship status (single, cohabitating, married) and age (30s or 40s) on individuals’ ratings of satisfaction with life

37. Factorial Design: Another Example A researcher is interested in studying the effects of relationship status (single, cohabitating, married) and age (30s or 40s) on individuals’ ratings of satisfaction with life –What is the Dependent Variable? –What are the Independent Variables? –What kind of a design is this?

38. Factorial Design: Another Example A researcher is interested in studying the effects of relationship status (single, cohabitating, married) and age (30s or 40s) on individuals’ ratings of satisfaction with life This is the data that is collected (average scores per group with scores ranging from 1 –10, most satisfied): Age 30s 40s SingleCohabMarried Relationship Status 89 10 9 8 7

39. Factorial Design: Another Example Age 30s 40s SingleCohabMarried Relationship Status 89 10 9 8 7 8.5 9 8

40. Factorial Design: Another Example Age 30s 40s SingleCohabMarried Relationship Status 89 10 9 8 7 8.5 9 8

41. Factorial Design: Another Example Age 30s 40s SingleCohabMarried Relationship Status 89 10 9 8 7 8.5 9 8

42. Factorial Design: Another Example A researcher is interested in studying the effects of marital status (single, cohabitating, married) and age (30s or 40s) on individuals’ ratings of satisfaction with life This is the data that is collected (average scores per group with scores ranging from 1 –10, most satisfied): 30s 40s singlecohabmarried

43. Practice 2 x 2 Factorial Determine if 1) there is a main effect of A 2) there is a main effect of B 3) if there is an interaction between AB

44. Practice A: NO B: NO AB: NO

45. Practice A: YES B: NO AB: NO

46. Practice A: NO B: YES AB: NO

47. Practice A: YES B: YES AB: NO

48. Practice A: YES B: YES AB: YES

49. Practice A: YES B: NO AB: YES

50. Practice A: NO B: YES AB: YES

51. Practice A: NO B: NO AB: YES