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 Pepsi Coke Diet Regular 2 X 2 Factorial Design
Diet Pepsi
Diet
Diet Coke
Regular Pepsi
Regular Coke
Regular
2 X 2 Factorial Design

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
Adult
Adolescent
Male
Gender
Female

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

13. Factorial Design: Influences on Ratings of Attractiveness
Relationship
Single
Dating
Married
Male
Gender
Female

14. Factorial Design: Influences on Ratings of Attractiveness
Relationship
2 X 3 Factorial Design
Single
Dating
Married
Male
Gender
Female

15. Factorial Design: Influences on Ratings of Attractiveness
Relationship
Dating
Married
Single
Age
Adults
Adolescents
Male
Gender
Female

16. Factorial Design: Influences on Ratings of Attractiveness
Relationship
Dating
Married
Single
Age
Adults
Adolescents
Male
Gender
Female
2 X 2 X 3 Factorial Design

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

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

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

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

27. Factorial Design: Influences on Ratings of Attractiveness
Males
Females
Adolescents
Adults
Male
Female
Age

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

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

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

31. Factorial Design: Influences on Ratings of Attractiveness
Males
Females
Adolescents
Adults
Male
Female
Age

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

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

34. Factorial Design: Influences on Ratings of Attractiveness
Age
Adult
Adolescent
Average score of 8
Average score of 10
Male 9
Gender
Average score of 6
Average score of 8
Female 7
There are main effects, but no interaction in this case

35. Factorial Design: Influences on Ratings of Attractiveness NO Interaction
Males
Females
Adolescents
Adults
Male
Female
Age

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):
Relationship Status
Single
Cohab
Married 10 8 9
30s
Age 9 7
40s 8

39. Factorial Design: Another Example
Age
30s
40s
Single
Cohab
Married
Relationship Status 8 9 10 7 9 8
8.5
8.5
8.5

40. Factorial Design: Another Example
Age
30s
40s
Single
Cohab
Married
Relationship Status 8 9 10 7 9 8
8.5
8.5
8.5

41. Factorial Design: Another Example
Age
30s
40s
Single
Cohab
Married
Relationship Status 8 9 10 7 9 8
8.5
8.5
8.5

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
single
cohab
married

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

52. No Class Next Week!