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

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.

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).

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

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).

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

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

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

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

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

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

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

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

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

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

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)

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

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 8.5 7

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

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 8.5 7

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 8.5 7

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 8.5 7

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

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 8.5 7

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

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

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 9 9

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 9 9

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

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

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 7 9

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 7 9

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

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

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?

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

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

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

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

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

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

Practice A: NO B: NO AB: NO

Practice A: YES B: NO AB: NO

Practice A: NO B: YES AB: NO

Practice A: YES B: YES AB: NO

Practice A: YES B: YES AB: YES

Practice A: YES B: NO AB: YES

Practice A: NO B: YES AB: YES

Practice A: NO B: NO AB: YES

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