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Chapter 11

# PSYB01H3 Chapter Notes - Chapter 11: Dependent And Independent Variables, Statistical Hypothesis Testing, Energy Drink

by OC1224795

Department

PsychologyCourse Code

PSYB01H3Professor

Anna NagyChapter

11This

**preview**shows pages 1-2. to view the full**8 pages of the document.** Factorial Design

I Lost My Phone Number, Can I Borrow Yours? Do

Pick-Up Lines Really Work?

Introduction to Our Research Question

● “ How do pick-up lines and a person’s scent influence relationship initiation?”

Picking a Design

● factorial design

○ the most frequently used experimental design for a study that has more

than one independent variable. + LEVELS

○ in this case → pick up line and scent

Anatomy of a Factorial Design

● the number of numbers indicates how many independent variables the design

includes.

○ e.g. In a 3 × 4 factorial design, there are two numbers, so there are

two independent variables.

○ e.g. In a 2 × 2 × 4 factorial design, there are three numbers, so there

are three independent variables.

● numbers themselves indicate how many levels or conditions each

independent variable has.

○ e.g. In a 3 × 4 factorial design, the first independent variable has 3

levels, while the second independent variable has 4 levels.

○ a 5 × 3 × 2 × 4 × 2 factorial design is theoretically possible, it is not

advisable

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● factorial designs are true experiments, where the researcher manipulates the

independent variables, all of which are between-subjects, such that each

participant only gets one level of each independent variable.

○ however, if one of the independent variables use a within-subject

format → factorial design becomes a mixed design

● hybrid design

○ any factorial design that has at least one quasi- independent variable.

○ a variable you cannot manipulate → gender

Benefits of Factorial Designs

● establish cause and effect!

○ allowing the researcher to run multiple experiments for the price of one

■ e.g. a 2 × 2 factorial design = two-group design

■ e.g. If we had a 2 × 3 factorial design, it would be like combining

a two-group design with a multi-group design

● it allows us to know interaction

○ when one independent variable influence on the dependent variable

changes depending on the level of the other independent variable(s).

○ e.g. If you were to consume too much of an energy drink, you would

become hyper alert, edgy, and jittery. But combined with alcohol, the

effect of the energy drink on these outcomes is different.

■ One independent variable (Red Bull) alters how the other

independent variable (alcohol) influences the dependent

variable (alertness).

Our Hypothesis

● “How do pick-up lines and a person’s scent influence relationship initiation?

○ to answer this question we need multiple independent variables =

multiple hypothesis

● main effect hypothesis

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○ focuses on one independent variable at a time, ignoring all other

independent variables.

○ e.g. In our study we have two potential main effect hypotheses, one

for pick-up approach and one for scent

● multiple independent variables can also mean we have interaction effect

hypothesis

○ prediction about how the levels of one independent variable will

combine with another independent variable to impact our dependent

variable in a way that extends beyond the sum of the two separate

main effects.

○ e.g. In our study we believe pick-up attempt and scent combined can

influence relationship receptivity, so we have a potential interaction

between our two independent variables.

● In factorial designs, you have as many potential main effect hypotheses as

you have independent variables, and as many interaction hypotheses as you

have combinations of independent variables.

○ main effect and interaction hypotheses are not dependent on one

another.

○ That is, you can have an interaction hypothesis without predicting

differences for either main effect.

○ Or you could have a main effect hypothesis for one independent

variable without having a main effect hypothesis for the other

independent variable.

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