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School

University of Victoria
Department

Psychology

Course Code

PSYC 201

Professor

Stephen Lindsay

Lecture

19

PSYC 201 Dr.Lindsay Lecture 19: Factorial Designs

2x2 between subjects allows you to examine the differences between the four conditions

and the differences that they each hold

o Can characterize the results in various ways

i.e. the mean being represented for each group then to be compared to each

other, then added across the two (marginal mean) conditions

o Interested in several different questions

Was there a main effect of one variable?

Overall were there a different score for two conditions?

Was there an interaction between the conditions?

o Is the overall averages going to be different from each other?

Line graphs can show interaction and main effects more than bar graphs

Need to average the subgroups of each conditions to determine if there is a main effect to

show whether or not there is a significant difference between the two conditions

A good strategy to determine if there is a difference; if the group sizes are about the same

then the mean of the groups would be roughly in the middle of the measures (see lecture

slides) [will be on the exam]

o Determine if the two points are distant form each other/if there is a significant

difference

You can tell at a glance whether or not there will be a tendency of an interaction, because

the lines are not parallel

Whether or not it is statistically significant depends on how large the groups are, how

much variability there are in the groups, and how big the interaction is.

The effect of one IV depends upon the other IV

Tendency of interaction is consistent of group congruency between two variables

Interaction effect: "cross-over" interaction, the direction of difference is important, there

can be a flip of the direction of the effect

Simple Effect Tests (post-hoc comparisons)

When a significant interaction occurs you want to perform a simple effect test as a way of

further exploring the data and looking at the effect of one IV separately at each level of

conditions

Includes doing two t-tests to see the difference between the conditions at each level

Orthogonally crossed= independently crossed

2x2 factorial design has three effects

Factorial Designs with Subject (Participant) Variables [non-manipulated]

Such as gender, ethnicity, age

Cannot change these variables but can create a factorial experiment using these variables

as the comparison groups

o i.e. boys vs. girls

Unlikely to happen by a fluke of chance

2x2 Within-subject Factorial Design

Same people tested at the time of each condition

Same individual tested in all four conditions

Would have advantages; not a large group of people, flukes of random assignment

couldn't account for your assignment because everyone has to go through each condition

Would try to counterbalance the order of each task for each individual

2x2 mixed-model factorial design

One of the IV are manipulated between subject and one IV is manipulated within subjects

2x3 Factorial Design

More than two levels of one or the other IV's

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