Factorial Design in Experiments

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Questions and Answers

What is a factorial design?

An experiment without any variables

An experiment with only one independent variable

An experiment with more than one independent variable (correct)

An experiment that only uses qualitative data

What does the notation 3 x 5 indicate in factorial designs?

There are 2 independent variables; one has 3 levels and the other has 5 levels.

In a 2 x 2 x 4 factorial design, the number of conditions is __________.

Levels and conditions can be used interchangeably in factorial designs.

False Signup and view all the answers

What inferential statistic is used in a single-factor multilevel design?

ANOVA Signup and view all the answers

What is a main effect in factorial designs?

The overall effect of one particular independent variable. Signup and view all the answers

What is true about interaction effects?

They occur when one IV depends on another IV. Signup and view all the answers

Why is a line graph preferred over a bar graph in factorial designs?

It clearly shows interaction effects. Signup and view all the answers

To determine if main and interaction effects are statistically significant, one would perform an __________.

ANOVA Signup and view all the answers

What is a factorial design?

An experiment with more than one independent variable (IV). Signup and view all the answers

What does a factorial notation like 2 x 2 x 4 indicate?

There are 3 IVs with levels of 2, 2, and 4 respectively. Signup and view all the answers

How many conditions are there in a 3 x 5 factorial design?

15 conditions. Signup and view all the answers

What inferential statistic is used in a single-factor multilevel design?

One-way analysis of variance (ANOVA). Signup and view all the answers

What is a main effect in factorial designs?

The overall effect of one particular independent variable (IV). Signup and view all the answers

Interaction effects occur when the effect of one IV does not depend on the level of another IV.

False Signup and view all the answers

What visualization method is preferred in factorial designs?

Line graph. Signup and view all the answers

What does it mean when lines are not parallel in a plotted line graph?

There is an interaction effect. Signup and view all the answers

What should you do to determine if main and interaction effects are statistically significant?

Perform an ANOVA. Signup and view all the answers

Study Notes

Factorial Design

Involves an experiment with multiple independent variables (IVs), typically 2 to 4
Each IV has multiple levels
Factorial notation describes the number of IVs and their levels
Example: 3 x 5 factorial design has two IVs, one with 3 levels and the other with 5 levels.
Number of conditions to be compared is equal to the product of the levels of each IV
Example: 2 x 4 factorial design has 8 conditions (2 x 4 = 8).
In factorial designs, "levels" and "conditions" refer to distinct concepts.

Analyzing Effects with Factorial Designs

Inferential statistics are used to analyze factorial designs
Main effects refer to the influence of one IV, independent of other IVs
Number of main effects equals the number of IVs
To determine a main effect, data from different levels of other IVs is collapsed (combined).
Interaction effects occur when the effect of one IV depends on the level of another IV
Example: Grades in Science might be higher than Humanities in a lab setting, but lower in a lecture setting.
Interaction effects take priority over main effects and provide more detailed insights
Line graphs are used to visualize factorial data, with each line representing a level of one IV
Non-parallel lines indicate interaction effects.
Data within the matrix of conditions (cells) shows main and interaction effects
Inferential statistics are needed to confirm if effects are statistically significant
Example: A study with imagery vs. control and 2 vs. 4 seconds presentation rate might show main effects for both recall strategy and presentation rate, as well as an interaction effect.

Main and Interaction Effects

To determine statistical significance of main and interaction effects, a two-way ANOVA is performed.
ANOVA tests the null hypothesis that there are no differences between groups for the main effects.
It also tests the null hypothesis that there is no interaction effect.
If the ANOVA is significant, further analyses (post-hoc tests) are conducted to determine which specific pairs of groups are different.

Factorial Design

Involves more than one independent variable (IV)
Most studies have 2 - 4 IVs

Factorial Notation

Describes the number of IVs and levels of each IV
The number of digits equals the number IVs, and the value of the digit equals the number of levels
Example: In a 3x5 factorial there are 2 IVs, one with 3 levels and one with 5 levels.
Example: In a 2x2x 4 factorial, there are 3 IVs with 2, 2, and 4 levels respectively
To determine the number of conditions, multiply the numbers in the notation
Example: 2x4 = 8 conditions
Example: 2x2x4 = 16 conditions
In factorial designs, "levels" and "conditions" are distinct and cannot be used interchangeably

Analyzing Effects with Factorial Designs

Inferential statistics are used to interpret data.
Main effects refer to the overall effect of a particular IV.
The number of main effects equals the number of IVs
To determine the main effect of one IV, combine data over all levels of other IVs.
Interaction effects occur when the effect of one IV depends on the level of another IV
Factorial designs allow for detecting interactions.
Interaction effects are generally more important than main effects.
A matrix is useful to organize data to make it easier to plot in a line graph.
Each cell of the matrix represents the mean for a given condition.
To visually assess if there’s an interaction in a line graph, check if the lines are parallel.

Main and Interaction Effects

A factorial design with 2 IVs (A and B) requires an ANOVA to determine if main and interaction effects are statistically significant.

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Description

This quiz covers the concept of factorial design in experimental research, focusing on the use of multiple independent variables and their levels. It also discusses the analysis of effects using inferential statistics, including main and interaction effects. Perfect for students looking to solidify their understanding of statistical methods in research.