Lesson 3: Between-Subjects Factorial Design

between-subjects factorial design

These designs involve more than one independent variable (IV)

between-subjects factorial design

allow researchers to examine how multiple IVs interact to affect a dependent variable (DV)

factorial design

s an experimental design in which two or more independent variables (IVs) are tested simultaneously to determine their individual and interactive effects on the dependent variable (DV).

factorial designs

These designs are particularly useful for understanding interaction effects between variables.

between-subjects factorial design

each participant is exposed to only one condition from each independent variable.

between-subjects factorial design

This is essential when the exposure to one condition might carry over effects to another, which would contaminate results in a within-subjects design

multiple independent variables, between-subjects design, interaction effects

key features of between-subjects factorial design

multiple independent variables

There are two or more IVs in the design.

between-subjects design

Each participant is assigned to only one condition of each IV.

interaction effects

Factorial designs allow researchers to test how the combination of IVs affects the DV, not just the individual effects.

factorial design

typically described in terms of its levels and factors.

factors

The independent variables (e.g., type of therapy, dosage of medication).

levels

The number of variations or categories within each factor (e.g., High, Low, and No treatment for the therapy factor)

2×2 factorial design

 2 factors (e.g., Type of therapy and Dosage of medication).

 Each factor has 2 levels (e.g., Therapy A vs. Therapy B, High dose vs. Low dose).

factorial designs

can be expanded to include more factors and levels.

3×2 factorial design

3 levels of IV1 and 2 levels of IV2

2×3×2 factorial design

2 levels of IV1, 3 levels of IV2, and 2 levels of IV3.

representative

When designing a factorial experiment, it’s crucial to choose a sample that is _____ of the population of interest.

size of the sample, random assignment, power analysis

factors to consider when selecting the right sample

size of the sample

Factorial designs typically require a larger sample size than simpler designs due to the need to fill multiple experimental conditions. Larger samples improve the power of the study and help ensure that the results are statistically significant.

random assignment

To control for extraneous variables, participants should be randomly assigned to the different conditions of the experiment

power analysis

Prior to the experiment, it’s useful to conduct a ____ to determine the necessary sample size, ensuring the study has enough power to detect a significant effect

power analysis

determine the necessary sample size, ensuring the study has enough power to detect a significant effect (

convenience, random, matched groups

types of samples in between-subjects factorial design

convenience sample

Easy to recruit (e.g., college students in an introductory

psychology class), but may lack generalizability

random sample

Increases external validity and allows for generalization to a broader population

matched groups

In cases where there are small sample sizes or participants have specific characteristics, researchers can match participants on relevant variables to ensure comparability across groups

interaction effects

One of the key advantages of factorial designs is the ability to investigate ____—how two or more IVs combine to influence the DV in a way that is different from the sum of their individual effects.

interaction effects

how two or more IVs combine to influence the DV in a way that is different from the sum of their individual effects.

simple main effects

refer to the effect of one IV at each level of another IV.

complexity in analysis, participant fatigue, resource intensive

practical issues in conducting factorial designs

complexity in analysis

As the number of factors and levels increases, so does the complexity of the statistical analysis.

factorial designs

require more advanced statistical tests, such as ANOVA, to analyze the interaction effects between variables

participant fatigue

With more conditions and factors, participants may become fatigued or lose motivation, especially if the experiment involves multiple tests or extended testing times.

resource intensive

More participants are needed, and recruitment, data collection, and analysis become more time-consuming

gravetter & forzano

Discuss how factorial designs offer a clear advantage in studying interaction effects between variables, which are often overlooked in simpler designs.

field

Emphasizes the statistical significance of interaction effects and highlights the importance of power analysis in ensuring a study is adequately powered.

shaughnessy et al

Explore the practical implementation of factorial designs, emphasizing random assignment and the complexities of controlling extraneous variables.

factorial designs

powerful tool for understanding how multiple independent variables interact to affect a dependent variable

between-subjects factorial design

particularly valuable when the potential for carryover effects exists