A 2×3 factorial design is a type of experimental design that allows researchers to understand the effects of two independent variables on a single dependent variable.
In this type of design, one independent variable has two levels and the other independent variable has three levels.
For example, suppose a botanist wants to understand the effects of sunlight (low vs. medium vs. high) and watering frequency (daily vs. weekly) on the growth of a certain species of plant.
This is an example of a 2×3 factorial design because there are two independent variables, one having two levels and the other having three levels:
- Independent variable #1: Sunlight
- Levels: Low, Medium, High
- Independent variable #2: Watering Frequency
- Levels: Daily, Weekly
And there is one dependent variable: Plant growth.
The Purpose of a 2×3 Factorial Design
A 2×3 factorial design allows you to analyze the following effects:
Main Effects: These are the effects that just one independent variable has on the dependent variable.
For example, in our previous scenario we could analyze the following main effects:
- Main effect of sunlight on plant growth.
- Mean growth of all plants that received low sunlight.
- Mean growth of all plants that received medium sunlight.
- Mean growth of all plants that received high sunlight.
- Main effect of watering frequency on plant growth.
- Mean growth of all plants that were watered daily.
- Mean growth of all plants that were watered weekly.
Interaction Effects: These occur when the effect that one independent variable has on the dependent variable depends on the level of the other independent variable.
For example, in our previous scenario we could analyze the following interaction effects:
- Does the effect of sunlight on plant growth depend on watering frequency?
- Does the effect of watering frequency on plant growth depend on the amount of sunlight?
How to Analyze a 2×3 Factorial Design
We can perform a two-way ANOVA to formally test whether or not the independent variables have a statistically significant relationship with the dependent variable.
For example, the following code shows how to perform a two-way ANOVA for our hypothetical plant scenario in R:
#make this example reproducible set.seed(0) #create data df <- data.frame(sunlight = rep(c('Low', 'Medium', 'High'), each = 15, times = 2), water = rep(c('Daily', 'Weekly'), each = 45, times = 2), growth = c(rnorm(15, 9, 2), rnorm(15, 10, 3), rnorm(15, 13, 2), rnorm(15, 8, 3), rnorm(15, 10, 4), rnorm(15, 12, 3))) #fit the two-way ANOVA model model <- aov(growth ~ sunlight * water, data = df) #view the model output summary(model) Df Sum Sq Mean Sq F value Pr(>F) sunlight 2 602.3 301.15 50.811 <2e-16 *** water 1 39.6 39.62 6.685 0.0105 * sunlight:water 2 15.1 7.56 1.275 0.2819 Residuals 174 1031.3 5.93 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Here’s how to interpret the output of the ANOVA:
- The p-value associated with sunlight is <2e-16. Since this is less than .05, this means sunlight exposure has a statistically significant effect on plant growth.
- The p-value associated with water is .0105. Since this is less than .05, this means watering frequency also has a statistically significant effect on plant growth.
- The p-value for the interaction between sunlight and water is .2819. Since this is not less than .05, this means there is no interaction effect between sunlight and water.
The following tutorials provide additional information on experimental design and analysis: