##### Browse Site

###### Tutorials

###### AP Statistics

###### Stat Tables

- Binomial
- Chi-Square
- f Distribution
- Hypergeometric
- Multinomial
- Negative Binomial
- Normal
- Poisson
- t Distribution

###### Stat Tools

- Random Number
- Probability Calculator
- Bayes Rule Calculator
- Combination-Permutation
- Factorial Calculator
- Event Counter
- Bartlett's test
- Sample Size Calculator

###### Help

# Statistics Dictionary

To see a definition, select a term from the dropdown text box below. The statistics dictionary will display the definition, plus links to related web pages.

**Select term:**

### Interaction Plot

An interaction plot is a line graph that reveals the presence or absence of interactions among independent variables. To create an interaction plot, do the following:

- Show the dependent variable on the vertical axis (i.e., the Y axis); and an independent variable, on the horizontal axis (i.e., the X axis).
- Plot mean scores on the dependent variable separately for each level of a potential interacting variable.
- Connect the mean scores, producing separate lines for each level of the interacting variable

To understand potential interaction effects, compare the lines from the interaction plot:

- If the lines are parallel, there is no interaction.
- If the lines are not parallel, there is an interaction.

For example, suppose researchers develop a drug to treat anxiety. The dependent variable is anxiety (plotted on the Y axis). The independent variable is dose (plotted on the X axis). Researchers might hypothesize an interaction effect, based on gender. To visualize the potential interaction, they would plot mean anxiety score by gender for each dose and connect the means with lines, as shown below:

In the plot above, the lines are parallel. This suggests no intereaction effect, based on gender. The drug has the same effect on men as on women. For both men and women, 1 mg of drug lowers anxiety level by 0.2 units.

Suppose, however, the interaction plot looked like this:

Here, the lines are not parallel. The line for women is steeper. This suggests a possible interaction effect, based on gender.

See also: | Interaction Effects in Regression | What is a Full Factorial Experiment? |