Create General Full Factorial Design

A full factorial design is a design in which researchers measure responses at all combinations of the factor levels. General full factorial designs are full factorial designs that contain factors with more than two levels.

For example, engineers conduct an experiment to investigate the effects of humidity, temperature, and copper content on the amount of warping that occurs in a copper plate. Humidity has 2 levels, temperature has 3 levels, and copper has 5 levels. They create a general full factorial design because two factors have more than 2 levels. The design includes 30 experimental runs, which represent all combinations of the factor levels.

Perform the analysis

Complete the following steps to specify the design.

Enter the name of your response variable: The worksheet includes a column with this name where you enter the data from the experiment.
Maximum number of levels: Select the number of levels for the factor with the most levels that you want to study in the experiment. The number of columns in the table of factors changes.
Table of factors: Under Name, enter a descriptive name for each factor.; Enter labels for the levels. Factors with fewer levels than the maximum number of levels have empty boxes. Labels can be numbers or text. If you have a text factor and the levels have no natural order, you can specify the levels in any order.
Number of replicates: Select the number of replicates. Replicates are multiple experimental runs with the same factor settings (levels). One replicate is equivalent to the base design, where you conduct each run once. With two replicates, you perform each run twice (in random order), and so on.; Adding replicates can help increase the precision of your model and increase the power to detect effects. To determine how many replicates to include in your design, consider the available resources and the purpose of your design. For example, in a screening design or in sequential experimentation you could begin with the base design (1 replicate) and then consider whether to add replicates after you analyze the data. You can add replicates to your design later with Stat > DOE > Modify Design. For more information on replicates, go to Replicates and repeats in designed experiments.

Example

A marketing manager wants to study the influence that three categorical factors have on the ability of test subjects to recall an online advertisement. Because the experiment includes factors that have 3 levels, the manager uses a general full factorial design.

Choose Stat > DOE > Quick Designs.
Select Select a Three-Factor Design.
Select Create an experiment with three categorical factors. Select OK.
Select Estimate main and interaction effects when at least one factor has more than two levels. Select OK.
In the new dialog, in Enter the name of your response variable, enter Recall.
In Maximum number of levels, select 3.

Complete the table with the following settings:

Name		Levels
Website	News	Social media	Sports
Product	Car	Video game	Medicine
Message style	You know you should.	Just the facts.	That is awesome!

In Number of replicates, select None. Select OK.

The design summary table shows that the design has 27 base runs. The worksheet contains the 27 runs in run order, which is random.