Choosing two-way ANOVA

What is two-way ANOVA used for?

Two-way ANOVA, also called two-factor ANOVA, determines how a response is affected by two factors. For example, you might measure a response to three different drugs in both men and women. In this example, drug treatment is one factor and gender is the other.

Two-way ANOVA simultaneously asks three questions:

1.	Does the first factor systematically affect the results? In our example: Are the mean responses the same for all three drugs?

2.	Does the second factor systematically affect the results? In our example: Are the mean responses the same for men and women?

3.	Do the two factors interact? In our example: Are the differences between drugs the same for men and women? Or equivalently, is the difference between men and women the same for all drugs?

Although the outcome measure (dependent variable) is a continuous variable, each factor must be categorical, for example: male or female; low, medium or high dose; or wild type or mutant. ANOVA is not an appropriate test for assessing the effects of a continuous variable, such as blood pressure or hormone level (use a regression technique instead).

Two-way ANOVA choices

Repeated measures

Choose a repeated-measures analysis when the experiment used paired or matched subjects. Prism can calculate repeated-measures two-way ANOVA with matching by either row or column, but not both. Details. This is sometimes called a mixed model.

You can only choose repeated measures when you have entered two or more side-by-side values into subcolumns for each row and dataset.

Post tests

Following two-way ANOVA, there are many possible multiple comparison tests that can help you focus in on what is really going on. However, Prism performs only the post tests biologists use most frequently. At each row, Prism will compare each column with every column, or compare every column with a control column.

Variable names

If you enter names for the factors that define the rows and columns, the results will be easier to follow.