There are several types of ANOVA procedures. The term *ANOVA*
refers to *Analysis of Variance*. *Variance*
is a statistical term we will review later. Variance refers to differences, so
the ANOVA procedures examine differences in scores among groups of people who
complete a survey. For example, an ANOVA can be used to assess different levels
of membership satisfaction by people who experience three or four different
leadership styles. The ANOVA procedure is usually reported with an *F* value. The larger the *F* value, the more likely it is that the
differences the researchers found are not due to chance.

There may be several independent
variables in a project. The effect of each variable is tested with an *F* test. When there are two or more
variables, researchers also test for possible interaction effects, which
results in additional *F* tests for
each interaction. *Interactions* refer
to the possibility that two or more variables combine to produce a change in
the dependent variable. As with *t* tests,
researchers include a probability (*p*)
value with each *F* test. A common
effect size associated with *F* tests
is partial eta squared. An ANOVA is used when there are one or more independent
variables but only one dependent variable.

**Read more about ANOVA in the following books.**

**Creating Surveys on AMAZON or GOOGLE**

**Applied Statistics Concepts for Counselors on AMAZON or GOOGLE**

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