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.
Independent or grouping Variable = 1 or more
Dependent or criterion Variable = 1
Overall tests are used to determine significant effects or differences among the grouping variables.
An F test indicates significance overall and for specific effects or relationships.
A commonly reported measure of effect size is eta squared.
A p value reveals the probability of a significant relationship-- one that is not due to chance factors.
Read more about ANOVA in the following books.
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