ABSTRACT
The purpose of a one-way analysis of variance ( one-way ANOVA ) is to compare the means of two or more groups (the independent variable) on one dependent variable to see if the group means are significantly different from each other. In fact, if you want to compare the means of two independent groups on a single variable, you can use either an independent samples t test or a one-way ANOVA. The results will be identical, except that instead of producing a t value, the ANOVA will produce an F ratio, which is simply the t value squared (more about this in the next section of this chapter). Because the t test and the one-way ANOVA produce identical results when there are only two groups being compared, most researchers use the one-way ANOVA only when they are comparing three or more groups. To conduct a one-way ANOVA, you need to have a categorical (or nominal) variable that has at least two independent groups (e.g., a race variable with the categories African-American, Latino, and Euro-American) as the independent variable and a continuous variable (e.g., achievement test scores) as the dependent variable. It is assumed in ANOVA that the variance in the dependent variable is equal in each of the groups being compared.
