Next section: Variance explained in ANOVA

Assuming job satisfaction is measured on a seven-point scale, how big an effect is one that is 1.5 units on the scale? Is it big enough to be important either theoretically or practically? There is no clear answer to this question. The most common approach is to interpret the size of the mean difference relative to the differences occurring within each group. This involves defining the size of the effect in terms of the degree of overlap between the groups. Thus, an experiment finding a difference of 1.5 with very little overlap between groups would be said to have revealed a larger effect than one finding the same difference of 1.5 but with greater overlap between groups.

Measures of the size of an effect based on the degree of overlap between groups usually involve calculating the proportion of the variance that can be explained by differences between groups. At one extreme is the case in which the only differences are differences between groups with all scores within a group being equal. At the other extreme is the case in which the group means are equal. In the former case, 100% of the variance is explained by differences between groups; in the latter case, 0% of the variance is explained.

Next section: Variance explained in ANOVA