One factor analysis of variance, also known as ANOVA , gives us a way to make multiple comparisons of several population means. Rather than doing this in a pairwise manner, we can look simultaneously at all of the means under consideration. To perform an ANOVA test, we need to compare two kinds of variation, the variation between the sample means, as well as the variation within each of our samples. We do this by dividing the variation between samples by the variation within each sample. The way to do this is typically handled by software, however, there is some value in seeing one such calculation worked out. It will be easy to get lost in what follows.
You want to tell your reader what type of analysis you conducted. This will help your reader make sense of your results. You also want to tell your reader why this particular analysis was used. What did your analysis tests for?
Published on March 6, by Rebecca Bevans. Revised on January 7, ANOVA, which stands for Analysis of Variance, is a statistical test used to analyze the difference between the means of more than two groups.
Measuring the mean scores of subjects during three or more time points. For example, you might want to measure the resting heart rate of subjects one month before they start a training program, during the middle of the training program, and one month after the training program to see if there is a significant difference in mean resting heart rate across these three time points. Notice how the same subjects show up at each time point. Measuring the mean scores of subjects under three different conditions.