One-Way Analysis of Variance (ANOVA) tells you if there are any statistical differences between the means of three or more independent groups.
How can ANOVA help?
ANOVA can help marketers understand how different groups respond.
For example, it can help you answer questions like this:
Do age, sex, and income have an effect on whether someone clicks on a landing page?
Do location, employment status, and education have an effect on NPS score?
The one-way ANOVA can help you know whether or not there are significant differences between the means of your independent variables (such as the first example: age, sex, income). When you understand how each independent variable’s mean is different from the others, you can begin to understand which of them has a connection to your dependent variable (landing page clicks).
How does ANOVA work?
Like other types of tests, ANOVA compares the means of different groups and shows you if there are any statistical differences between the means. ANOVA is classified as an omnibus test statistic. This means that it can’t tell you which specific groups were statistically significantly different from each other, only that at least two of the groups were.
It’s important to remember that the main ANOVA research question is whether the sample means are from different populations. There are two assumptions upon which ANOVA rests:
First: Whatever the technique of data collection, the observations within each sampled population are normally distributed.
Second: The sampled population has a common variance of s2.
We can help you run an ANOVA test. When users select one categorical variable with three or more groups and one continuous or discrete variable, we run a one-way ANOVA (Welch’s F test) and a series of pairwise “post hoc” tests (Games-Howell tests).
The one-way ANOVA tests for an overall relationship between the two variables, and the pairwise tests test each possible pair of groups to see if one group tends to have higher values than the other.
Assumptions Of Welch’s F Test ANOVA
KAJIDATA recommends an unranked Welch’s F test if several assumptions about the data hold:
- The sample size is greater than 10 times the number of groups in the calculation (groups with only one value are excluded), and therefore the Central Limit Theorem satisfies the requirement for normally distributed data.
- There are few or no outliers in the continuous/discrete data.
Unlike the slightly more common F test for equal variances, Welch’s F test does not assume that the variances of the groups being compared are equal. Assuming equal variances leads to less accurate results when variances are not in fact equal, and its results are very similar when variances are actually equal (Tomarken and Serlin, 1986) .
When assumptions are violated, the unranked ANOVA may no longer be valid. In that case, KAJIDATA recommends the ranked ANOVA (also called “ANOVA on ranks”); KAJIDATA rank-transforms the data (replaces values with their rank ordering) and then runs the same ANOVA on that transformed data.
The ranked ANOVA is robust to outliers and non-normally distributed data. Rank transformation is a well-established method for protecting against assumption violation (a “nonparametric” method), and is most commonly seen in the difference between the Pearson and Spearman correlation. Rank transformation followed by Welch’s F test is similar in effect to the Kruskal-Wallis Test (Zimmerman, 2012) .
Note that our ranked and unranked ANOVA effect sizes (Cohen’s f) are calculated using the F value from the F test for equal variances.
Assumptions Of Games-Howell Pairwise Test
KAJIDATA runs Games-Howell tests regardless of the outcome of the ANOVA test (as per Zimmerman, 2010 ). Results show unranked or ranked Games-Howell pairwise tests based on the same criteria as those used for ranked vs. unranked ANOVA, so if you see “Ranked ANOVA” in the advanced output, the pairwise tests will also be ranked.
The Games-Howell is essentially a t-test for unequal variances that accounts for the heightened likelihood of finding statistically significant results by chance when running many pairwise tests. Unlike the slightly more common Tukey’s b test, the Games-Howell test does not assume that the variances of the groups being compared are equal. Assuming equal variances leads to less accurate results when variances are not in fact equal, and its results are very similar when variances are actually equal (Howell, 2012 ).
Note that while the unranked pairwise test tests for the equality of the means of the two groups, the ranked pairwise test does not explicitly test for differences between the groups’ means or medians. Rather, it tests for a general tendency of one group to have larger values than the other.
Additionally, while KAJIDATA does not show results of pairwise tests for any group with less than four values, those groups are included in calculating the degrees of freedom for the other pairwise tests.
Additional ANOVA Considerations
- With smaller sample sizes, data can still be visually inspected to determine if it is in fact normally distributed; if it is, unranked t-test results are still valid even for small samples. In practice, this assessment can be difficult to make, so KAJIDATA recommends ranked t-tests by default for small samples.
- With larger sample sizes, outliers are less likely to negatively affect results. KAJIDATA uses Tukey’s “outside fence” to define outliers as points more than three times the intra-quartile range above the 75th or below the 25th percentile point.
- Data like Highest level of education completed or Finishing order in marathon are unambiguously ordinal. Though Likert scales (like a 1 to 7 scale where 1 is Very dissatisfied and 7 is Very satisfied) are technically ordinal, it is common practice in social sciences to treat them as though they are continuous (i.e., with an unranked t-test).
READ MORE ABOUT ADDITIONAL STATISTICAL ANALYSIS TYPES:
- Conjoint Analysis – coming soon
- T-Tests – coming soon
- CrossTab Analysis – coming soon
- Cluster Analysis – coming soon
- Factor Analysis – coming soon