# ANOVA-Type Tests - Comparing Three or More Groups

Related Online Training modules
Column Comparisons
Planned Tests Of Statistical Significance
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ANOVA-type tests involve comparing three or more cells, most commonly in rows of a table. They are conducted automatically when:

There are two basic variants of these tests:

• Independent samples tests, which compare data between different sub-groups. This is sometimes referred to as One-Way ANOVA and One-Way Layouts. In Q, these occur when you have
• The table has numeric data in the rows (i.e., Number or Number - Multi question in the brown drop-down).
• The table has mutually exclusive categories within spans in the columns (i.e., has no NETs).
• Repeated measures tests, where each respondent has provided multiple responses and these are to be compared (e.g., evaluations of different products). These are sometimes referred to as Two-Way ANOVA and Two-Way Layouts. In Q, these occur when you have a Pick One – Multi question selected in the blue drop-down menu with the variables appearing as the columns. (Where you wish to compare averages rather than categories, select Statistics – Below and Average.)

Additionally, whether the setting for Means in Statistical tests for categorical and numeric data is set to Non-parametric and the Question Type of the question in the blue drop-down are also determinants of how the tests are computed, as described in the table below.

Independent samples Dependent samples
Not Non-parametric and the data in the rows is numeric (i.e., Number or Number - Multi) F-Test (ANOVA) Repeated Measures ANOVA with Greenhouse & Geisser Epsilon Correction .
Non-parametric and the data in the rows is numeric (i.e., Number or Number - Multi) Kruskal-Wallis Test Friedman Test for Correlated Samples
Non-parametric and the data in the rows is categorical (Pick One, Pick Any or Pick Any - Compact) Pearson's Chi-Square Test of Independence Cochran's Q
Non-parametric and the data in the rows is mutually exclusive (e.g., choices on a rating scale) Pearson's Chi-Square Test of Independence Chi-Square Test for Compatibility of K Counts