ANOVA-Type Tests - Comparing Three or More Groups
From Q
Related Online Training modules | |
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Column Comparisons | |
Planned Tests Of Statistical Significance | |
Generally it is best to access online training from within Q by selecting Help > Online Training |
ANOVA-type tests involve comparing three or more cells, most commonly in rows of a table. They are conducted automatically when:
- ANOVA-Type Test is selected in in the Column comparisons settings of Statistical Assumptions.
- When appropriate data is selected when pressing (i.e., when conducting Planned Tests Of Statistical Significance). See Planned ANOVA-Type Tests.
- When using Smart Tables with a Number question by a Pick One question.
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 | |
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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 |