Archive:Advanced ANOVA/Assumptions: Difference between revisions
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This page outlines and summarises the assumptions for various ANOVA models. | This page outlines and summarises the assumptions for various '''ANOVA''' models. | ||
==All models== | ==All models== | ||
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#** ANOVA is quite robust to violations of this assumption if sample sizes are large and approximately equal (> 15 cases per group) | #** ANOVA is quite robust to violations of this assumption if sample sizes are large and approximately equal (> 15 cases per group) | ||
==[[ | ==[[{{BASEPAGENAME}}/Testing differences|''t''-tests]], [[{{BASEPAGENAME}}/ANOVA|one-way ANOVA]] and [[{{BASEPAGENAME}}/Factorial ANOVA|factorial ANOVA]]== | ||
# The data in each cell is '''[[normality|normally distributed]]'''. | # The data in each cell is '''[[normality|normally distributed]]'''. | ||
# '''Homogeneity of variance''': | # '''Homogeneity of variance''': | ||
Revision as of 05:32, 3 December 2008
Template:50%done This page outlines and summarises the assumptions for various ANOVA models.
All models
- Dependent variables must be:
- Measured at interval or ratio level level of measurement.
- Normally distributed in all groups of the independent variable.
- ANOVA is quite robust to violations of this assumption if sample sizes are large and approximately equal (> 15 cases per group)
- The data in each cell is normally distributed.
- Homogeneity of variance:
- Cells are independent - Cases represent random samples from the target populations and the scores of the test variable should be independent of each other (i.e., the scores in one cell are not dependent on the scores in another cell).
- Inaccurate p-values are produced if the independence assumption is violated.