![]() ![]() ![]() How the repeated measures ANOVA is calculated ![]() If the P value is high, then you may question the decision to use repeated measures ANOVA in future experiments like this one. If the P value is small, this shows you have justification for choosing repeated measures ANOVA. The corresponding P value tests the null hypothesis that the subjects are all the same. This row quantifies how much of all the variation among the values is due to differences between subjects. Repeated measures ANOVA has one additional row in the ANOVA table, "Subjects (matching)". Also read the general page on the assumption of sphericity, and assessing violations of that assumption with epsilon. So read the general page on interpreting two-way ANOVA results first. When interpreting the results of two-way ANOVA, most of the considerations are the same whether or not you have repeated measures. Interpreting P values from repeated measures two-way ANOVA If one of the factors in ANOVA is dose (say 0, 10, 20 and 50 mg) or time (say 0, 10, 20, 30, 60 minutes), ANOVA treats these doses or time points just like it teats different species or different drugs, totally ignoring the fact that doses or time points are ordered. If one of the factors is a quantitative factor like time or dose, consider alternatives to ANOVA. Are you sure that ANOVA is the best analysis?īefore interpreting the ANOVA results, first do a reality check. Note there is a separate page for interpreting the fit of a mixed model. ![]()
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