Post-hoc Pairwise Comparisons of Two-way ANOVA

February 4, 2011
By

(This article was first published on R Blog, and kindly contributed to R-bloggers)

I read this post today by John Quick. I was a little taken back when he used a pairwise t-test for post hoc analysis. In a contradiction the t-test did not show differences in the treatment means when the ANOVA model did. This is because the pairwise.t.test does not take into account the two-way anova, it only looks marginally, and so gives erroneous results. The more appropriate analysis should be TukeyHSD applied to the fitted model.

> model1<-aov(StressReduction~Treatment+Age, data )
> TukeyHSD(model1, "Treatment")
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = StressReduction ~ Treatment + Age, data = data)

$Treatment
                 diff         lwr        upr     p adj
mental-medical      2  0.92885267 3.07114733 0.0003172
physical-medical    1 -0.07114733 2.07114733 0.0702309
physical-mental    -1 -2.07114733 0.07114733 0.0702309

Created by Pretty R at inside-R.org

Here I already had the data read in as data, then I fit the model and applied a post-hoc pairwise test. This yielded that the mental and medical are different, but no other treatments. This is shown by the plot of the data.

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