[R] Non-parametric test for repeated measures and post-hoc single comparisons in R?

[saschaview at gmail.com]

Some attribute x from 17 individuals was recorded repeatedly on 6 time 
points using a Likert scale with 7 distractors. Which statistical 
test(s) can I apply to check whether the changes along the 6 time points 
were significant?

set.seed( 123 )
x <- matrix( sample( 1:7, 17*6, repl=T ),
   nrow = 17, byrow = TRUE,
   dimnames = list(1:17, paste( 'T', 1:6, sep='' ))
)

I found the Friedman test and the Quade test for testing the overall 
hypothesis.

friedman.test( x )
quade.test( x )

However, the R help files, my text books (Bortz, Lienert and Boehnke, 
2008; Köhler, Schachtel and Voleske, 2007; both German), and the 
Wikipedia texts differ in what they propose as requirements for the 
tests. R says that data need to be unreplicated. I read 'unreplicated' 
as 'not-repeated', but is that right? If so, the example, in contrast, 
in friedman.test() appears to use indeed repeated measures. Yet, 
Wikipedia says the contrary that is to say the test is good especially 
if data represents repeated measures. The text books say either (in the 
same paragraph, which is very confusing). What is right?

In addition, what would be an appropriate test for post-hoc single 
comparisons for the indication which column differs from others 
significantly?

Bortz, Lienert, Boehnke (2008). Verteilungsfreie Methoden in der 
Biostatistik. Berlin: Springer
Köhler, Schachtel, Voleske (2007). Biostatistik: Eine Einführung für 
Biologen und Agrarwissenschaftler. Berlin: Springer

-- 
Sascha Vieweg, saschaview at gmail.com