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, saschav...@gmail.com
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