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

peter dalgaard pdalgd at gmail.com
Sun Feb 19 22:04:06 CET 2012

Repeated measures means that you have multiple measurements on the same individual. Usually, the same person measured at different time points. So if you have N individuals and T times, then you can place your observations in an N*T layout. 

In this layout, you can have 1 observation per cell or R > 1 observations. In the former case, the design is referred to as unreplicated.  Got it?


On Feb 19, 2012, at 19:25 , saschaview at gmail.com wrote:

> 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
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Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com

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