[R] Non-parametric test for repeated measures and post-hoc single comparisons in R?
Meyners, Michael
meyners.m at pg.com
Mon Feb 20 17:26:07 CET 2012
No, the authors are correct: the individuals (i.e. the 17 individuals) you have need to be independent (i.e. no correlation between them, let alone any individual running through your temporal experiment more than once, as indicated in the citation), while the *observations* are of course dependent as they are within the same subject (individual -- they have the same subject effect). Think of Friedman as a non-parametric 2-way ANOVA with one of the factors being subject; observations of the same subject are dependent, but once you include the subject effect, the errors are assumed to be independent (which implies that subjects need to be independent and should, e.g., not work on the assessment together).
The imprecision is in your interpretation of individuals vs. observations.
HTH, Michael
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of saschaview at gmail.com
> Sent: Montag, 20. Februar 2012 09:59
> To: peter dalgaard
> Cc: r-help at r-project.org
> Subject: Re: [R] Non-parametric test for repeated measures and post-hoc
> single comparisons in R?
>
> Thanks, I got it! (And I think I should have googled what "replicated"
> means!) However, then Bortz, Lienert, Boehnke are imprecise, if not
> wrong: "Der Friedman-Test setzt voraus, dass die N Individuen
> wechselseitig unabhängig sind, dass also nicht etwa ein und dasselbe
> Individuum zweimal oder mehrmals im Untersuchungsplan auftritt" (p.
> 271). Which I (hope to) translate: The Friedman test requires the N
> individuals to be reciprocally independent, which means that one
> individual cannot occur twice or more times in the research design.
>
> *S*
>
> On 19.02.12 22:04, peter dalgaard wrote:
>
> > 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?
> >
> > -pd
> >
> >
> > 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
> >>
> >> ______________________________________________
> >> R-help at r-project.org mailing list
> >> https://stat.ethz.ch/mailman/listinfo/r-help
> >> PLEASE do read the posting guide
> >> http://www.R-project.org/posting-guide.html
> >> and provide commented, minimal, self-contained, reproducible code.
> >
>
>
> --
> Sascha Vieweg, saschaview at gmail.com
>
> ______________________________________________
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