Alex Baugh wrote:
Hi Folks,
I have repeated measures for data on association time (under 2
acoustic condtions) in male and female frogs as they grow to adulthood
(6 timepoints). Thus, two within-subject variables (Acoustic
Condition: 2 levels, Timepoint: 6 levels) and one between-subject
variable (Sex:male or female).
I am pretty sure my distributions depart from normality but I would
first like to simply run a RM anova on the data. My problem is that
when I do this I generate different values of F for my main effects
and interaction when I do the analysis in [R] and SPSS - so I don't
know which one to believe.
Here is my code in R:
mydata.tab=read.delim("mydata.txt", header=T) #read in my data
mydata.tab$Timepoint=as.factor(mydata.tab$Timepoint) #col headings
are factors so df are correct
mydata.tab$Acx.Cond=as.factor(mydata.tab$Acx.Cond)
mydata.tab$Sex=as.factor(mydata.tab$Sex)
aov.F=aov(Targ.Assoc.Time~(Timepoint*Acx.Cond*Sex) +
Error(Subject/(Timepoint*Acx.Cond))+(Sex), data=mydata.tab)
#run aov where i look at the main effects of Timepoint, Acoustic
Condition and Sex as well as all the interactions therein on the
amount of time a frog spends associating with the target sound.
Include anything to do with Subject in the error term.
Does this look right for a Repeated Measures ANOVA, or am I missing
something to make it RM and that explains the large discrepancies in
my F-values between [R] and SPSS?
Nothing obviously wrong to my eyes. There's a stray (Sex) term, but I
don't think that actually does anything. I assume that your data are
balanced and complete. Apart from that it coincides with my expectation
of what SPSS would do.
You'd give us a better chance of helping if you actually included some
output on the two systems.
If you approach this as a multivariate linear model (with 12-dimensional
response), then you can also use the features of anova.mlm (the example
on the help page is quite similar to your setup). This takes a bit more
work, but it give the "epsilon" corrections that people like to
calculate for these models.
As soon as I get this canonical aov code figured out I want to derive
my p-values by bootstrapping my F distributions, but first I need
those canonical F's.
Thanks
-Alex
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