I think this means an unequal sample in different conditions. But it seems to mean something else. . .
I have a data set like below
particip group device width length accep thresh rating d-rating 1 RA Dingo nom nom Y 5 8 3 1 RA Dingo nom long Y 4 6 2 1 RA Dingo fat nom Y 4 6 2 1 RA Dingo fat long N 6 4 -2
and I'm running an ANOVA on it like so
aov.AMIDS_d <- aov(d.rating ~ group*device*width*length + Error(particip/(device*width*length))+group,data.AMIDS_d)
This works ok until I try to print the condition means like so
and it says
Error in model.tables.aovlist(aov.AMIDS_d, "means") : design is unbalanced so cannot proceed
According to the design, it ought to be balanced, so I need to check my data structure. I tried
to give a table of observations per condition and got this
, , width = fat, length = long device group Dingo SNAR NR 12 12 NV 12 12 RA 12 12 , , width = nom, length = long device group Dingo SNAR NR 12 12 NV 12 12 RA 12 12 , , width = fat, length = nom device group Dingo SNAR NR 12 12 NV 12 12 RA 12 12 , , width = nom, length = nom device group Dingo SNAR NR 12 12 NV 12 12 RA 12 12
which looks both correct and balanced. So what is causing the unbalanced design error?
In case anyone else has this problem, check that your all your factor columns, particularly any containing numbers and including your participant-identifier column, are classed as a Factor and not as an Integer by using str(yourdata) or class(yourdata$columnname). My particular culprit was the participants column.
If it's classed as an Integer, then
yourdata$columnname <- as.factor(yourdata$columnname)
will re-class it as a factor.
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