Hello, I am hoping someone could give me some advice on an approach to analysing my data.
I have a set of behavioural data (39 behavioural variables such as time spent foraging, number of foraging bouts, time spent vigilant etc), which were measured for 34 sites over 3 nights. Each of the 34 sites had one of 5 different treatments (so evidently I don't have completely equal numbers of replicates in each treatment, but close). The first of the 3 nights was a baseline night without any treatment.
So I basically want to see if the animals changed their behaviour on nights 1 and 2 with comparison to baseline, and if that differed between treatments.
OPTION 1: would be to attempt a PERMANOVA on the original scores, and the design would be something like:
1 Between-Subjects factor (Treatment, 5 levels)
1 Within-Subjects/Repeated Measures factor (Night, 2 levels)
1 Random factor (Site, 34 levels) that is nested within Treatment
1 covariate matrix (Baseline measurements) <- there is a lot of between-site variance so this is important
39 dependent variables (behaviours)
I tried this in Primer-E, and came up with some strange 'cannot compute' type answers in the read out - so I assume I have something wrong somewhere.
Do I need to normalise my data for this? I have transformed it. Does it matter than several behaviours are highly correlated (e.g. number of bouts of vigilance, duration of bouts of vigilance), or that there are several different measurement scales (e.g. proportion of time in sight, duration (in milliseconds), number of bouts (count))
The other issue here is telling which behaviours differentiate the groups - (we would expect it was an anxiety-like behaviour) - I know you can do this with ANOSIM and then SIMPER in Primer-E, but there doesn't seem to be a SIMPER equivalent for PERMANOVA.
OPTION 2: Calculate change scores for each night by subtracting baseline values (so the depvars become change from baseline values). The result is that we don't need a covariate anymore. Complete PERMANOVA using a similar model. Do I need to normalise my data for this? I have transformed it. Same issues with correlated depvars, different measurement scales of depvars.
1 Between-Subjects factor (Treatment, 5 levels)
1 Within-Subjects/Repeated Measures factor (Night, 2 levels)
1 Random factor (Site, 34 levels) that is nested within Treatment
OPTION 3: Transform, normalise behavioural scores and the perform PCA to reduce them down to a limited number of factors. Use factor scores to calculate how each animal did on a new behavioural score that hopefully is meaningful (e.g. 'anxiety'), based on a meaningful principle component, by summing the scores that contribute to that PC for each animal. Then perform a repeated measures ANOVA (still with site nested in Treatment) on how the animals in each treatment change their score on this new 'anxiety' measure in each treatment, over two nights. This is an approach I saw in another paper, but they didn't have a repeated measure, so I'm not clear if it is ok to do the PCA on the full data set with all 3 nights, or just the change scores, or what other approach to use. I have read about PARAFAC but I'm not sure where to find it/how to implement it.
Phew. I'd really appreciate any advice, these all seem like pretty complicated approaches and I'd like to know if one of them makes more sense than the others.
I have a set of behavioural data (39 behavioural variables such as time spent foraging, number of foraging bouts, time spent vigilant etc), which were measured for 34 sites over 3 nights. Each of the 34 sites had one of 5 different treatments (so evidently I don't have completely equal numbers of replicates in each treatment, but close). The first of the 3 nights was a baseline night without any treatment.
So I basically want to see if the animals changed their behaviour on nights 1 and 2 with comparison to baseline, and if that differed between treatments.
OPTION 1: would be to attempt a PERMANOVA on the original scores, and the design would be something like:
1 Between-Subjects factor (Treatment, 5 levels)
1 Within-Subjects/Repeated Measures factor (Night, 2 levels)
1 Random factor (Site, 34 levels) that is nested within Treatment
1 covariate matrix (Baseline measurements) <- there is a lot of between-site variance so this is important
39 dependent variables (behaviours)
I tried this in Primer-E, and came up with some strange 'cannot compute' type answers in the read out - so I assume I have something wrong somewhere.
Do I need to normalise my data for this? I have transformed it. Does it matter than several behaviours are highly correlated (e.g. number of bouts of vigilance, duration of bouts of vigilance), or that there are several different measurement scales (e.g. proportion of time in sight, duration (in milliseconds), number of bouts (count))
The other issue here is telling which behaviours differentiate the groups - (we would expect it was an anxiety-like behaviour) - I know you can do this with ANOSIM and then SIMPER in Primer-E, but there doesn't seem to be a SIMPER equivalent for PERMANOVA.
OPTION 2: Calculate change scores for each night by subtracting baseline values (so the depvars become change from baseline values). The result is that we don't need a covariate anymore. Complete PERMANOVA using a similar model. Do I need to normalise my data for this? I have transformed it. Same issues with correlated depvars, different measurement scales of depvars.
1 Between-Subjects factor (Treatment, 5 levels)
1 Within-Subjects/Repeated Measures factor (Night, 2 levels)
1 Random factor (Site, 34 levels) that is nested within Treatment
OPTION 3: Transform, normalise behavioural scores and the perform PCA to reduce them down to a limited number of factors. Use factor scores to calculate how each animal did on a new behavioural score that hopefully is meaningful (e.g. 'anxiety'), based on a meaningful principle component, by summing the scores that contribute to that PC for each animal. Then perform a repeated measures ANOVA (still with site nested in Treatment) on how the animals in each treatment change their score on this new 'anxiety' measure in each treatment, over two nights. This is an approach I saw in another paper, but they didn't have a repeated measure, so I'm not clear if it is ok to do the PCA on the full data set with all 3 nights, or just the change scores, or what other approach to use. I have read about PARAFAC but I'm not sure where to find it/how to implement it.
Phew. I'd really appreciate any advice, these all seem like pretty complicated approaches and I'd like to know if one of them makes more sense than the others.
