I have been trying to get answers for a while, and have looked up every help section and tried other stats sites. I have a thesis that I need to be completing shortly, but one set of data has been completely out of my league due to a small sample size.
Basically, I have four broods (birds with chicks - precocial so they can peck at hatch e.g. chickens). I measured the proportion of time each brood fed every other day (approximately) until they flew. I had three broods in the ocean front and one brood in the mudflat habitats.
Each data point within a brood is non-independent, so the only way to compare the mf brood against the OF broods in terms of time spent feeding (and other behaviours) would be to use the mean and use a Kruskal-Wallis or a one-sample Wilcoxon (mu=mean for MF brood). Of course the sample is too small to get anywhere near significance even though there is a clear pattern of MF feeding more and being less disturbed when I look at all the data points.
It was suggested that I could use a mixed effects model so that each data point for a brood could be used and each brood would be considered a block. The person typed out some ideas for models but no one could tell me how to do it step-by-step and I don't really understand them. I found a source which would let me do it in Minitab (https://onlinecourses.science.psu.edu/stat502/node/72) but when I tried it it told me that it is unbalanced and I think it is because the first block is in one 'treatment' and the other three in the other 'treatment' but there is no overlapping of either.
Another issue is that now I may have figured what the models do, I think they just try to explain the reasons why things are as they are, but I would still like to be able to say that MF feed significantly more than OF broods and that MF brood is disturbed less often. The model would only try to explain the reasons why rather than if they are significantly different (which is all I am really trying to figure out).
If there is an easier way to compare two groups that have independent and non-independent data then I would greatly appreciate it. I actually thought of using a point biserial correlation using the means with MF coded as 0 and OF coded as 1. It works but is it valid? (http://faculty.vassar.edu/lowry/pbcorr.html)
Is there anyone out there who could possibly help me with this? I am reaching my deadline and know that if I could just do this part then the rest should be okay. I am also a foreign student and my professor has said he can't help me long distance.
I also have flush rates vs number of days to hatch. I wanted to see if birds were less likely to flush off their nest closer to hatch so used correlation. I have data for 7 nests and plotted each data point (n=77). The issue again is that not all points are independent but I need to show the flush rate for nest age (and not all nests were measured at the same age). Should I do a correlation for each nest and then is there someway of combining them to give me a p-value, or should I leave it as it is?