I'm just starting to work with some count data and I'm still trying to understand some of the complexities of it, so any help would be greatly appreciated. First the simple version, and then the potentially more complex version.
I have a dataset that looks something like this:
Group Count L 4 R 5 C 9 L 16 L 3 R 8 C 5
A central part of my research question is within/between group variation; my hypothesis suggests that observations in L, for instance, will be more like each other than they will observations in C and R. There are some 0 observations, but not a great amount of them. (Edit: The data describes the number of articles over a time period by a news outlet on a given topic. Group is a characteristic of the news outlet.)
Because it's count data, I understand I can't really use a straightforward one way ANOVA, so what should I do?
Now the more complex version: I also have those observations in Count as a percentage of each individual across 20 different test cases. So this data looks more like:
Group Perc1 Perc2 ... PercN L .3 .04 .2 R .15 .6 .02 C .9 .04 .2 L .21 .08 .34 L .13 .75 .02
Etc. (Edit: Each row represents the proportion of that outlet's coverage on each topic measured. Perc1 = Count1 / (Sum(Count1..CountN) .)
What would be the best approach? I'm comfortable using R or Stata, whichever is best/easiest. This is somewhat similar to this post, but I'm not sure it fully applies.
Thank you in advance.