I am analyzing survey data collected among plant breeders. I want to know if there is any association between some of the variables we collected, namely their perception of climate change and the region where they work. I have 200 answers from breeders who belong to one out of 6 regions. They were asked about 10 climate change parameters (drought, rainfall, temperature, etc.). For each of these, their answers could be "increased", "no change", "decreased".
I want to know if breeders' perception of each climate variable is significantly related to their region of work. I'd like to know this to be able to say, for example, that breeders working in East Asia are observing a greater increase in rainfall than those in Central America.
I am working in R.
I have tried creating contingency tables between breeders' regions and each climate variable using CrossTab from the
gmodels package, but I am not sure how to interpret the output (in a table with so many relationships, what does the significance level apply to?). Also, most of my cells (well over 60%) have expected frequencies < 5 which I understand makes the test less reliable.
I have tried fisher test but I get error messages about a too small workspace, which I am not sure how to solve, and in any case I don't know if this is a good test either.
I would appreciate your help in choosing the best statistical method for my objective.