I am pursuing my master's thesis in Global Health at Uppsala University. One step in my statistical analysis involves "consumption of ultra-processed food (UPF)" as the independent variable (having 3 mutually exclusive categories: number of subjects who consumed 1-2 UPFs, 3-4 UPFs, and 5 UPFs in the last 24 hours) and "dietary diversity" as the dependent variable (having 2 mutually exclusive categories: adequate dietary diversity and inadequate dietary diversity). The dependent variable was actually a (discrete, numerical) score, the dietary diversity score, ranging from 0 to 10; that was dichotomized by a cut-off score of 5- those scoring 5 and above categorized as having adequate dietary diversity and those scoring below 5 as having inadequate dietary diversity (cut-off based on WHO recommendation).
My questions are:
What test should I choose to test for statistical significance? My guess is Pearson's Chi-squared test; as I have the assumptions fulfilled by the data.
What co-efficient should I choose to reflect the strength of association? I read about the following two options: Goodman-Kruskal's Lambda and bias-corrected Cramer's V. I am not sure which one to use, or there is another more appropriate option, as I don't have the in-depth knowledge of statistics to assess the strength and weakness of these to co-efficients. I am really confused here.
Sincere thanks in advance.