I have some multilevel-data-structure, where I want to regress top 10 music chart listings (dependent variables: downloads and rank) on some song characteristics (Xi). The problem for me now comes from my specific setting where I want to use top 10 charts from different countries and for different months. So as far I understand multilevel modelling I have to cluster the individual entries into the corresponding countries (top 10 chart entry nested in country top 10 charts). So basically my data structure is:
Country Song-ID Rank Downloads X1 X2 X3 … Month USA a 1 100000 male_singer indie_label Award_yes January USA b 2 90000 female_singer major_label Award_no January USA c 3 80000 male_singer major_label Award_yes January … China b 1 150000 female_singer major_laber Award_no January China a 2 80000 male_singer indie_label Award_yes January China d 3 45000 female_singer indie_label Award_yes January … S.Africa c 1 75000 male_singer major_label Award_yes February S.Africa d 2 55000 female_singer indie_label Award_yes February S.Africa a 3 40000 male_singer indie_label Award_yes February …
As far as I understand this relates to multiple membership and/or a crossed level setting. But what gives me a real headache is how to treat the possible multiple outcomes for the individual songs.
I wanted to try out brms/lme4 in R and from the according manuals and help files I figured out how to use them (also for the multiple membership case). But as I stated, the possibility of different outcome variables confuses me a lot. I´m absolutely not sure if my approach of using a multilvel model is adequate and also how to write down the corresponding formulas in these packages or how to set up an adequate dataframe for this analysis.
Thank you very much for your help in advance.