> table(indvar_FODs$categorical_FOD_FODs) ascending descending identity 1683 1432 206 > table(indvar_FODs$Condition_FODs) A B C D 880 880 880 880 > table(indvar_FODs$Language_used_FODs) English German Hungarian Italian Turkish 880 160 160 160 2160 categorical_FOD_FODs is the dependent variable. please apply multinomial logistic regression with interaction effect Treat each level of the Condition_FODs variable (4 levels) and Language_used_FODs variable (5 levels) equally. include the interaction effect of variables in the analysis.

# http://www.ats.ucla.edu/stat/r/dae/mlogit.htm
# http://www.ats.ucla.edu/stat/r/faq/mlogit.htm
library(mlogit)
mlogit(categorical_FOD_FODs ~ 1 | Condition_FODs + Language_used_FODs , data = indvar_FODs, method = "nr", print.level = 0)
#> Warning: The following arguments have been renamed: to --> data
#> Warning: The following arguments have been renamed: to --> data
#> 
#> Call:
#> mlogit(formula = categorical_FOD_FODs ~ 1 | Condition_FODs + 
#>     Language_used_FODs, data = indvar_FODs, method = "nr", 
#>     print.level = 0)
#> 
#> Coefficients:
#>             (Intercept)  Condition_FODsA  Condition_FODsB 
#