# Determine which group is more important

I have a question how to analyze mean differences where I am unsure whether I am lacking the statistical vocabulary or simply ignoring something important.

I have a 5 point satisfaction variable (very satisfied to totally unsatisfied) as dependent variable and three group independent variable to predict the satisfaction. The independent variable has three groups: "Top 2 users", "Neutral", "Bottom 2" - these groups are defined according to some other detailed satisfaction questions which are aggregated before (e. g. "Do you like your telephones color? Do you like the camera? etc.). I have used a Oneway test to determine whether or not there are differences in the first place - and the differences are highly significant and very obvious (mean in the Top 2 group at 1.9 and in the Bottom 2 group 4, ignoring the "neutral" group which is not of real interest).

But know my supervisor asked me which of the two groups is more important - e.g. is it more important to gain Top 2 users or to avoid having Bottom 2 users. I am unsure if I can answer the question with the data I have at hand. Do you have an idea if this question is even answerable? I have calculated the effect size, which is quite strong with -0.76, but is this really a good answer to the question at hand?

My method to get the effect size:

df is a dataframe, var is the independent variable, testvar the dependent variable.

getEffectSize <-function(df, var, testvar)
{
out <- by(df[,testvar],df[,var], stat.desc)

effectSize <- mes(out[[1]][["mean"]], out[[3]][["mean"]], out[[1]][["std.dev"]], out[[3]][["std.dev"]], out[[1]][["nbr.val"]], out[[3]][["nbr.val"]])

print(effectSize)
}