# Multilevel model with responses only at level 2

I have hierarchical data of individuals nested into families. For each individual, I have independent variables such as age, gender, education, and familiarity with product. For each family unit, I also have covariates such as household income, purchase behavior, and distance to retail centers.

The dependent satisfaction measure is only recorded at the family level. More specifically, satisfaction is asked of a head-of-household respondent, who ideally represents the household. While satisfaction is measured on a 5-point scale, we typically re-express it as dichotomous (top 2 box).

I would like to take into consideration the individual-level effects as well as the family-level effects in modeling product satisfaction propensity. Is it appropriate to explore multilevel modeling when the outcome is only measured at the second level? If not, is there a different approach I should be following?

Basically the approach that they outline involves computing adjusted group means on the predictor variables and then regressing the outcome on the adjusted group means. The adjusted group mean for each group is the best linear unbiased predictor (BLUP) of the predictor variable for that group. You can compute those using equations given in the paper or, if you're using R, using the lme4 package and its coef() function.
• @Amw5G No, that would just yield the simple means, not the adjusted (shrunk) means. But you could use something like mod <- lmer(X ~ 1 + (1|groupID)); coef(mod) – Jake Westfall Sep 1 '15 at 23:42