When different responses/outcomes are correlated this lends itself to a multivariate multilevel data structure in which level-1 are sets of response variables measured on individuals at level-2, nested in neighborhoods at level-3. The key feature is that the set of responses (outcomes) is nested within individuals. The response could be a set of outcomes that relate to, for instance, different aspects of health behavior (e.g., smoking and drinking). Crucially, such responses could be a mixture of ‘quality’ (do you smoke/do you drink) and ‘quantity’ (how many/how much). A multilevel structure on different aspects of health behavior could include measurements (e.g., smoking and drinking, both at level-1), nested within individuals (at level-2), within neighborhoods (at level-3).
The substantive benefit of this approach is that it is possible to assess whether different types of behavior are related to individual characteristics in the same or different ways. Moreover, the residual co-variances at level-2 and level-3 measure the ‘correlation’ of behaviors between individuals and between places. Additionally, we can ascertain whether neighborhoods that are high for one behavior are also high for another; and whether neighborhoods with high prevalence of smoking, for instance, are also high in terms of the number of cigarettes smoked.
Technical benefits flow in terms of efficiency if the response is correlated and if there are many missing responses, as in matrix sample designs. Figure 3(c) presents a structure where the responses at level-1 capture different aspects of health behaviors and Figure 3(d) portrays the idea of ‘mixed’ (quality and quantity) responses on a particular aspect of health behavior.