Multilevel modeling of complex data structures with multiple unit membership and missing unit identifications - ARCHIVED

This paper presents a method for handling longitudinal data in which individuals belong to more than one unit at a higher level, and also where there is missing information on the identification of the units to which they belong. In education, for example, a student might be classified as belonging sequentially to a particular combination of primary and secondary school, but for some students, the identity of either the primary or secondary school may be unknown. Likewise, in a longitudinal study, students may change school or class from one period to the next, so 'belonging' to more than one higher level unit. The procedures used to model these stuctures are extensions of a random effects cross-classified multilevel model.