Multilevel Modeling of Educational Data With Cross-Classification and Missing Identification for Units

Abstract

This paper presents a method for handling educational data in which students belong to more than one unit at a given level, but there is missing information on the identification of the units to which students belong. For example, a student might be classified as belonging sequentially to a particular combination of primary and secondary school, but for some students the identify of either the primary or the secondary school may be unknown. Similar situations arise in longitudinal studies in which students change school or class from one year to the next. The method involves setting up a cross-classified model, but replacing (0, 1) values for unit membership with weights reflecting probabilities of unit membership in cases where membership information is randomly missing. The method is illustrated with reference to longitudinal data on students’ progress in English.