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

Peter W. Hill and Harvey Goldstein

Journal of Educational and Behavioral Statistics, 1998, vol. 23, issue 2, 117-128

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.

Keywords: Keywords: cross-classification; missing unit identification; multilevel; multiple membership model (search for similar items in EconPapers)
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.sagepub.com/doi/10.3102/10769986023002117 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:23:y:1998:i:2:p:117-128

DOI: 10.3102/10769986023002117

Access Statistics for this article

More articles in Journal of Educational and Behavioral Statistics
Bibliographic data for series maintained by SAGE Publications ().