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Multilevel Cross-Classified Models

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  • HARVEY GOLDSTEIN

    (Institute of Education, London)

Abstract

Random cross-classifications of units can arise at any level of a data hierarchy. For example, school students may be classified both by the schools they attend and their neighborhoods of residence. This article explores the issues of efficiently modeling such data and gives an example from a study of parental choice of schools.

Suggested Citation

  • Harvey Goldstein, 1994. "Multilevel Cross-Classified Models," Sociological Methods & Research, , vol. 22(3), pages 364-375, February.
    • Handle: RePEc:sae:somere:v:22:y:1994:i:3:p:364-375
    DOI: 10.1177/0049124194022003005
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    Cited by:

    1. Alastair H. Leyland & Øyvind Næss, 2009. "The effect of area of residence over the life course on subsequent mortality," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(3), pages 555-578, June.
    2. Bornmann, Lutz & Mutz, Rüdiger & Hug, Sven E. & Daniel, Hans-Dieter, 2011. "A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants," Journal of Informetrics, Elsevier, vol. 5(3), pages 346-359.
    3. Matthew Quick, 2019. "Multiscale spatiotemporal patterns of crime: a Bayesian cross-classified multilevel modelling approach," Journal of Geographical Systems, Springer, vol. 21(3), pages 339-365, September.
    4. Ajai S Gaur & Chinmay Pattnaik & Deeksha Singh & Jeoung Yul Lee, 2019. "Internalization advantage and subsidiary performance: The role of business group affiliation and host country characteristics," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 50(8), pages 1253-1282, October.
    5. Putzhammer, Moritz & Fainshmidt, Stav & Puck, Jonas & Slangen, Arjen, 2018. "To elevate or to duplicate? Experiential learning, host-country institutions, and MNE post-entry commitment increase," Journal of World Business, Elsevier, vol. 53(4), pages 568-580.

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