This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Browne, W. J Articles by Rasbash, J. Search for Related Content Social Bookmarking                         What's this?

Multiple membership multiple classification (MMMC) models

Institute of Education, University of London, London, UK, w.browne{at}ioe.ac.uk

Harvey Goldstein

Institute of Education, University of London, London, UK

Jon Rasbash

Institute of Education, University of London, London, UK

In the social and other sciences many data are collected with a known but complex underlying structure. Over the past two decades there has been an increase in the use of multilevel modelling techniques that account for nested data structures. Often however the underlying data structures are more complex and cannot be fitted into a nested structure. First, there are cross-classified models where the classifications in the data are not nested. Secondly, we consider multiple membership models where an observation does not belong simply to one member of a classification. These two extensions when combined allow us to fit models to a large array of underlying structures. Existing frequentist modelling approaches to fitting such data have some important computational limitations. In this paper we consider ways of overcoming such limitations using Bayesian methods, since Bayesian model fitting is easily accomplished using Monte Carlo Markov chain (MCMC) techniques. In examples where we have been able to make direct comparisons, Bayesian methods in conjunction with suitable ‘diffuse’ prior distributions lead to similar inferences to existing frequentist techniques. In this paper we illustrate our techniques with examples in the fields of education, veterinary epidemiology, demography, and public health illustrating the diversity of models that fit into our framework.

Key Words: multilevel modelling • hierarchical modelling • Monte Carlo Markov chain (MCMC) • cross-classified models • multiple membership models • complex data structures • Bayesian GLMM modelling

Statistical Modelling, Vol. 1, No. 2, 103-124 (2001)
DOI: 10.1177/1471082X0100100202


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				M. Callens and C. Croux Poverty Dynamics in Europe: A Multilevel Recurrent Discrete-Time Hazard Analysis International Sociology, May 1, 2009; 24(3): 368 - 396. [Abstract] [PDF]

				W. J Browne, R. H McCleery, B. C Sheldon, and R. A Pettifor Using cross-classified multivariate mixed response models with application to life history traits in great tits (Parus major) Statistical Modeling, October 1, 2007; 7(3): 217 - 238. [Abstract] [PDF]

				H. May and J. A. Supovitz Capturing the Cumulative Effects of School Reform: An 11-Year Study of the Impacts of America's Choice on Student Achievement Educational Evaluation and Policy Analysis, January 1, 2006; 28(3): 231 - 257. [Abstract] [PDF]

				T. Chandola, P. Clarke, R. D Wiggins, and M. Bartley Who you live with and where you live: setting the context for health using multiple membership multilevel models J Epidemiol Community Health, February 1, 2005; 59(2): 170 - 175. [Abstract] [Full Text] [PDF]

				D. Oberwittler A Multilevel Analysis of Neighbourhood Contextual Effects on Serious Juvenile Offending: The Role of Subcultural Values and Social Disorganization European Journal of Criminology, April 1, 2004; 1(2): 201 - 235. [Abstract] [PDF]