Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology

Jeffrey D. Long; Rolf Loeber; David P. Farrington

doi:10.1080/00273170802620071

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Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology

Journal article

Peer reviewed

Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology

Jeffrey D. Long, Rolf Loeber and David P. Farrington

Multivariate behavioral research, Vol.44(1), pp.28-58

01/01/2009

DOI: 10.1080/00273170802620071

PMCID: PMC2893373

PMID: 20592941

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https://www.ncbi.nlm.nih.gov/pmc/articles/2893373View

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Abstract

Two models for the analysis of longitudinal binary data are discussed: the marginal model and the random intercepts model. In contrast to the linear mixed model (LMM), the two models for binary data are not subsumed under a single hierarchical model. The marginal model provides group-level information whereas the random intercepts model provides individual-level information including information about heterogeneity of growth. It is shown how a type of numerical averaging can be used with the random intercepts model to obtain group-level information, thus approximating individual and marginal aspects of the LMM. The types of inferences associated with each model are illustrated with longitudinal criminal offending data based on N = 506 males followed over a 22-year period. Violent offending indexed by official records and self-report were analyzed, with the marginal model estimated using generalized estimating equations and the random intercepts model estimated using maximum likelihood. The results show that the numerical averaging based on the random intercepts can produce prediction curves almost identical to those obtained directly from the marginal model parameter estimates. The results provide a basis for contrasting the models and the estimation procedures and key features are discussed to aid in selecting a method for empirical analysis.

Mathematical Methods In Social Sciences

Mathematics

Mathematics, Interdisciplinary Applications

Physical Sciences

Psychology

Psychology, Experimental

Science & Technology

Social Sciences

Social Sciences, Mathematical Methods

Statistics & Probability

Details

Title: Subtitle: Marginal and Random Intercepts Models for Longitudinal Binary Data With Examples From Criminology
Creators: Jeffrey D. Long - University of Minnesota
Rolf Loeber - University of Pittsburgh
David P. Farrington - University of Cambridge
Resource Type: Journal article
Publication Details: Multivariate behavioral research, Vol.44(1), pp.28-58
DOI: 10.1080/00273170802620071
PMID: 20592941
PMCID: PMC2893373
NLM abbreviation: Multivariate Behav Res
ISSN: 0027-3171
eISSN: 1532-7906
Publisher: Taylor & Francis
Number of pages: 31
Grant note: 411018 / National Institute of Drug Abuse; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute on Drug Abuse (NIDA) 96-MU-FX-0012 / Office of Juvenile Justice and Delinquency Prevention R01MH073841 / NATIONAL INSTITUTE OF MENTAL HEALTH; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) 50778 / National Institute of Mental Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH)
Language: English
Date published: 01/01/2009
Academic Unit: Psychiatry; Biostatistics
Record Identifier: 9984280838002771

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