Selecting and fitting graphical chain models to longitudinal data
Borgoni, Riccardo, Berrington, Ann M. and Smith, Peter W.F. (2012) Selecting and fitting graphical chain models to longitudinal data. Quality and Quantity, 46, (3), 715-738. (doi:10.1007/s11135-010-9407-8).
- Accepted Manuscript
The aim of this paper is to demonstrate how graphical chain models can be used as effective tools in life course research focusing in particular on models for longitudinal prospective data. The substantive research question focuses on whether young motherhood is a pathway through which socio-economic disadvantage in childhood is related to poor self-reported health in adulthood among the 1970 British birth cohort. By breaking down large multivariate systems into simpler more tractable subcomponents and analysing them via local regressions, graphical models helps the understanding of complicated life course processes, show the intermediate relationships between predictors, and aid the understanding of the mechanisms through which potential confounding and mediating factors affect the outcome of interest.
|Digital Object Identifier (DOI):||doi:10.1007/s11135-010-9407-8|
|Keywords:||graphical modelling, path analysis, life course, health inequalities, attrition weights|
|Subjects:||H Social Sciences > HA Statistics|
|Divisions:||University Structure - Pre August 2011 > Southampton Statistical Sciences Research Institute
|Date Deposited:||03 Jun 2010 10:37|
|Last Modified:||31 Mar 2016 13:25|
Centre for Population Change: Understanding Population Change in the 21st Century
Funded by: ESRC (RES-625-28-0001)
Led by: Jane Cecelia Falkingham
1 January 2009 to 31 December 2013
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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