Selecting and fitting graphical chain models to longitudinal data
Selecting and fitting graphical chain models to longitudinal data
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.
graphical modelling, path analysis, life course, health inequalities, attrition weights
715-738
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Berrington, Ann M.
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
April 2012
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Berrington, Ann M.
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Smith, Peter W.F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
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), .
(doi:10.1007/s11135-010-9407-8).
Abstract
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.
Text
BorSmtBer_QQ_eprints.pdf
- Accepted Manuscript
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Published date: April 2012
Keywords:
graphical modelling, path analysis, life course, health inequalities, attrition weights
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Local EPrints ID: 155247
URI: http://eprints.soton.ac.uk/id/eprint/155247
ISSN: 0033-5177
PURE UUID: 6aa31450-9279-43f6-a809-af762ba3d6a1
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Date deposited: 03 Jun 2010 10:37
Last modified: 14 Mar 2024 02:37
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Author:
Riccardo Borgoni
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