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Bayesian hierarchical modeling of the temporal dynamics of subjective well-being: A 10 year longitudinal analysis

journal contribution
posted on 2015-12-01, 00:00 authored by Jeromy AnglimJeromy Anglim, Melissa WeinbergMelissa Weinberg, Robert CumminsRobert Cummins
This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (. n=. 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

History

Journal

Journal of research in personality

Volume

59

Pagination

1 - 14

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0092-6566

eISSN

1095-7251

Language

eng

Publication classification

C Journal article; C1 Refereed article in a scholarly journal

Copyright notice

2015, Elsevier