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dc.contributor.authorTanskanen, Jussi
dc.contributor.authorTaipale, Sakari
dc.contributor.authorAnttila, Timo
dc.date.accessioned2016-02-12T05:39:46Z
dc.date.available2016-02-12T05:39:46Z
dc.date.issued2016
dc.identifier.citationTanskanen, J., Taipale, S., & Anttila, T. (2016). Revealing Hidden Curvilinear Relations Between Work Engagement and Its Predictors : Demonstrating the Added Value of Generalized Additive Model (GAM). <em>Journal of Happiness Studies</em>, 17 (1), 367-387. <a href="http://dx.doi.org/10.1007/s10902-014-9599-z">doi:10.1007/s10902-014-9599-z</a>
dc.identifier.otherTUTKAID_64347
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/48753
dc.description.abstractPrevious studies measuring different aspects of the quality of life have, as a rule, presumed linear relationships between a dependent variable and its predictors. This article utilizes non-parametric statistical methodology to explore curvilinear relations between work engagement and its main predictors: job demands, job control and social support. Firstly, the study examines what additional information non-linear modeling can reveal regarding the relationship between work engagement and the three predictors in question. Secondly, the article compares the explanatory power of non-linear and linear modeling with regard to work engagement. The generalized additive model (GAM), that makes possible non-linear modeling, is compared with the widely used simply linear generalized linear model (GML) procedure. Based on the survey data (N = 7,867) collected in eight European countries in 2007, the article presents the following main results. GAM clearly fitted the data better than GLM. All investigated job characteristics had curvilinear relationships with work engagement, although job demands and job control relationships were almost linear. Social support had a clear U-shaped curvilinear connection to work engagement. Interactions between the three job characteristics were also found. Interaction between job demands and social support was curvilinear in shape. Finally, GAM proved to be a more practical and efficient tool of analysis than GLM in situations where there are reasons to assume curvilinear relationships, complex interactions effects between predictors.
dc.language.isoeng
dc.publisherSpringer Netherlands; International Society for Quality of Life Studies
dc.relation.ispartofseriesJournal of Happiness Studies
dc.subject.otherwork engagement
dc.subject.othergeneralized additive model (GAM)
dc.subject.otherjob demands
dc.subject.otherjob control
dc.subject.othersocial support
dc.subject.otherKarasek’s model
dc.titleRevealing Hidden Curvilinear Relations Between Work Engagement and Its Predictors : Demonstrating the Added Value of Generalized Additive Model (GAM)
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201602111551
dc.contributor.laitosYhteiskuntatieteiden ja filosofian laitosfi
dc.contributor.laitosDepartment of Social Sciences and Philosophyen
dc.contributor.oppiaineYhteiskuntapolitiikka
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2016-02-11T13:15:03Z
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange367-387
dc.relation.issn1389-4978
dc.relation.volume17
dc.type.versionacceptedVersion
dc.rights.copyright© Springer Science+Business Media Dordrecht 2014. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
dc.rights.accesslevelopenAccess
dc.relation.doi10.1007/s10902-014-9599-z


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