Authors: Jussi Tanskanen Sakari Taipale Timo Anttila
Publish Date: 2014/12/11
Volume: 17, Issue: 1, Pages: 367-387
Abstract
Previous 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 nonparametric 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 nonlinear modeling can reveal regarding the relationship between work engagement and the three predictors in question Secondly the article compares the explanatory power of nonlinear and linear modeling with regard to work engagement The generalized additive model GAM that makes possible nonlinear modeling is compared with the widely used simply linear generalized linear model GML procedure Based on the survey data N = 7867 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 Ushaped 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
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