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Neural Network Analysis of Nonlinear Effects of Hardiness on Burnout in Chinese Nurses
Author(s) -
Felix Ladstätter,
Eva Garrosa,
Junming Dai
Publication year - 2014
Publication title -
open journal of social sciences
Language(s) - English
Resource type - Journals
eISSN - 2327-5960
pISSN - 2327-5952
DOI - 10.4236/jss.2014.25019
Subject(s) - hardiness (plants) , stressor , psychology , artificial neural network , burnout , nonlinear system , cluster (spacecraft) , social psychology , computer science , clinical psychology , artificial intelligence , biology , physics , quantum mechanics , cultivar , horticulture , programming language
Substantial research attention is evident in the hardiness and related literature concerning the topic of moderational effects of hardiness on work-related stressors and strains. In this research mostly linear methods have been used to analyze these moderational effects. However, it is not very likely that these effects are purely linear. The present study uses a neural network, a method which can model nonlinear relationships, to analyze the effects of hardiness. A cluster analysis of 268 Chinese nurses based on their self-ratings in the hardiness dimensions of commitment, challenge, and control was performed. Two groups of individuals were identified, consisting of (1) those who scored above average and (2), those who scored below average on all hardiness dimensions. On the basis of these clusters, a multi-layer neural network was used to analyze the data.

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