The use of artificial neural networks and multiple linear regression in modeling work-health relationships: translating theory into analytical practice

Karanika-Murray, M ORCID: https://orcid.org/0000-0002-4141-3747 and Cox, T, 2010. The use of artificial neural networks and multiple linear regression in modeling work-health relationships: translating theory into analytical practice. European Journal of Work and Organisational Psychology, 19 (4), pp. 461-486. ISSN 1359-432X

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Abstract

Although psychological theory acknowledges the existence of complex systems and the importance of nonlinear effects, linear statistical models have been traditionally used to examine relationships between environmental stimuli and outcomes. The way we analyse these relationships does not seem to reflect the way we conceptualize them. The present study investigated the application of connectionism (artificial neural networks) to modelling the relationships between work characteristics and employee health by comparing it with a more conventional statistical linear approach (multiple linear regression) on a sample of 1003 individuals in employment. Comparisons of performance metrics indicated differences in model fit, with neural networks to some extent outperforming the linear regression models, such that R 2 for worn-out and job satisfaction were significantly higher in the neural networks. Most importantly, comparisons revealed that the predictors in the two approaches differed in their relative importance for predicting outcomes. The improvement is attributed to the ability of the neural networks to model complex nonlinear relationships. Being unconstrained by assumptions of linearity, they can provide a better approximation of such psychosocial phenomena. Nonlinear approaches are often better fitted for purpose, as they conform to the need for correspondence between theory, method, and data.

Item Type:	Journal article
Publication Title:	European Journal of Work and Organisational Psychology
Creators:	Karanika-Murray, M. and Cox, T.
Publisher:	Routledge for the European Association of Work and Organizational Psychology
Date:	2010
Volume:	19
Number:	4
ISSN:	1359-432X
Identifiers:	Number Type 10.1080/13594320902995916 DOI
Divisions:	Schools > School of Social Sciences
Record created by:	EPrints Services
Date Added:	09 Oct 2015 09:58
Last Modified:	09 Jun 2017 13:15
URI:	https://irep.ntu.ac.uk/id/eprint/5776

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