The application of a facet scale job satisfaction model for environmental health officers in Australia and Scotland

Abstract

The direction provided from published research emphasises that job satisfaction is multi-dimensional, and the associations with work related and non-work related predictor variables as being complex. The isolation of the discrete influence of individual predictor variables is therefore difficult. The application of multivariate analysis incorporating hierarchical stepdown protocols and interaction effects offers one approach that provides for the discrimination between the explanatory characteristics of these variables. Principal Axis Factoring with oblimin rotation and Structural Equations Modelling were applied to survey data to develop a Measurement Model comprising six latent constructs that characterised the job satisfaction of Environmental Health Officers (EHOs) in Australia. The model was successfully applied to data from the EHOs in Scotland. This paper presents the application of the Measurement Model to examine the association between selected work content and the demographic aspects of the EHOs in Australia and in Scotland, and their job satisfaction. The analysis protocol included Canonical Correlation, Multiple Linear Regression and Factorial Manova and Mancova. The unique variance explained by the work content factors and the demographic aspects of the job were similar. The linear representation of model pathways provided more comprehensive explanatory power than the curvilinear association. The presentation of evidence of multidimensional complexity, including interaction effects, underscored the need for caution before generalising on the influence of individual factors on job satisfaction. The analysis protocol presented in this paper can be applied to other situations in environmental health involving multivariate complexity.