Abstract
Previous research that has evaluated the effectiveness of personality variables
for predicting work performance has predominantly relied on methods designed to
detect simple relationships. The research reported in this thesis employed artificial
neural networks a method that is capable of capturing complex nonlinear and
configural relationships among variables and the findings were compared to those
obtained by the more traditional method of linear regression.
Six datasets that comprise a range of occupations, personality inventories, and
work performance measures were used as the basis of the analyses. A series of studies
were conducted to compare the predictive performance of prediction equations that a)
were developed using either artificial neural networks or linear regression, and b)
differed with respect to the type and number of personality variables that were used as
predictors of work performance. Studies 1 and 2 compared the two methods using
individual personality variables that assess the broad constructs of the five-factor model
of personality. Studies 3 and 4 used combinations of these broad variables as the
predictors. Study 5 employed narrow personality variables that assess specific facets of
the broad constructs. Additional methodological contributions include the use of a
resampling procedure, the use of multiple measures of predictive performance, and the
comparison of two procedures for developing neural networks.
Across the studies, it was generally found that the neural networks were rarely
able to outperform the simpler linear regression equations, and this was attributed to the
lack of reliable nonlinearity and configurality in personality-work performance
relationships. However, the neural networks were able to outperform linear regression
in the few instances where there was some independent evidence of nonlinear or
configural relationships. Consequently, although the findings do not support the
usefulness of neural networks for specifically improving the effectiveness of personality
variables as predictors of work performance, in a broader sense they provide some
grounds for optimism for organisational researchers interested in applying this method
to investigate and exploit complex relationships among variables.