Industrial Psychology : goodness of fit? Fit for goodness?

Inaugural lecture--Dept. of Industrial Psychology, University of Johannesburg, 6 November 2006This paper represents a critical reflection on the relevance of industrial psychology. Against a historical-developmental background of the discipline, the inquiry questions its goodness of fit, i.e. its contribution to organisation and society. It is found that the fit is limited to its relevance for inwardly focused organisational behaviour due to its endorsement of the instrumental (strategic) motives of organisations that subscribe to an owner/shareholder agenda. Industrial psychology’s potential fit for goodness is explored with a view to enhance its relevance in an era of goodness. Scientific and practical interaction between industrial psychology and business ethics is suggested to facilitate movement away from a descriptive approach. The heuristics of reflection, resolve, research and resources are suggested to facilitate movement towards a normative (multiple stakeholder) paradigm aimed at broad based goodness and sustainability. Lastly, the potential risks inherent to an application of the heuristics are accounted for

Goodness of Fit: an axiomatic approach

An axiomatic approach is used to develop a one-parameter family of measures of divergence between distributions. These measures can be used to perform goodness-of-fit tests with good statistical properties. Asymptotic theory shows that the test statistics have well-defined limiting distributions which are however analytically intractable. A parametric bootstrap procedure is proposed for implementation of the tests. The procedure is shown to work very well in a set of simulation experiments, and to compare favourably with other commonly used goodness-of-fit tests. By varying the parameter of the statistic, one can obtain information on how the distribution that generated a sample diverges from the target family of distributions when the true distribution does not belong to that family. An empirical application analyses a UK income data set.Goodness of fit; axiomatic approach; measures of divergence; parametric bootstrap

Goodness-of-fit criteria for survival data

The definition of an appropriate measure for goodness-of-fit in case of survival data comparable to R^2 in linear regression is difficult due to censored observations. In this paper, a variety of answers based on different residuals and variance of survival curves are presented together with a newly introduced criterion. In univariate simulation studies, the presented criteria are examined with respect to their dependence on the value of the coefficient associated with the covariate; underlying covariate distribution and censoring percentage in the data. Investigation of the relations between the values of the different criteria indicates strong dependencies, although the absolute values show high discrepancies and the criteria building processes differ substantially