Score Distributions of the Job Descriptive Index: An Evaluation of the Effects of Transformation

Abstract

Watson, Watson, and Stowe (1985) demonstrated that scores on some scales of the Job Descriptive Index (JDI) might not be normally distributed. As such, they recommended data transformation prior to parametric analysis of JDI scores. The present study used a Monte Carlo simulation to investigate the effect of data transformation on average correlations between JDI scores. Three degrees of nonnormality were investigated for different sample sizes and different magnitudes of correlation. In general, the results indicated that transformation of scores did not result in marked changes in the correlations. Contrary to the Watson et al. study, the present study revealed that transformation of JDI scores is probably not worthwhile.