Measurement and Prediction of Job Satisfaction

Abstract

Most prior research on the measurement and prediction of job satisfaction has utilized statistical tools that require the analysis to be cross-sectional. The results of these studies are based on interpersonal comparisons. However, it is well known that interpersonal utility comparisons cannot be rigorously interpreted. Thus, this study attempts to predict the level of job satisfaction for a new breed of professionals - the data processing professional - using discriminant analysis. Based upon past studies and the expectancy-value framework, various models were developed and tested to obtain a model with the maximum predictive ability. The implications of the results derived with the discriminant analysis are discussed.