CONSTRUCTING JOB FAMILIES - AN ANALYSIS OF QUANTITATIVE TECHNIQUES USED FOR GROUPING JOBS

A Monte Carlo study was conducted to examine the performance of severa l quantitative grouping strategies for the purpose of grouping jobs in to job families. Two factors were found to substantially affect the ac curacy of these grouping strategies in terms of identifying the correc t number of families, and accurately classifying jobs into those famil ies. Through simulation of job analysis data sets designed to reflect various underlying structures among a set of jobs, it was found that t echniques based on the commonly used hierarchical cluster analysis mod el were relatively inaccurate when applied to data containing measurem ent error or overlap between job families. Alternatively, Q-type facto r analysis and hybrid techniques involving a combination of factor and cluster analysis proved to be viable and robust grouping strategies f or job classification research.