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CONSTRUCTING JOB FAMILIES: AN ANALYSIS OF QUANTITATIVE TECHNIQUES USED FOR GROUPING JOBS
Author(s) -
COLIHAN JOE,
BURGER GARY K.
Publication year - 1995
Publication title -
personnel psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.076
H-Index - 142
eISSN - 1744-6570
pISSN - 0031-5826
DOI - 10.1111/j.1744-6570.1995.tb01771.x
Subject(s) - cluster (spacecraft) , set (abstract data type) , psychology , job analysis , affect (linguistics) , monte carlo method , computer science , statistics , social psychology , job satisfaction , mathematics , communication , programming language
A Monte Carlo study was conducted to examine the performance of several quantitative grouping strategies for the purpose of grouping jobs into job families. Two factors were found to substantially affect the accuracy of these grouping strategies in terms of identifying the correct number of families, and accurately classifying jobs into those families. Through simulation of job analysis data sets designed to reflect various underlying structures among a set of jobs, it was found that techniques based on the commonly used hierarchical cluster analysis model were relatively inaccurate when applied to data containing measurement error or overlap between job families. Alternatively, Q‐type factor analysis and hybrid techniques involving a combination of factor and cluster analysis proved to be viable and robust grouping strategies for job classification research.