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DETERMINING JOB GROUPS: APPLICATION OF HIERARCHICAL AGGLOMERATIVE CLUSTER ANALYSIS IN DIFFERENT JOB ANALYSIS SITUATIONS
Author(s) -
GARWOOD MARCIA K.,
ANDERSON LANCE E.,
GREENGART BARRY J.
Publication year - 1991
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.1744-6570.1991.tb00697.x
Subject(s) - hierarchical clustering , task (project management) , linkage (software) , cluster (spacecraft) , multilevel model , psychology , job analysis , range (aeronautics) , hierarchical database model , cluster analysis , computer science , statistics , social psychology , data mining , mathematics , artificial intelligence , job satisfaction , management , engineering , economics , programming language , biochemistry , chemistry , gene , aerospace engineering
The present study showed that researchers must consider underlying data structure when using hierarchical agglomerative cluster analysis to group jobs. Five cluster procedures were applied to four simulated data sets constructed to reflect common job analysis situations. The structures contained jobs varying in degree of task overlap, number of tasks performed, and relative number of people doing the jobs. Average linkage/distance was the most accurate procedure when jobs had highly positively correlated task profiles, a situation characteristic of jobs within a career family over a restricted range of levels. Average linkage/correlation was the most accurate for three other structures containing jobs whose profiles were not highly positively correlated. Such are characteristically found when analyzing (a) jobs in different functional units, (b) jobs over a wide range of hierarchical levels such as entry to advanced, and (c) jobs differing markedly in the number of incumbents.