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JOB ANALYSIS MODELS AND JOB CLASSIFICATION
Author(s) -
CORNELIUS EDWIN T.,
CARRON THEODORE J.,
COLLINS MARIANNE N.
Publication year - 1979
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.1744-6570.1979.tb02341.x
Subject(s) - job analysis , psychology , task (project management) , job performance , cluster analysis , job characteristic theory , job design , hierarchical clustering , social psychology , artificial intelligence , computer science , job satisfaction , management , economics
Recent research in job classification has focused on the appropriate data analysis model for analyzing the similarities and differences among jobs. In the present research, the data analysis model is held constant, and the type of job analysis data is varied to examine the effect on the resulting job classification decisions. Seven foremen jobs in a chemical processing plant were analyzed using three different levels of job analysis data: task‐oriented, worker‐oriented, and abilities‐oriented. All three sets of data were analyzed using the same hierarchical clustering procedure. Results indicated that the number and type of resulting job clusters was clearly dictated by the type of job analysis data that was used to compare the foremen jobs. Practical implications of these findings are presented.