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

Autor:	Gary K. Burger, Joe Colihan
Rok vydání:	1995
Předmět:	Set (abstract data type) Organizational Behavior and Human Resource Management Job analysis Job classification Data mining Disease cluster Psychology computer.software_genre computer Applied Psychology Hierarchical clustering
Zdroj:	Personnel Psychology. 48:563-586
ISSN:	1744-6570 0031-5826
DOI:	10.1111/j.1744-6570.1995.tb01771.x
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::57db159fbc3b4136298083988f05efa0 https://doi.org/10.1111/j.1744-6570.1995.tb01771.x Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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