DETERMINING JOB GROUPS: APPLICATION OF HIERARCHICAL AGGLOMERATIVE CLUSTER ANALYSIS IN DIFFERENT JOB ANALYSIS SITUATIONS
研究比较了五种分层凝聚聚类方法在四种模拟工作数据中的准确性，发现平均连接/距离法适用于任务高度正相关的工作，而平均连接/相关法适用于其他结构，帮助研究者选择合适方法。
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.