A Multivariate Methodology for Simultaneously Capturing and Clustering Judgment Policies
提出一种基于典型相关分析的线性模型方法，可同时聚类具有同质决策策略的评判者并区分策略维度，相比传统回归聚类法能更好处理多重共线性等问题，并以信息系统招聘官的政策捕获为例验证。
ABSTRACT This paper presents a new linear model methodology for clustering judges with homogeneous decision policies and differentiating dimensions which distinguish judgment policies. This linear policy capturing model based on canonical correlation analysis is compared to the standard model based on regression analysis and hierarchical agglomerative clustering. Potential advantages of the new methodology include simultaneous instead of sequential consideration of information in the dependent and independent variable sets, decreased interpretational difficulty in the presence of multicollinearity and/or suppressor/moderator variables, and a more clearly defined solution structure allowing assessment of a judge's relationship to all of the derived, ideal policy types. An application to capturing policies of information systems recruiters responsible for hiring entry‐level personnel is used to compare and contrast the two techniques.