A Framework for Measuring the Importance of Variables with Applications to Management Research and Decision Models*
综合了统计学、心理测量学和管理科学中衡量变量相对重要性的方法，提出了一个基于最大熵原理的分析框架，并通过组织设计案例展示了其应用。
In many disciplines, including various management science fields, researchers have shown interest in assigning relative importance weights to a set of explanatory variables in multivariable statistical analysis. This paper provides a synthesis of the relative importance measures scattered in the statistics, psychometrics, and management science literature. These measures are computed by averaging the partial contributions of each variable over all orderings of the explanatory variables. We define an Analysis of Importance (ANIMP) framework that reflects two desirable properties for the relative importance measures discussed in the literature: additive separability and order independence. We also provide a formal justification and generalization of the “averaging over all orderings” procedure based on the Maximum Entropy Principle. We then examine the question of relative importance in management research within the framework of the “contingency theory of organizational design” and provide an example of the use of relative importance measures in an actual management decision situation. Contrasts are drawn between the consequences of use of statistical significance, which is an inappropriate indicator of relative importance and the results of the appropriate ANIMP measures.