A framework for measuring the importance of variables with applications to management research and decision models

A framework for measuring the importance of variables with applications to management research and decision models

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Article ID: iaor20031796
Country: United States
Volume: 31
Issue: 3
Start Page Number: 595
End Page Number: 625
Publication Date: Jul 2000
Journal: Decision Sciences
Authors: , ,
Abstract:

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.

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