Decision Sciences Journal
Volume 31, Number 3
Summer 2000
A Framework for Measuring the Importance of Variables with
Applications to Management Research and Decision Models
Ehsan S. Soofi
School of Business Administration, University of Wisconsin-Milwaukee,
P.O. Box 742, Milwaukee, WI 53201, email: esoofi@uwm.edu
Joseph J. Retzer
Maritz Marketing Research, 1415 W. 22nd Street, Suite 800, Oak
Brook, IL 60523, email: retzerjj@maritz.com
Masoud Yasai-Ardekani
School of Business Administration, University of Wisconsin-Milwaukee,
P.O. Box 742, Milwaukee, WI 53201, email: yasai@uwm.edu
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.
Subject Areas: Analysis of Variance, Entropy, Linear
Regression, Logit Analysis, Multivariate Statistics, and Planning/Strategy. |