​​A review of statistical methods for determination of relativ | InsightStream

​​A review of statistical methods for determination of relative importance of correlated predictors and identification of drivers of consumer liking

This article attempts to deliver the following message to the researchers and practitioners in the sensory field:

Theoretically, drivers of consumer liking is based on relative importance of explanatory variables in a linear model. The problem is complicated when the variables involve linear dependence, which is the common situation in sensory and consumer data.
The commonly used methodologies, e.g., conjoint analysis, preference mapping and Kano’s model, have serious limitations for determination of relative importance of correlated attributes and identification of drivers of consumer liking.
The conventional statistics, e.g., correlation coefficient, standard regression coefficient and P values of tests for regression parameters, etc., are inadequate and invalid measures of relative importance of correlated attributes.
There are three state-of-the-artmethods for determination of relative importance of correlated attributes. They are the Lindeman, Merenda and Gold’s method, Breiman’s Random Forest and Johnson’s relative weight.

This article also provides statistical background and almost exhaustive main references on the topic of relative importance of variables scattered in various academic journals in different fields. The information will help the sensometricians and researchers with more statistical knowledge to embrace the mainstream of the research on the topic and to pursue advanced methods for drivers of consumer liking.

Practical applications

This article reviews some new methods for determination of relative importance of correlated explanatory variables to response variable in a regression model. The methods can be used for identification of drivers of consumer liking. The article also provides the sources of the corresponding computer packages and codes implementing the new methods. The packages and codes are freely available and easy to use. The R packages “relaimpo” for the LMG method, “randomForest” and “party” for the original and modified Breiman’s Random Forest method are available at http://cran.r-project.org. The R or S-Plus code “johnson” for Johnson’s relative weight is available from the online supplementary Appendix S1 of this article.

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