  This paper presents a method of exploring the relative predictive importance
of individual variables in multicollinear data sets at three levels of
significance: strong importance, moderate importance, and no importance.
Implementation of Bonferroni adjustment to control for Type I error in the
method is described, and results with and without the correction are compared.
An example of the method in binary logistic modeling is demonstrated by using a
set of 20 acoustic features to discriminate vocalic nasality in the speech of
six speakers of the Mixean variety of Low Navarrese Basque. Validation of the
method is presented by comparing the direction of significant effects to those
observed in separate logistic mixed effects models, as well as goodness of fit
and prediction accuracy compared to partial least squares logistic regression.
The results show that the proposed method yields: (1) similar, but more
conservative estimates in comparison to separate logistic regression models,
(2) models that fit data as well as partial least squares methods, and (3)
predictions for new data that are as accurate as partial least squares methods.
