Comparing Methods of Exploratory Data Analysis for the Moral Foundations Questionnaire with a Small Sample

Abstract

When applied in a principled exploratory manner Data Mining and Machine Learning (DM/ML) can generate new insights and inform future research. This paper compares traditional regression with DM/ML methods to determine the best method for interpreting a small data set and develop insights to health professional’s moral foundations. Forward, Backward, Hierarchical, and Elastic-Net regression with k-fold Cross Validation and Bootstrapping were compared. Elastic-Net regression outperformed the traditional methods, Cross Validation and Bootstrapping produced comparable outcomes. Elastic-Net was used to determine a model of moral foundations for health professional students. DM/ML methods are appropriate for use with a psychological measure, small sample size, and present new opportunities for psychological research. Determination of sample size, all data exclusions, all manipulations, and all measures in the study are reported.