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

27 Pages Posted: 21 Aug 2021

See all articles by Efrem Violato

Efrem Violato

Northern Alberta Institute of Technology, Centre for Advanced Medical Simulation; Northern Alberta Institute of Technology, Centre for Advanced Medical Simulation

Date Written: August 19, 2021

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.

Keywords: Data Mining; Machine Learning; Moral Foundations; Psychometrics; Health Sciences

Suggested Citation

Violato, Efrem, Comparing Methods of Exploratory Data Analysis for the Moral Foundations Questionnaire with a Small Sample (August 19, 2021). Available at SSRN: https://ssrn.com/abstract=3908201 or http://dx.doi.org/10.2139/ssrn.3908201

Efrem Violato (Contact Author)

Northern Alberta Institute of Technology, Centre for Advanced Medical Simulation ( email )

126, 11762 106 St NW
Edmonton, Alberta T6E 6J9
Canada

Northern Alberta Institute of Technology, Centre for Advanced Medical Simulation ( email )

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