Examining the utility of nonlinear machine learning approaches versus linear regression for predicting body image outcomes: The U.S. Body Project I.

Journal: Body image
Published Date: Jun 1, 2022

Abstract

Most body image studies assess only linear relations between predictors and outcome variables, relying on techniques such as multiple Linear Regression. These predictor variables are often validated multi-item measures that aggregate individual items into a single scale. The advent of machine learning has made it possible to apply Nonlinear Regression algorithms-such as Random Forest and Deep Neural Networks-to identify potentially complex linear and nonlinear connections between a multitude of predictors (e.g., all individual items from a scale) and outcome (output) variables. Using a national dataset, we tested the extent to which these techniques allowed us to explain a greater share of the variance in body-image outcomes (adjusted R) than possible with Linear Regression. We examined how well the connections between body dissatisfaction and dieting behavior could be predicted from demographic factors and measures derived from objectification theory and the tripartite-influence model. In this particular case, although Random Forest analyses sometimes provided greater predictive power than Linear Regression models, the advantages were small. More generally, however, this paper demonstrates how body image researchers might harness the power of machine learning techniques to identify previously undiscovered relations among body image variables.

Authors

  • Dehua Liang
    Schmid College of Science and Technology, Chapman University, United States; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, United States.
  • David A Frederick
    Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA. Electronic address: dfrederi@chapman.edu.
  • Elia E Lledo
    Fowler School of Engineering, Chapman University, Orange, CA, USA.
  • Natalia Rosenfield
    Fowler School of Engineering, Chapman University, Orange, CA, USA.
  • Vincent Berardi
    Fowler School of Engineering, Chapman University, Orange, CA, USA; Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, USA.
  • Erik Linstead
    Schmid College of Science and Technology, Chapman University, Orange, CA, USA.
  • Uri Maoz
    Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA; Computational Neuroscience, Health and Behavioral Sciences and Brain Institute, Chapman University, Orange, CA 92866, USA; Institute for Interdisciplinary Brain and Behavioral Sciences, Chapman University, Orange, CA 92866, USA; Department of Anesthesiology, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA.

Keywords

External Resources

Popular Topics
Recent Journals