A Monte Carlo Simulation on Nonlinear Effects of Likert-Scaled Items With Machine Learning

• In Event: Data Science in Education

Abstract

Although linear effects of predictors are predominant in social science research, nonlinear effects are often reported. Focusing on the nonlinear effects of Likert-scaled items, we investigated how different coding schemes (dummy coding or not) and machine learning methods (group Mnet or random forest) affect models’ prediction measures and variable selection in a Monte Carlo simulation. Other conditions to emulate large-scale survey data included linearity (linear, quadratic, cubic, and piecewise constant), density of true predictors in item parcels (concentrated or scattered) and symmetry in responses (symmetric or asymmetric). Beyond providing interpretable prediction models as a linear method, group Mnet either outperformed or was on par with random forest in terms of prediction and variable selection regardless of condition combinations.

Authors

• Tai Sun Jeong, Korea Institute for Curriculum and Evaluation

• Jin Eun Yoo, Korea National University of Education

• Hyeong Gwan Kim, Korea National University of Education