Latent Class Analysis of Students’ Science Motivation: A Comparison Between the United States and China

Abstract

Using latent class analysis (LCA), we examine the potential ways of classifying students’ science motivation in the United States and China using data from PISA 2015. Based on a set of nine observed variables of science motivation, we identify three subgroups of cases varied in their internal patterns of motivation, covering, respectively, 24.78%, 12.85%, and 62.37% of the entire sample size. Instead of classifying students into groups with a linear increase in motivation scores, latent class analysis shows that there are students who feel pure enjoyment in learning science but do not associate science with their future careers (Class 1), students who do not like learning science but believe science is important to their future (Class 2), and students who have both high enjoyment and the prospect of doing science for a living in the future (Class 3). Multinomial logistic regression reveals that science motivation groups are significantly affected by gender, nationality, and family background.