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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 classdoi:10.5296/ire.v8i2.17030 fatcat:bnu2fiwalnh2zi3dfxj5s25ptm