Testing the Reciprocal Effect between Value of Education, Time Investment, and Academic Achievement in a Large Non-Western Sample

Abstract

Many theories of motivation suggest that motivation and academic achievement reinforce each other over time, yet few longitudinal studies have examined behavioral pathways that may mediate interplay from motivation to achievement. Moreover, empirical studies so far have mostly focused on Western countries. In this study, we first examined whether students’ value of education, as a measure of motivation, is reciprocally related to achievement (class rank and self-rated performance) in a sample of junior high schoolers in an East-Asian country (N = 3445, Korean Youth Panel Study). We tested this reciprocity using different statistical models. Second, we investigated whether the relation between motivation and achievement was mediated by time invested in learning. Reciprocal effects between value of education and academic achievement were found in classic cross-lagged panel models, but only unilateral effects (from achievement to value of education) were found when we used random-intercept and random-curve cross-lagged panel models. Adding the time investment variable, the reciprocal effect between value of education, time investment, and academic achievement was found with the random intercept model. In conclusion, the reciprocity between of motivation and achievement are more elusive than previous research suggested; further studies should be dedicated to scrutinizing its existence with various statistical models.

Unraveling individual differences in learning potential: A dynamic framework for the case of reading development

“…In studying how multiple genetic-to-environmental factors interactively shape longitudinal change, determining cause and effect (or leading and lagging relationships) still poses a particular challenge. Bi-directional influences between factors of interest, such as academic performance and effort, across multiple longitudinal measurements, can be quantified using e.g., cross-lagged panel models ( Liu et al, 2023 , Usami et al, 2019 ). Comprehensive theoretical and analytical frameworks for understanding complex multifactorial interactions are provided by dynamic system approaches.…”