Abstract
Most research approaches operationalize persistence through a static index, neglecting that persistence is inherently dynamic. The study aimed to analyze persistence as a dynamic process through a real-time modeling approach. More specifically, we computed a latent profile analysis to examine temporal-behavioral dynamics in a persistence task. The Swiss sample consisted of N = 241 children (Mage = 71.8 months, 52.3% female, 21% migration background). During a persistence task, task engagement was repeatedly coded in 10-s intervals, yielding 18 indicators of persistence. The results revealed three behavioral profiles: a highly persistent profile (70%), a declining persistence profile (19%), and a low persistence profile (11%). Furthermore, covariate analyses revealed profile differences in terms of executive functions and behavioral problems.
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