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Materialism, Egocentrism and Delinquent Behavior in Chinese Adolescents in Mainland China: A Short-Term Longitudinal Study.

Daniel Tan Lei ShekDiya DouXiaoqin ZhuXiang LiLindan Tan
Published in: International journal of environmental research and public health (2022)
Although research generally showed that holding materialistic beliefs would lead to poor developmental outcomes, few studies have used adolescent delinquency as an outcome measure. In addition, the intervening processes between materialism and adolescent developmental outcomes are unclear. In particular, it is not clear how materialistic beliefs influence egocentrism and adolescent delinquency. Methodologically, the existing studies have several weaknesses, including small samples, cross-sectional research designs, and being limited to people living in Western cultures. Using two waves of data collected from Sichuan, China (N = 4981), we studied the predictive effect of adolescent materialism on delinquency and the mediating role of egocentrism. Over two occasions separated by six months, students aged 11 and above responded to a questionnaire evaluating adolescent materialism, egocentrism, and delinquency (mean Wave 1 age = 13.15, range between 11 and 20.38). Results of multiple regression analyses suggested that materialism at Time 1 positively predicted Time 2 egocentrism. Additionally, Time 1 materialism positively predicted the level and change in Time 2 delinquency. Finally, based on 5000 bootstrap samples with gender, age, ethnic group, and Time 1 delinquent behavior as covariates, PROCESS analyses showed that egocentrism partially mediated the influence of Time 1 materialism delinquency and its change at Time 2. This study suggests that materialistic beliefs shape egocentrism, which further strengthens adolescent delinquent behavior. This study also replicates the findings of a pioneer study in China reported previously.
Keyphrases
  • young adults
  • mental health
  • cross sectional
  • south africa
  • childhood cancer
  • metabolic syndrome
  • machine learning
  • artificial intelligence
  • data analysis