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Latent Class Analysis to Identify Parental Involvement Styles in Chinese Children's Learning at Home.

Xiaorui HuangRandall E SchumackerBin-Bin ChenMing Ming Chiu
Published in: Behavioral sciences (Basel, Switzerland) (2022)
Parental involvement is one of the most important factors affecting students' academic learning. Different families seem to show similar parental involvement patterns. This study employed a representative sample of 12,575 seventh- and eighth-grade Chinese students' parents to explore the patterns of parental involvement. (2) Methods: Latent class analysis (LCA) was used to identify different parental involvement styles in children's studies at home. Discriminant analysis, MANOVA, post-hoc tests, and effect size were used to verify the LCA results. (3) Results: Four distinctive latent class groups were identified and named: supportive (20%), permissive (54%), restrictive (8%), and neglectful (18%). A discriminant analysis supported the LCA group classification results. The MANOVA results indicated statistically significant differences between the four latent classes using the set of predictor variables. The post-hoc test results and effect sizes showed that the predictor variables had substantial differences among the four latent class groups. Parental education and family income showed statistically significant links to these four parental involvement styles, which, in turn, were linked to students' academic achievement according to the MANOVA, effect sizes, and post-hoc test results. (4) Conclusions: Parental involvement styles in children's learning at home can be identified and categorized into four different latent class styles.
Keyphrases
  • young adults
  • machine learning
  • mental health
  • sensitive detection