Gender Differences in the Development of Growth and Fixed Mindsets Among Chinese Adolescents: A Cross-Lagged Panel Network Analysis.

Authors

Shuting Li, Yang Gao, Sha Tao, Daoyang Wang

Published in

Journal of adolescence. Oct 19, 2026. Epub Oct 19, 2026.

Abstract

Students' mindsets about the malleability of their abilities shape their academic performance, social adaptation, and psychological well-being, making it important to track how these beliefs change during adolescence. Although mindsets undergo substantial change in this period, most studies have used latent variable models that ignore the dynamic interactions among mindset items. Examining how specific items change over time can thus reveal how mindsets develop and whether this process differs between boys and girls.
This study examined the temporal links among the 10 growth mindset items and the 10 fixed mindset items, identifying the central beliefs within each network, in 15,386 students (40% girls; $M_{\mathrm{age}}$  = 16.73 years) from 31 secondary vocational schools in China, followed annually over 3 years. Mixed-design ANOVA and cross-lagged panel network (CLPN) analysis were used to compare mindset development between genders.
Boys consistently reported higher aggregate scores on both growth and fixed mindsets than girls, though these differences were small. The CLPN analysis showed gender-specific trajectories: girls showed earlier and more pronounced changes in the growth mindset network, whereas boys showed slower integration of growth-related beliefs. By contrast, the fixed mindset networks developed similarly across genders. Item-level cross-lagged associations were small, and network comparisons were descriptive.
Conceptualizing mindsets as interconnected networks, this study identifies gender-specific developmental patterns in mindset organization among Chinese vocational students, showing how mindset items co-develop over time and suggesting candidate targets for gender-sensitive interventions in the future.

PMID:
42895174
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.