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The stability of the twofold multidimensionality of academic self-concept: A study of Chinese secondary school students

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Introduction The present investigation examined the stability of the twofold multidimensional structure of academic self-concepts (ASCs) in three domains, namely Chinese, math, and general school using four-wave data collected over… Click to show full abstract

Introduction The present investigation examined the stability of the twofold multidimensional structure of academic self-concepts (ASCs) in three domains, namely Chinese, math, and general school using four-wave data collected over 2 years among 552 Chinese secondary school students. Method Adopting both a within-network and a between-network approach, confirmatory factor analyses (CFAs) and factor correlations were performed in Mplus 8.2. Results The within-network results showed that CFA models wherein competence and affect dimensions were conflated generated unacceptable fit. In contrast, the CFAs in which competence and affect were modeled as separate latent factors consistently produced superior fit to the data. The between-network results demonstrated that in the Chinese and math domains and across the four-time waves, the competence components were more strongly related to the achievements in matching domains than the affect components were. Furthermore, both the competence and affect components of ASCs and achievements were positively correlated in the non-matching domains, which were somewhat contradictory to the internal/external frame of reference model predicting zero or negative relations. Discussion Such results seem to suggest more involvement in social comparison than in dimensional comparison of Chinese students, which might be attributed to the collectivistic Chinese culture and the common phenomenon of academic social comparisons among Chinese adolescents in schools.

Keywords: academic self; chinese secondary; stability twofold; secondary school; school; school students

Journal Title: Frontiers in Psychology
Year Published: 2023

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