Disentangling different sources of stability and change in students’ academic self-concepts: An integrative data analysis using the STARTS model.

Abstract

Academic self-concept (ASC) is characterized by the dual nature of stability and change. That is, students strive for consistency in their self-concept but also receive achievement feedback that leads to changes in ASC. Only a few previous studies have scrutinized the stability of ASC. The STARTS model (Stable, AutoRegressive Trait, and State) disentangles three sources of variation that underlie individual differences in a construct across time: (a) a time-invariant stable component, (b) a time-varying, partly stable component, and (c) an occasion-specific state component. This study is the first to analyze the stability of ASC with the STARTS model. Rather than selecting a single data set, we followed the idea of using an integrative data analysis (IDA) and applied the STARTS model to 11 longitudinal studies that included more than 20,000 students. Our results show that there is a substantial proportion of stable trait variance in both mathematical (26%) and verbal self-concept (24%)—that is, some sources of individual differences in ASC are completely stable (e.g., genes, preschool environment). The largest part of the variation in ASC across time could be attributed to factors that systematically changed in an autoregressive way (e.g., achievement feedback). Mathematical self-concept showed higher stability than verbal self-concept as a result of a smaller proportion of occasion-specific state variance. The IDA also revealed substantial heterogeneity across studies. We argue that disentangling stable and temporally changing aspects of ASC is important not only for informing theory but also for assessing the potential of psychological interventions. (PsycInfo Database Record (c) 2020 APA, all rights reserved)