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

Autor/inn/en	Jansen, Malte; Lüdtke, Oliver; Robitzsch, Alexander
Titel	Disentangling different sources of stability and change in students' academic self-concepts. An integrative data analysis using the STARTS model. Gefälligkeitsübersetzung: Entflechtung verschiedener Quellen von Stabilität und Veränderung im akademischen Selbstkonzept von Schülerinnen und Schülern: Eine integrative Datenanalyse unter Verwendung des STARTS-Modells.
Quelle	In: Journal of educational psychology, 112 (2020) 8, S. 1614-1631Infoseite zur Zeitschrift PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	0022-0663; 1939-2176
DOI	10.1037/edu0000448
Schlagwörter	Längsschnittuntersuchung; Selbstkonzept; Längsschnittuntersuchung; Modell; Schüler; Sprachunterricht; Stabilität; Vorschulerziehung; Genetik; Datenanalyse; Schulerfolg; Modellierung; Zeitfaktor; Merkmal; Mathematisch-naturwissenschaftlicher Unterricht; Vorschulerziehung; Schüler; Schulerfolg; Feedback; Genetik; Sprachunterricht; Datenanalyse; Mathematisch-naturwissenschaftlicher Unterricht; Zeitfaktor; Individueller Unterschied; Merkmal; Modell; Modellierung; Stabilität
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, [the authors] 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. [The] 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. [The authors] 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. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/2