School effects on the development of motivation toward learning tasks and the development of academic self-concept in secondary education: a multivariate latent growth curve approach

Abstract

The main objective of the present study is to gain more insight into the school effects on the development of 2 noncognitive student outcomes, namely, the motivation toward learning tasks and the academic self-concept, and, more specifically, on the consistency of the school effects on these 2 outcomes. Data were drawn from the “Longitudinaal Onderzoek Secundair Onderwijs” (LOSO)-project and consisted of 2,687 students in 50 schools that were tested repeatedly at 4 different time points during secondary education (Grades 7–12). A multivariate multilevel latent growth curve model was used to analyze the data. The results showed that the school effects on the development of the noncognitive outcomes were considerable, and, more importantly, the school effects were larger on growth than on student status. Schools that were effective for the development of the motivation toward learning tasks also proved to be effective for the development of academic self-concept. However, the consistency of the school effects resulted largely from intake differences between schools.

Keywords:

• multivariate multilevel latent growth curve model
• motivation toward learning tasks
• academic self-concept
• secondary education

Notes

1. In the present study, we used the parametric approach to growth curve modelling by imposing a specific functional form to the data such as a straight line (linear growth curve) or a quadratic (growth) curve (Singer & Willett, 2003). A possible alternative approach entails the estimation of growth curves by freeing parameter loadings, and, thus, this approach does not impose a predefined functional form to the data. The latter approach allows for a more flexible incorporation of changes in trajectories over time, but it is more data driven than the former approach and, thus, requires very reliable data. The former (parametric) approach has the advantage that it provides numeric summaries of the trajectories (i.e., intercept, slope, quadratic term) that can be used for further research.

2. At some occasions, the kurtosis reached high values (see Table 1: values above 1), meaning that the distribution is peaked at some occasions. As kurtosis is a less important contributor than skewness to non-normality (Cole & Green, 1992), it can be assumed that the variables are approximately normally distributed.

3. The χ² difference tests were calculated by subtracting the χ²′s of Model 1 and 2, taking into account a correction factor. For more information, see the Technical Appendices of Mplus at www.statmodel.com.