TopicThe paper is situated in the domain of educational effectiveness research. This research domain is concerned with differences between schools in student outcomes. A wide variety of student outcomes are studied by educational effectiveness researchers. In recent years, student learning gains are seen as the most appropriate criterion to evaluate school effects. Moreover, many researchers are convinced that school effects are larger for student learning gains (student growth) than for student outcomes at one particular point in time (student status). This conviction stems mainly from the study of Raudenbush (1989, 1995) in which the school effect on student initial status for math was 14% whereas the school effect for math learning rates was over 80%.Objective This study examines the widely held belief that schools have a larger impact on their students growth than on their students status at a certain point in time. This idea is questioned through the application of multilevel growth curve models.How to best measure student growth and change has been a central concern in educational research. In recent years, the estimation of individual growth trajectories is widely seen as the appropriate way for modeling longitudinal data (Rogosa, 1995; Singer & Willett, 2003). These individual growth curves can be estimated by means of growth curve models.In the growth curve model, measurement occasions are nested within students and students are nested within schools. The fixed part of the model describes the average growth trajectory. The random part of the model describes the differences between students and the differences between schools. In fact, an individual growth trajectory is estimated for every student. In this study, the trajectory is defined by means of three parameters: the students' initial status, linear growth and quadratic growth. The school level variance (intraclass correlation) indicates the differences between schools regarding these three parameters. Data stem from a large-scale longitudinal research project in Flanders, the Dutch-speaking part of Belgium. More than 2500 students in 50 schools completed a questionnaire regarding their well-being and academic self-concept. This questionnaire was administered at four moments: at the end of seventh, eight, tenth and twelfth grade. Students also completed a language test (Dutch), five times during their school career.The results of the growth curve models are mixed. Some analyses confirm that the school effect is larger for student change parameters than for status, but the results are less spectacular than those of Raudenbush (1989; 1995). However, other analyses point in the opposite direction.These mixed results will be discussed, refering to the characteristics of the analyses, like the choice of the effectiveness criterion.Raudenbush, S.W. (1989). The analysis of longitudinal, multilevel data. International Journal of Educational Research, 13, 721-740. Raudenbush, S.W. (1995). Hierarchical linear models to study the effects of social context on development. In J.M. Gottman (Ed.), The analysis of change (pp. 165-201). Hillsdale, New Jersey: Lawrence Erlbaum. Rogosa, D.R. (1995). Myths and methods: "Myths about longitudinal research," plus supplemental questions. In: J.M. Gottman (Ed.), The analysis of change. Mahwah, NJ: Lawrence Erlbaum, pp. 3-65.Singer, J.D., & Willett, J.B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press.