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Multilevel growth curve models for repeated measures data have become increasingly popular and stand as a flexible tool for investigating longitudinal change in students’ outcome variables. In addition, these models allow the estimation of school effects on students’ outcomes though making strong assumptions about the serial independence of level-1 residuals. This paper introduces a method which takes into account the serial correlation of level-1 residuals and also introduces such serial correlation at level-2 in a complex double serial correlation (DSC) multilevel growth curve model. The results of this study from both real and simulated data show a great improvement in school effects estimates compared to those that have previously been found using multilevel growth curve models without correcting for DSC for both the students’ status and growth criteria.

Multilevel growth curve models are becoming invaluable in educational research because they model changes in student outcomes efficiently. The coding of the time variable in these models plays a crucial role as illustrated in this study for the case of a three-level quadratic growth curve model. This paper shows clearly how the choice of a time coding affects school effects estimates and their interpretation. A new definition for school effects for growth curve models with random intercepts and slopes is proposed. This study recommends that the choice of a time coding should not only be based on the ease of interpretation and model convergence but also on its consequences on the student status and growth parameter estimates. The current application illustrates that in general the school effects for student growth in well-being and language achievement in secondary school, are greater for student growth than for student status.

Literature on the combination of qualitative and quantitative research components at the primary empirical study level has recently accumulated exponentially. However, this combination is only rarely discussed and applied at the research synthesis level. The purpose of this paper is to explore the possible contribution of mixed methods research to the integration of qualitative and quantitative research at the synthesis level. In order to contribute to the methodology and utilization of mixed methods at the synthesis level, we present a framework to perform mixed methods research syntheses (MMRS). The presented classification framework can help to inform researchers intending to carry out MMRS, and to provide ideas for conceptualizing and developing those syntheses. We illustrate the use of this framework by applying it to the planning of MMRS on effectiveness studies concerning interventions for challenging behavior in persons with intellectual disabilities, presenting two hypothetical examples. Finally, we discuss possible strengths of MMRS and note some remaining challenges concerning the implementation of these syntheses. ispartof: Quality and Quantity vol:47 issue:2 pages:659-676 status: published

This article investigates the potential of multilevel confirmatory factor analysis compared to using conventional confirmatory factor analysis, as regards the construction of measures for school process variables within educational effectiveness research. Since both techniques have not been systematically compared within educational research, we propose to contrast several aspects: factor structure, pattern of factor loadings, communalities of the indicators, variance extracted by the factors, internal consistency of the factors, and inter-factor correlations. To this end, teacher questionnaire data are used to construct measures for school process variables. Our results show no substantial differences when comparing both techniques at the teacher level. However, when contrasting the different levels within the more complex technique, we notice a fairly large discrepancy between the teacher-level results and the school-level results.

This paper reports on an exploration of student wellbeing in secondary school. A wellbeing questionnaire was administered four times to the same students. Multilevel models were applied in which measurements are grouped within students within schools. Differences between students are large, but there are only minor differences between schools regarding the wellbeing. Two methods of analysis of longitudinal data are compared: a multilevel multivariate approach and a multilevel growth curve analysis. It is shown that the estimation of individual growth curves is an elegant and parsimonious way of modelling. The multivariate approach on the other hand is a more modest model. The assumptions, advantages and disadvantages of both perspectives are listed.