The reporting of findings from Programme for International Student Assessment (PISA) is an important part of educational monitoring in Germany. However, until now, the subsample of students with special educational needs (SEN) had been too small to single out this group and report findings. In PISA 2012, the sample of 9th-grade students in Germany was thus expanded by students with SEN in inclusive settings and students with SEN in an oversample of 49 special schools. This article describes and compares the proficiency of students with SEN in inclusive settings and in special schools. In all 3 PISA domains assessing literacy, reading, mathematics, and science, students with SEN in inclusive settings achieve proficiency Level 2, whereas students with SEN in special schools achieve proficiency Level 1. It turns out that students with SEN have a lower average socioeconomic status than regular students, especially those in special schools. Possible explanations for the higher achievement of students with SEN in inclusive settings are discussed.

In der PISA-Studie 2018 wurde als innovative Domäne erstmals Global Competence bei fünfzehnjährigen Schülerinnen und Schülern erfasst. In dieser Zusatzerhebung werden das selbsteingeschätzte Wissen von Schülerinnen und Schülern zu Themen mit lokaler und globaler Bedeutung (z. B. Klimawandel, Armut, Pandemien) sowie ihre von ihnen berichteten Einstellungen zu globalen und interkulturellen Themen in den Blick genommen. Dabei geht es beispielsweise um den respektvollen Umgang mit Menschen unterschiedlicher nationaler Herkunft und entsprechend ethnischem, religiösem, sozialem oder kulturellem Hintergrund. Diese Broschüre stellt die Ergebnisse der Schülerinnen und Schüler in Deutschland aus der Zusatzauswertung Global Competence bei der PISA-Studie 2018 vor und betrachtet diese im internationalen Vergleich. Zusätzlich werden die Sicht der Schulleitungen und Lehrkräfte in den verschiedenen Schularten sowie die Sicht der Eltern einbezogen.

Background Standard methods for analysing data from large-scale assessments (LSA) cannot merely be adopted if hierarchical (or multilevel) regression modelling should be applied. Currently various approaches exist; they all follow generally a design-based model of estimation using the pseudo maximum likelihood method and adjusted weights for the corresponding hierarchies. Specifically, several different approaches to using and scaling sampling weights in hierarchical models are promoted, yet no study has compared them to provide evidence of which method performs best and therefore should be preferred. Furthermore, different software programs implement different estimation algorithms, leading to different results. Objective and method In this study, we determine based on a simulation, the estimation procedure showing the smallest distortion to the actual population features. We consider different estimation, optimization and acceleration methods, and different approaches on using sampling weights. Three scenarios have been simulated using the statistical program R. The analyses have been performed with two software packages for hierarchical modelling of LSA data, namely Mplus and SAS. Results and conclusions The simulation results revealed three weighting approaches performing best in retrieving the true population parameters. One of them implies using only level two weights (here: final school weights) and is because of its simple implementation the most favourable one. This finding should provide a clear recommendation to researchers for using weights in multilevel modelling (MLM) when analysing LSA data, or data with a similar structure. Further, we found only little differences in the performance and default settings of the software programs used, with the software package Mplus providing slightly more precise estimates. Different algorithm starting settings or different accelerating methods for optimization could cause these distinctions. However, it should be emphasized that with the recommended weighting approach, both software packages perform equally well. Finally, two scaling techniques for student weights have been investigated. They provide both nearly identical results. We use data from the Programme for International Student Assessment (PISA) 2015 to illustrate the practical importance and relevance of weighting in analysing large-scale assessment data with hierarchical models.

Das Skalenhandbuch für das Programme for International Student Assessment (PISA) 2018 umfasst die Dokumentation aller Instrumente, die in der Studie im Jahre 2018 in Deutschland eingesetzt wurden. Neben den Fragen – den sogenannten Items – des Schülerfragebogens, des Elternfragebogens, des Lehrerfragebogens sowie des Schulleiterfragebogens werden die mittleren nationalen Lösungshäufigkeiten und dazugehörige Kenngrößen der verwendeten Testaufgaben für die Domänen Lesen, Naturwissenschaften und Mathematik aufgelistet. In der Erhebung 2018 bildete die Domäne Lesen mit der Neuerung des adaptiven Testens die Hauptdomäne. Damit beginnt ein neuer Zyklus, da in den vorhergehenden Zyklen jede der drei untersuchten Grundbildungsdomänen Lesen, Mathematik und Naturwissenschaften zweimal den inhaltlichen Schwerpunkt bildete. Es werden sowohl die internationalen Fragen als auch Fragen abgebildet, welche nur in Deutschland erhoben worden sind.

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