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Abstract This editorial introduces a special issue of Large-Scale Assessments in Education (LSAE) that addresses key challenges in analyzing longitudinal data from large-scale studies. These challenges include ensuring fair measurement across time, developing common metrics, and correcting for measurement errors. The special issue highlights recent methodological innovations, particularly for studies like the National Education Panel Study (NEPS), providing approaches for improving the accuracy and robustness of longitudinal educational research. The papers in this issue present advances in methods for estimating trends, incorporating background information, and analyzing longitudinal relationships between constructs. Innovative approaches such as Bayesian modeling for borrowing historical information, continuous-time models for capturing developmental trends, and plausible value estimation provide practical solutions for researchers working with complex longitudinal data. In addition, the issue presents new software tools that facilitate the implementation of these advanced methodologies. Together, these papers contribute to both the theory and practice of educational assessment and provide valuable insights for those working with longitudinal data in national and international panel studies.

The investigation of learning processes by assessing students’ experience along with objective characteristics within a classroom context has a long tradition in empirical learning process research (e.g. Sembill, 1984 et passim; Wild & Krapp, 1996). However, most of the existing studies confine themselves to psychological variables that seem to be too narrowly considered, as there is theoretical and empirical evidence proving the involvement of somatic and psychological processes in learning and in stress reactions. Furthermore there is a lack of studies that investigate situation-related experience (states) as an outcome of interactions between relatively stable characteristics (traits) and continuously changing “objective” context conditions. Against this background, we will present an approach for cross-classified multilevel longitudinal modelling of person-situation interactions in naturalistic educational settings. We illustrated our model with the example of students’ stress experience referring to empirical data that we measured within a multidisciplinary research project (pedagogy, psychology, adolescent medicine). 53 students at a public German vocational school were investigated during 9 lessons. There are up to 38 state measurements per person, resulting in 2,014 measurements in total. Taking into account that states are nested within persons and within situations we applied a cross-classified multilevel model to analyse effects on students’ stress experience. The analysis shows significant person-situation interactions between academic self-concept and classroom demands and between baseline cortisol concentration and classroom demands: the relation between classroom demands and stress experience depends on relatively stable person-related characteristics. A deeper knowledge about the complex interrelations between traits, states, and continuously changing context conditions seems to be essential for a more holistic understanding of learning at school and for the identification of crucial aspects for an evidence-based design and implementation of teaching and learning arrangements.