Methods for intensive longitudinal data

Intensive longitudinal data are microprocess data that can be used to track people's current experiences and behavior, e.g., in everyday life or during learning. In order for this method to generate and interpret valid data, methodological developments are necessary in various areas.

Process diagnostic measurement instruments

In order to investigate specific processes of everyday experience that underlie the developmental trends or effects found in long-term studies, survey instruments are needed that can be used to validly capture situational experiences and behavior in specific moments of everyday life.

We develop such process diagnostic instruments for various content areas, such as momentary motivation (Dietrich, Viljaranta, Moeller, & Kracke, 2017) or professional and political identity (Dietrich, Lichtwarck-Aschoff, & Kracke, 2013).

Research designs

We also develop research designs for microprocess studies in educational contexts. These contexts are particularly complex, as pupils or students learn different subjects and attend different courses taught by different teachers. We have developed various research designs for this purpose (e.g., Moeller, Viljaranta, Kracke, & Dietrich, 2020). These designs make it possible to distinguish between situation-specific, person-specific, and context-specific influencing factors and to determine which effects are specific to specific learners or contexts and which effects can be generalized more broadly.

Evaluation methods

Intensive longitudinal data also require special evaluation methods. There are currently rapid developments in this area. On the one hand, we focus on methods that can describe the heterogeneity of different individuals, situations, and contexts. On the other hand, we are working on methods that can be used to investigate systematic developmental changes in everyday processes. 

Publications

Moeller, J., Dietrich, J., Jähne, M. F., & Nörenberg, L. (2024). Dynamics of achievement motivation in concrete situations questionnaire (DYNAMICS-Q). Retrieved from https://osf.io/kycav/

Moeller, J., Dietrich, J., & Baars, J. (2024). The Experience Sampling Method in the research on achievement-related emotions and motivation. In R.C. Lazarides, G. Hagenauer, & H. Järvenoja (Eds.), Motivation and emotion in learning and teaching across educational contexts: Theoretical and methodological perspectives and empirical insights (pp. 178–196). Routledge. https://doi.org/10.4324/9781003303473-14

Dietrich, J., Schmiedek, F., & Moeller, J. (2022). Learning happens in learning situations, so let's study them. Introduction to the special issue. Learning & Instruction, 81, 101623. https://doi.org/10.1016/j.learninstruc.2022.101623

Moeller, J., Viljaranta, J., Kracke, B. & Dietrich, J. (2020). Disentangling objective characteristics of learning situations from subjective perceptions thereof, using an experience sampling method design. Frontline Learning Research, 8, 63-84. https://doi.org/10.14786/flr.v8i3.529

Dietrich, J. (2019). Methoden der Veränderungsmessung. In B. Kracke & P. Noack (Eds.), Handbuch Entwicklungs- und Erziehungspsychologie. Heidelberg, Berlin: Springer VS. https://doi.org/10.1007/978-3-642-53968-8_32

Reitzle, M. & Dietrich, J. (2019). From between-person statistics to within-person dynamics. Diskurs Kindheits- und Jugendforschung, 14, 323-342. https://doi.org/10.3224/diskurs.v14i3.06

Dietrich, J., Lichtwarck-Aschoff, A., & Kracke, B. (2013). Deciding on a college major: Commitment trajectories, career exploration, and academic well-being. Diskurs Kindheits- und Jugendforschung , 8, 305-318.