The ReMa-IDA contains four internal traineeships. The internal traineeships allow students to acquire hands-on experience with diverse aspects of research. Each traineeship has a different primary goal, covering knowledge and skills required throughout all phases of a research project in the following sequence: knowledge/skill acquisition (Traineeship 1); research design (Traineeship 2); data analysis and reporting (Traineeship 3); and peer-reviewing (Traineeship 4).
In the Internal Traineeship 3, students will gain expertise in data analysis and writing sections of a manuscript that must include the reporting of methods and results. It is necessary that the traineeship involves the acquisition of a new skill(i.e., not just the application of a data analytic technique that the student already knows).
In practice, the third traineeship gives students the opportunity to work hands-on with data while developing new analytical skills and learning how to communicate their results in a clear and structured way. This is the perfect opportunity for students to build on their methodological interests or explore entirely new techniques. Additionally, as this traineeship is placed at the start of Year 2, it also presents students with a great opportunity to learn new data analysis skills that they can apply during their Master’s Thesis.
Students have chosen to learn about a broad range of analytical approaches, often in combination with different types of data. Examples include experience sampling methodology (ESM) and other longitudinal analyses, Bayesian multilevel models, factor analytic techniques, or netword models such as Gaussian graphical or Ising models. Others work with qualitative approaches, such as inductive thematic analysis or qualitative comparative analysis.
Some projects focus on more technical statistical analyses, such as Monte Carlo simulation studies, multivariate correlational meta-analyses, item response theory (IRT) models, Mokken scale analysis, or evidence accumulation modeling. Students have also worked on topics such as measurement invariance, reliable change indices, bifactor modeling, and estimation performance.
In addition, students are increasingly engaging with data-driven and computation approaches. For examples, projects may involve supervised machine learning techniques, natural language processing, classification trees, or ensemble methods such as random forests.
The types of data analysed are also diverse. Some students have worked with intesenive longitudinal data, large panel datasets and simulated data, while others used qualitative materials such interviews, narratives, or focus group data.