Researchers in the field of learning and instruction explore the relationships between the determinants of learning and learning success as well as the causal mechanisms that mediate these relationships. Based on knowledge about these relationships and causal mechanisms, they develop guidelines that help to design learning environments and learning processes. To this end, researchers typically collect data with a multilevel structure in which individuals are nested within clusters; for example, students are nested within classrooms. Thus, multilevel modeling (MLM) is a prominent modeling approach because it allows researchers to account for this multilevel structure.
With this completed project, we deepened the understanding of how these techniques work and thereby contributed to the development of approaches for estimating MLMs. Specifically, we proposed a method that allows for optimal estimation of MLMs in small samples. This method yielded estimates that were more accurate than any standard software. We implemented this method within the Maximum Likelihood, Bayesian, and factor score regression frameworks. In addition, we made the optimized procedures available by publishing an R package, which allowed researchers to obtain more accurate estimates, particularly when sample sizes were small.
Research areas: Optimal Estimation
Project Members: Valerii Dashuk, Martin Hecht, Steffen Zitzmann
Funders: German Research Foundation (DFG), Medical School Hamburg (MSH)
Funding Period: 2022-2025