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 the proposed project, we will deepen the understanding of how these techniques work and thereby contribute to the development of approaches for estimating MLMs. Specifically, we will propose a method that allows for optimal estimation of MLMs in small samples. This method will yield estimates that are more accurate than any standard software. We will implement this method within the Maximum Likelihood, Bayesian, and factor score regression frameworks. In addition, we will make the optimized procedures available by publishing an R package, which will allow researchers to obtain more accurate estimates, particularly when sample sizes are 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