Hierarchical linear models for research on professional learning
relevance and implications
pp. 337-368
in: Stephen Billett, Christian Harteis, Hans Gruber (eds), International handbook of research in professional and practice-based learning, Berlin, Springer, 2014Abstract
The goal of this book chapter is twofold. Because research on professional learning using Hierarchical Linear Modelling (HLM) is scarce, the first goal of this book chapter is to familiarise the readers with HLM. The opportunities, assumptions, and limitations of these techniques will be discussed and illustrated with an authentic dataset. Secondly, this chapter will focus on the relevance and implications of HLM for research in the field of professional learning and training: Why and when should this method be adopted within research on professional learning? Which conditions should be fulfilled? What are the advantages of this technique in general and which advantages are specifically relevant for the proposed field of research?This chapter will provide a basic introduction into HLM. HLM, also known as multilevel modelling, is a statistical technique that takes the nested structure of the data into account. First, HLM will be introduced from a conceptual point of view and discussed why and when it could be applied. Subsequently, the different steps in HLM analysis will be presented and illustrated with an authentic dataset of a study investigating employees' learning intentions. The following section will focus on prior research in the field of professional learning that applied HLM. Finally, the main conclusions and future research perspectives will be provided.