This paper is intended to be a consideration of the role of multi–level modelling in educational research. It is not a guide on how to design or perform such an analysis. There are several references in the text to sources that teach the practicalities perfectly well, and the technique is anyway similar to other forms of regression and to analysis of variance. Rather, the paper describes what multi–level modelling is, why it is used, and what its limitations are. It does so in the hope that some readers will be enthused sufficiently to become appropriately critical ‘consumers’ of research using this approach, so building research capacity, and easing pressures on ‘specialist’ reviewers. Anyone who can read or perform standard multivariate analyses can understand, referee, or conduct a multi–level model. Additionally, the paper makes three key points. The generally small sample size in each cluster at the lowest level of any multi–level model means that there is a danger of a greater bias in the results than in standard analyses that pool the data from all clusters. Even where there are genuine gains through the use of multi–level models these have to be set against a loss in simple intuitive grasp of the results, especially amongst policy–makers and practitioners. Therefore, long term, we are probably better advised to improve our research designs and the quality of the data we collect than to focus on more and more complex forms of analysis to overcome deficiencies in the datasets we already have.