Data analysis in medical education research: a multilevel perspective

A substantial part of medical education research focuses on learning in teams (e.g., departments, problem-based learning groups) or centres (e.g., clinics, institutions) that are followed over time. Individual students or employees sharing the same team or centre tend to be more similar in learning than students or employees from different teams or centres. In other words, when students or employees are nested within teams or centres, there is a within-team or within-centre correlation that should be taken into account in the analysis of data obtained from individuals in these teams or centres. Further, when individuals are measured several times on the same performance (or other) variable, these repeated measurements tend to be correlated, that is: we are dealing with an infra-individual correlation that should be taken into account when analyzing data obtained from these individuals. In such a study context, many researchers resort to methods that cannot account for intra-team and/or infra-individual correlation and this may result in incorrect conclusions with regard to effects and relations of interest. This comparison paper presents the benefits which result from adopting a proper multilevel perspective on the conceptualization and estimation of effects and relations of interest.