The need for considering multilevel analysis in CSCL research - An appeal for the use of more advanced statistical methods

12 Abstract Per definition, CSCL research deals with the data of individuals nested in groups, 13 and the influence of a specific learning setting on the collaborative process of learning. 14 Most well-established statistical methods are not able to analyze such nested data 15 adequately. This article describes the problems which arise when standard methods are 16 applied and introduces multilevel modelling (MLM) as an alternative and adequate 17 statistical approach in CSCL research. MLM enables testing interactional effects of 18 predictor variables varying within groups (for example, the activity of group members in a 19 chat) and predictors varying between groups (for example, the group homogeneity created 20 by group members’ prior knowledge). So it allows taking into account that an instruction, 21 tool or learning environment has different but systematic effects on the members within the 22 groups on the one hand and on the groups on the other hand. The underlying statistical 23 model of MLM is described using an example from CSCL. Attention is drawn to the fact 24 that MLM requires large sample sizes which are not provided in most CSCL research. A 25 proposal is made for the use of some analyses which are useful.