I‘m doing mediational analysis on clustered (AKA nested/multilevel) data. The question I have is not specific to mediational analyses but applies to any kind of multilevel analysis.
I‘m assessing whether the effect of SES (socioeconomic status) on an outcome is mediated by another variable. SES is a continuous individual predictor and the other two variables are continuous (Likert scores transformed to percentages). This data comes from schools so the nested structure is children within classes within schools. Meaning that the analysis needs to account for the possible similarities in responses of children who are in the same school and in the same class.
The problem is that since the schools were selected on the basis of the SES status of the neighbourhood, the school and the individual SES predictor are correlated. This means that modeling the effect of school does not seem to make sense in this case (as it would presumably dramatically reduce the effect of the predictor). Would it be safe here to simply ignore the school level of the hierarchy and model the effect of class membership? Any advice would be greatly appreciated!