Symbolic data extend the classical tabular model, where each individual, takes exactly one value for each variable by allowing multiple, possibly weighted, values for each variable. New variable types - interval-valued, categorical multi-valued and modal variables - have been introduced, which allow representing variability and/or uncertainty inherent to the data. But are we still in the same framework when we allow for the variables to take multiple values? Are the definitions of basic notions still so straightforward? What properties remain valid? In this paper we discuss some issues that arise when trying to apply classical data analysis techniques to symbolic data. The central question of the measurement of dispersion, and the consequences of different possible choices in the design of multivariate methods will be addressed.