  In the framework of Symbolic Data Analysis (SDA), distribution-variables are
a particular case of multi-valued variables: each unit is represented by a set
of distributions (e.g. histograms, density functions or quantile functions),
one for each variable. Factor analysis (FA) methods are primary exploratory
tools for dimension reduction and visualization. In the present work, we use
Multiple Factor Analysis (MFA) approach for the analysis of data described by
distributional variables. Each distributional variable induces a set new
numeric variable related to the quantiles of each distribution. We call these
new variables as \textit{quantile variables} and the set of quantile variables
related to a distributional one is a block in the MFA approach. Thus, MFA is
performed on juxtaposed tables of quantile variables. \\ We show that the
criterion decomposed in the analysis is an approximation of the variability
based on a suitable metrics between distributions: the squared $L_2$
Wasserstein distance. \\ Applications on simulated and real distributional data
corroborate the method. The interpretation of the results on the factorial
planes is performed by new interpretative tools that are related to the several
characteristics of the distributions (location, scale and shape).
