  In this paper, we present a computational technique to deal with uncertainty
in dynamic continuous models in Social Sciences. Considering data from surveys,
the method consists of determining the probability distribution of the survey
output and this allows to sample data and fit the model to the sampled data
using a goodness-of-fit criterion based on the chi-square-test. Taking the
fitted parameters non-rejected by the chi-square-test, substituting them into
the model and computing their outputs, we build 95% confidence intervals in
each time instant capturing uncertainty of the survey data (probabilistic
estimation). Using the same set of obtained model parameters, we also provide a
prediction over the next few years with 95% confidence intervals (probabilistic
prediction). This technique is applied to a dynamic social model describing the
evolution of the attitude of the Basque Country population towards the
revolutionary organization ETA.
