Voting advice applications (VAAs), online tools that provide voters with an estimate of their ideological congruence with political parties or candidates, have become increasingly popular in recent years. Many VAAs draw on low-dimensional spatial representations to match voters to political elites. Yet VAA spatial maps tend to be defined purely on a priori grounds. Thus fundamental psychometric properties, such as unidimensionality and reliability, remain unchecked and potentially violated. This practice can be damaging to the quality of spatial matches. In this paper we propose dynamic scale validation (DSV) as a method to empirically validate and thereby improve VAA spatial maps. The basic logic is to draw on data generated by users who access the VAA soon after its launch for an evaluation (and potential adjustment) of the spatial maps. We demonstrate the usefulness of DSV drawing on data from three actual VAAs: ParteieNavi, votulmeu and choose4cyprus.