Although traditional methods to collect similarity data (for multidimensional scaling, MDS) are robust, they share a key shortcoming. Specifically, the possible pairwise comparisons in any set of objects grow rapidly as a function of set size. This leads to lengthy experimental protocols, or procedures that involve scaling stimulus subsets. We review existing methods of collecting similarity data, and critically examine a spatial arrangement method (SpAM) proposed by Goldstone (1994a), in which similarity ratings are obtained by presenting many stimuli at once. The participant moves stimuli around the computer screen, placing them at distances from one another that are proportional to subjective similarity. This provides a fast, efficient, and user-friendly method for obtaining MDS spaces. Participants gave similarity ratings to artificially constructed visual stimuli (comprising 2–3 perceptual dimensions), and non-visual stimuli (animal names) with less-defined underlying dimensions. Ratings were obtained using four methods: pairwise comparisons, spatial arrangement, and two novel hybrid methods. We compared solutions from alternative methods to the pairwise method, finding that the SpAM produces high-quality MDS solutions. Monte Carlo simulations on degraded data suggest that the method is also robust to reductions in sample sizes and granularity. Moreover, coordinates derived from SpAM solutions accurately predicted discrimination among objects in “same/different” classification. In the General Discussion, we address the benefits of using a spatial medium to collect similarity measures.