We present a user study quantifying the effectiveness of Scaled Data-Driven Spheres (SDDS), a multivariate three-dimensional data set visualization technique. The user study compares SDDS, which uses separate sets of colored sphere glyphs to depict variable values, to superquadric glyphs, an alternative technique that maps all variable values to a single glyph. User study participants performed tasks designed to measure their ability to estimate values of particular variables and identify relationships among variables. Results from the study show that users were significantly more accurate and faster for both tasks under the SDDS condition.
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