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Information visualization significantly enhances human perception bygraphically representing complex data sets. The variety of visualizationdesigns makes it challenging to efficiently evaluate all possible designscatering to users' preferences and characteristics. Most of existing evaluationmethods perform user studies to obtain multivariate qualitative responses fromusers via questionnaires and interviews. However, these methods cannot supportonline evaluation of designs as they are often time-consuming. A statisticalmodel is desired to predict users' preferences on visualization designs basedon non-interference measurements (i.e., wearable sensor signals). In this work,we propose a multivariate regression of mixed responses (MRMR) to facilitatequantitative evaluation of visualization designs. The proposed MRMR method isable to provide accurate model prediction with meaningful variable selection. Asimulation study and a user study of evaluating visualization designs with 14effective participants are conducted to illustrate the merits of the proposedmodel.

Multivariate Regression of Mixed Responses for Evaluation of Visualization Designs