Journal
IISE TRANSACTIONS
Volume 53, Issue 3, Pages 313-325Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/24725854.2020.1755068
Keywords
Generalized linear regression; GLASSO; information visualization; mixed responses; quantitative evaluation; wearable sensors
Funding
- Ministry of Education of China [20YJC910007]
- National Natural Science Foundation of China [71871047]
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Information visualization significantly enhances human perception by graphically representing complex data sets. The variety of visualization designs makes it challenging to efficiently evaluate all possible designs catering to users' preferences and characteristics. Most existing evaluation methods perform user studies to obtain multivariate qualitative responses from users via questionnaires and interviews. However, these methods cannot support online evaluation of designs, as they are often time-consuming. A statistical model is desired to predict users' preferences on visualization designs based on non-interference measurements (i.e., wearable sensor signals). In this work, we propose a Multivariate Regression of Mixed Responses (MRMR) to facilitate quantitative evaluation of visualization designs. The proposed MRMR method is able to provide accurate model prediction with meaningful variable selection. A simulation study and a user study of evaluating visualization designs with 14 effective participants are conducted to illustrate the merits of the proposed model.
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