The interaction of
data type, &
in multivariate visualization
All graphics and papers in these Web pages are copyrighted by Dr. Yu, Chong-ho
- Problem: While physical and engineering scientists endorse
the use of high-dimensional graphics for data analysis, several
psychologists have been unable to confirm the effectiveness of
- Framework: To explain this discrepancy, this study
proposed an alignment framework maintaining that a successful data
visualization results from the proper combination of data, task, and
- Based upon the alignment framework, it was hypothesized
that under conditions of medium and large data sets 3D graphs would
outperform their 2D counterparts for the tasks of examining
relationships and spotting outliers.
- Also, it was expected performance would not vary across graphical formats when small data sets were used.
- Findings: Twenty-three graduate students with
experience in data visualization participated in an experiment to test
these hypotheses. Superior performance for 3D graphics was found across
all data sizes for both research tasks. Results are largely consistent
with the theoretical expectations derived from the alignment framework.
Table of Contents
Press this icon to contact Dr. Yu via various channels