Google Understanding Visualization by Understanding Individual Users - Manual (Page 4)

aspects of a visualization are significant to the individual user.
Much prior work in individual differences in visualization has been
case-based and domain-specific. While the researchers in the previ-
ously cited studies found individual differences in performance, it is
unclear whether those differences are inherent to all visualization use
or reliant on aspects of the specifics systems they used. It would be
helpful to analyze these systems to discover how they vary from one
another. Unfortunately, there does not exist a standardized set of di-
mensions on which to analyze, let alone synthesize, visual designs.
This proved a challenge when we designed our study on locus of
control [19]. Much previous research has aimed to find individual dif-
ferences in the use of a single visualization system, or in one or more
real-world systems that differ from one another in many respects. Our
purpose, on the other hand, was to take a known individual difference
in performance and attempt to isolate the design factors that influence
it. Therefore, we needed to create a set of visualizations that system-
atically varied on a particular visual quality: in our case, whether the
visualization used a containment- or list-driven visual metaphor. We
arrived at the solution in Figure 1 through trial and error. Although it
was sufficient to isolate the effect we were testing, a more controlled
approach and a common language would more quickly advance this
field of study. This requires some way to classify a visualization based
on its design factors.
There are many high-level classifications of visualization types, but
these do not offer the level of detail needed to isolate design vari-
ables. Classical visualization taxonomies are often based on data vari-
ables, such as dimensionality and data type (e.g., categorical, ordinal,
or numerical). For example, Shneiderman’s task-by-data-type taxon-
omy [15] and Tory and M¨oller’s design model taxonomy [16] fol-
low this pattern. Other taxonomies, inspired by Bertin’s Semiology of
Graphics
[1], rely on describing the mapping between data variables
and visual variables such as color or position. Card and Mackinlay’s
work [2] on structuring the information visualization design space falls
under this category.
While work of this kind is useful in describing the kinds of data
a visualization can depict, it is limited when it comes to describing
factors of the visual design itself. This makes it difficult to isolate
the factors that cause performance differences for varying user types.
For example, there is no existing language in visualization theory to
easily describe the differences between the visual designs we stud-
ied in relation to locus of control. The visualizations are the same in
terms of basic visual mapping; the significant design differences are at
a structural or metaphorical level. In Card and Mackinlay’s descrip-
tions of visual mappings, these properties are generally denoted with
an asterisk indicating a special case. There is no systematic way to fit
structural design differences into a visual mapping schema.
What is lacking is a usable decomposition of visualization design.
In order to correlate individual factors and design factors, we need to
know what those design factors are and be able to manipulate them in
a controlled way. This means being able to take a single visualization
and reliably analyze what components make it up and how they relate
to one another. This is an approach closer to that proposed by Steven
Pinker [10]. Pinker’s goal was to represent charts in a way that would
make it possible to model the chart comprehension process compu-
tationally. The result is a decomposition into parts that includes both
visual mappings, perceptual qualities, and structural elements like axes
and labels in a single graph representation (Figure 2).
Pinker based his decomposition theory on simple, static charts, and
it is unclear how to extend it for more complex situations like inter-
active visualizations or multiple linked views. As the existing re-
search shows, it is in these more complex situations that individual
differences in visualization user are most likely to arise. Nonetheless,
Pinker’s work does offer a model for a more comprehensive analysis
of the composition of a visualization design. An abstract representa-
tion of a visual design produced by decomposition analysis could be
measured and analyzed in more quantitative ways, producing metrics
that can be usefully correlated to individual personality factors.
Individual factors and visual design are both necessary components
in order to explain visualization performance in the context of user
differences. However, they are not the only factors in performance.
Further research in this area should also consider the effect of data
complexity and task type in individual visualization use. For example,
we and other researchers have already shown that complex tasks are
more likely to be associated with individual differences. Nonetheless,
we argue that the first step in this process is understanding significant
factors of the user and of the visual representation.
After building a rich taxonomy of design factors that interact with
various personality traits and a good understanding of which traits are
significant for visualization use, we will be poised to begin running
valuable experiments to determine how individual differences affect
visualization use. Such experiments could potentially include exami-
nations of how other factors such as data and task complexity play into
individual performance differences. This experimental toolkit could
also form the basis for deeper questions about how people make sense
of visual information under varied circumstances. This is an ambi-
tious long-term research agenda, but its results have the potential to
transform our understanding of visualization.
5 C ONCLUSION
As we begin to understand the complex relationships among person-
ality, design, and performance, we move toward a fundamental shift
in how we approach design of visual interfaces. A goal of such in-
terfaces is to support user thinking. Acknowledging that there is no
single, representative user is a critical step toward more effective visu-
alization design. Existing visualization theory has provided extensive
knowledge about how to create good visual information designs. How-
ever, evaluation results are often ambiguous when two well-designed
systems are compared to one another. By understanding the differ-
ences between individuals, we may gain the ability to select between
good designs to find the best design for a given user. This approach
is reinforced by empirical findings, outlined in this article, that indi-
vidual personality factors affect performance on visualization tasks.
Evidence suggests that these performance differences are more appar-
ent in cognitively demanding situations.
Our formal model of visualization must incorporate models of in-
dividual users, their personality profiles, and their situational strate-
gies. There are many rich areas for exploration that accompany this
ideological shift. For example, because the possible combinations of
personality traits are functionally limitless, the process of designing
for the user as an individual inherently demands the development and
adoption of adaptive interfaces. That is, the interfaces we develop with
individual differences in mind should learn about the user as an indi-
vidual, and adjust themselves to best support the unique combination
of personality factors expressed in the user at that time. Such person-
alized, or adaptive, interfaces are designed to enhance an individual
user’s strengths and address individual weaknesses, and have been ex-
tensively studied in human-computer interaction [9]. Combining this
existing work with the knowledge of users we gather in visualization
studies will make it possible to tune a visualization interface in accor-
dance with the principles uncovered by this research.
Adapting visualizations to broad classes of users is a valuable strat-
egy for design. However, it is impractical to subject every user of a
real-world system to the kind of multiple-choice personality invento-
ries or tests of cognitive ability used in the experiments outlined above.
In lieu of laborious tests, we propose to build a model of a user’s per-
sonality and cognitive ability by analyzing his or her interaction his-
tory over time.
While there has been little research in this area to date, exploratory
work by Khan et al. [8] shows that significant correlations can be found
between interaction measures in a programming task and several per-
sonality measures, including those in the Five-Factor model. For ex-
ample, a negative correlation was found between openness to experi-
ence and the number of times a participant switched between windows.
As this dimension also showed a positive correlation with the length of
time between interaction events, this suggests that more open partici-
pants spent more time in each window. Findings like these, extended
to visualization-specific tasks, could form the basis for a model of user
personality based on tracked interactions. We posit that such a model