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Bars and lines: A study of graphic communication

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Abstract

Interpretations of graphs seem to be rooted in principles of cognitive naturalness and information processing rather than arbitrary correspondences. These predict that people should more readily associate bars with discrete comparisons between data points because bars are discrete entities and facilitate point estimates. They should more readily associate lines with trends because lines connect discrete entities and directly represent slope. The predictions were supported in three experiments--two examining comprehension and one production. The correspondence does not seem to depend on explicit knowledge of rules. Instead, it may reflect the influence of the communicative situation as well as the perceptual properties of graphs.
Bars and Lines: A Study of Graphic Communication
Jeff Zacks and Barbara.Tversky
Psychology Department
Stanford University
Stanford, CA 94305-2130
{zacks,bt} @psych.stanford.edu
Abstract
Interpretations of graphic displays of information seem to be
rooted in principles of cognitive naturalness and information
processing rather than arbitrary correspondences. Both of
these considerations predict that people should more readily
associate bars with discrete information because bars are
discrete entities and facilitate point estimates. Similarly,
people should more readily associate lines with trends
because lines connect discrete entities and directly represent
slope. In two experiments, viewers tended to describe bar
graphs in terms of discrete comparisons between individual
data points, while they tended to describe line graphs in
terms of continuous trends. In a third experiment,
participants sketched graphic displays to illustrate verbal
descriptions of data; they tended to use bar graphs to convey
discrete comparisons, and line graphs to convey trends. The
strength of the bar/line convention seems to depend on the
communicative situation as well as the perceptual and
conceptual properties of the graphic displays.
Introduction
Graphs are a pervasive species of cognitive artifact, used
both to reason about data and to communicate them. Given
a particular data set, there are a large number of ways it
could be portrayed, and people develop conventions that
govern what techniques are used in a given situation. One
such convention is the use of bar graphs to depict
comparisons among discrete data points and line graphs to
depict trends. This convention is reflected in instructional
books (Kosslyn, 1993), publication manuals (American
Psychological Association, 1994), and in the frustrated
complaints of editors when it is violated. It does not seem
to be arbitrary. On the one hand, it fits with the way people
use space to convey meaning. On the other hand, it fits
with the ease with which people extract information from
graphics.
In the sections that follow, we will describe how the bar-
line convention could originate from biases in the
perceptual and cognitive abilities of graph viewers. We
will then present data documenting how the convention
functions both for viewers and authors of graphs. Based on
the strength of the effect, and on the relationship of
authors’ choices to viewers’ behavior, we conclude that the
convention can’t be fully understood just in terms of the
information-processing properties of graph viewers, but
requires thinking about the larger situation in which graphs
are used to communicate.
Cognitive Naturalness
Many conventions of graphic communication have been
invented and reinvented across cultures and by children,
suggesting that they derive from cognitively natural ways
of using space to convey meaning (Tversky, 1995). This
may be the case for bars and lines as well. Some support
for this comes from research on production and
comprehension of graphic displays. In producing graphic
representations of temporal, quantitative, and preference
relations, children across cultures line up dots they perceive
as representing levels of an underlying dimension but do
not line up dots they do not perceive as related
dimensionally (Tversky, Kugelmass & Winter, 1991).
selecting what graphic displays they would use for
conveying various sorts of information, adults prefer to use
bars for conveying discrete information and lines for
conveying trends (Levy, Zacks, Tversky & Schiano, 1996).
The Gestalt principles underlying figural perception
support the naturalness of bars for categorical information
and lines for ordinal or interval data. In bar graphs, each
value is represented as a separate bar, suggesting separate
entities or categories, whereas in line graphs, values are
connected by a single line, suggesting that all the values
belong to the same entity.
Information-processing Models Of Graphical
Perception
Pinker (1990) has developed the most detailed information-
processing theory of graph comprehension. In Pinker’s
model, a graph reader first builds up a visual description of
the visual display, similar to Marr’s (1982) 2 1/2 D sketch.
This representation is constrained by several factors,
including Gestalt laws of grouping and prior experience
with other graphs. From the visual description, a graph
reader constructs conceptual messages, which are
propositions about variables depicted in the graph. The
reader can also construct conceptual questions, which are
essentially conceptual messages with missing values.
High-level inferential processes are available to operate on
conceptual messages.
144
From: AAAI Technical Report FS-97-03. Compilation copyright © 1997, AAAI (www.aaai.org). All rights reserved.
Pinker argues, "different types of graphs are not easier or
more difficult across the board, but are easier or more
difficult depending on the particular class of information
that is to be extracted" (1990, p. 11). Based on the theory,
Pinker predicts that it should be easier to make discrete
comparisons between individual data points from bar
graphs and easier to make trend assessments from line
graphs.
~
The Gestalt principles again support this
prediction. Absolute values are easier to discern when the
values are presented separately, as in bars, whereas trends
are easier to discern when values are connected, as in lines.
The prediction of an interaction between graph type (bar
vs. line) and ease of making discrete comparisons or trend
assessments is also supported by empirical results. Simcox
(1984, described in Pinker, 1990) found that viewers were
faster to make judgments about the absolute value of data
points with bar graphs than with line graphs, but faster to
make judgments of slope with line graphs than with bar
graphs. This pattern held both for a sorting task and a
classification task. In related (as yet unpublished) work,
we found that viewers were faster to make discrete
comparisons from bar graphs than from line graphs. For
trend judgments, there was no difference between the two
graph types. Interestingly, this pattern held even when the
graphs contained only two data points. In this case, the
discrete comparison and the trend assessment are formally
equivalent (Zacks, Levy, Tversky & Schiano, 1996).
Finally, Simkin and Hastie (1987) compared bar graphs (of
a type slightly different from that used in our experiments)
with pie graphs for discrete comparison judgments and
proportion-of-the-whole judgments. They found viewers
were more accurate making discrete comparisons with the
bar graphs than the pie graphs, while the opposite was true
for proportion-of-the-whole judgments. Also, they
reported that in a survey, viewers tended to spontaneously
describe bar graphs in terms of discrete comparisons, and
to describe pie graphs in terms of proportions of the whole.
Origins of the Bar-Line Convention
Together, the theory and results described above suggest
that people should be more likely to interpret information
presented in bars as deriving from discrete variables and
information presented in lines as deriving from continuous
underlying variables. This should be evident in the way
they describe the depicted relations. Information presented
as bar graphs should be described categorically, in terms of
discrete comparisons between individual data points, using
terms such as "higher," "lower," "greater than," and "less
than." On the other hand, information presented as lines
should be described as continuous trends between the data
I Graphical perception, including the comprehension of
trends and discrete comparisons from line and bar graphs,
has also been modeled quantitatively by Lohse (1993).
However, specific predictions about the interaction of
graph type (bar vs. line) and task were not reported.
points, using terms like "rising," "falling," "increasing,’"
and "decreasing." The first two experiments examine
people’s spontaneous descriptions of data graphed as bars
or lines.
Mirroring the predictions for comprehension of graphics
are the predictions for production of graphics. When asked
to graphically represent information described
categorically, using terms like "greater than," people
should produce relatively more bars than lines. When
asked to graphically represent information described
continuously, using terms like "increases," people should
produce relatively more lines than bars. The last
experiment examines people’s constructions of graphs for
representing data described discretely or continuously.
Experiment 1
In the first experiment, we presented participants with
simple bar or line graphs and asked them to describe what
they saw. We predicted that viewers would be more likely
to describe the bar graphs in terms of discrete comparisons
between the individual data points. On the other hand, we
predicted they would be more likely to describe the line
graphs in terms of continuous trends.
Method
Participants. 69 Stanford University undergraduates
participated in this experiment to partially fulfill a course
requirement.
Stimuli and procedure. Simple graphs were drawn of a
two-point data set. The two points were always drawn so
as to be appreciably different; which point was higher was
counterbalanced across viewers. The horizontal axis was
labeled "X" and the vertical axis was labeled "Y." The
data point on the left was labeled "A," and the one on the
right labeled "B." One critical factor was manipulated:
viewers either saw a version of the graph drawn as a bar
graph or as a line graph. Examples of the stimuli are
shown in Figure 1.
Below each graph was the instruction: "Please describe
in a sentence what is shown in the graph above:". The
graph and instructions took up half a page; the other half
was printed with another unrelated question about graphs.
The graphs were printed on 8.5" x 11" paper and
distributed as part of a packet of questionnaires. (A note:
though equal numbers of each version of the graph were
distributed, not all the questionnaire packets were fully
completed, so the numbers of participants viewing each
graph version was not equal. The same was true for the
two studies described below.)
145
A
Y
A
x
Figure 1: Examples of the bar and line graph stimuli used
in Experiment I.
Results
Three judges (the first author and two judges who were
naive to the hypotheses and blind to the conditions)
classified each response as either a "discrete comparison"
between the two points or a "trend assessment". Their
instructions were:
"Classify the way the sentence characterizes the data as a
comparison or a trend description. Comparisons use terms
like more/less, more/fewer, higher/lower, larger/smaller,
stronger/weaker; they tend to refer to discrete values.
Trend descriptions use terms like function, relationship,
correlation, varies, trend; the tend to refer to continuous
changes in the variables. Not all the sentences will have
unambiguous assignments; use your judgment. "
All three judges agreed on 59 of the 69 responses. For
those 59 responses, every bar graph was described with a
discrete comparison and every line graph was describe with
a trend assessment, ~2(1) = 54.9, p < .001. Table 1 shows
this pattern.
Bar graph Line graph
Discrete comparison
24 0
Trend assessment
0
35
Table 1: Frequency of data characterization responses as a
function of graph type.
The particular content of the responses was quite
variable. Most respondents provided conceptual
descriptions of the relationship between the data point A
and B, but some gave physical characterizations of the
graph, and others invented fictional situations to explain
the data. The following are three typical "discrete
comparison" responses, and-three typical "trend
assessment" responses:
Discrete comparisons:
"Y is greater in a than B"
"A is a larger Y quantity than B"
"B is bought more often than A"
Trend assessments:
"A line, drawn on the XY plane, descending from A to B
along the X axis"
"As x increases in value y increases"
"As X increases, Y decreases"
To summarize, the deployment of the bar-line convention
had an unambiguous affect on the disposition of the readers
to respond. When they saw bar graphs, they described
discrete contrasts in the data; when they saw line graphs,
they described trends.
Experiment 2
Experiment 1 demonstrated the existence of a bar-line
convention effect on conceptual structure. Is this effect a
"hot house" laboratory phenomenon, or does it reflect
processes that have real impact on our interactions in the
world? One way to get at this question is to add a salient
source of real-world variation in conceptual structure and
check to see that the bar-line convention effect holds up.
Experiment 2 did this by manipulating the conceptual
domain of the graph along with the graph type. The
dependent variable was always continuous (height). For
the independent variable, the discrete conceptual domain of
gender was contrasted with the continuous domain of age.
This produced situations in which the bar-line convention
conflicted with the content of the data. Most interesting is
the case where line graphs were used with gender as the
conceptual domain. Here the convention implies that a
continuous trend is being depicted, but the domain is
clearly discrete. In this situation, how will people describe
the graph?
Method
Participants. 106 Stanford University undergraduates
participated in this experiment to partially fulfill a course
requirement.
146
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Female Male
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e-
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10-year-olds 12-year-olds 10-year-olds 12-year-olds
Figure 2: Examples of the bar and line graph stimuli, and the continuous and discrete conceptual domains used in
Experiment 2.
Stimuli and procedure. The stimuli were quite similar to
those used in Experiment 1. Simple graphs were drawn of
a two-point data set. The two points were always drawn so
as to be appreciably different; the second point was always
higher than the first.
In this experiment, two factors were manipulated. As in
Experiment I, viewers either saw a version of the graph
drawn as a bar graph or as a line graph. Also, the
conceptual domain of the data was manipulated by varying
the labeling of the points and axes. Two domains were
selected such that the dependent variable could be held
constant while the nature of the independent variable was
manipulated. In one version (the "gender" domain), the
two points were labeled "Female" and "Male." In the other
version, (the "age" domain) the points were labeled "10-
year-olds" and "12-year-olds." The vertical axis was
always labeled "Height (inches)." In the first version, the
two labels identified (more-or-less) discrete categories;
thus the labels denoted a discrete conceptual domain. For
the second version the labels denoted a continuous
variable, age, and therefore a continuous conceptual
domain. To summarize, a given graph stimulus was either
associated with a discrete or continuous conceptual
domain, and either was drawn as a bar graph or line graph.
Examples of the stimuli are shown in Figure 2.
In the previous experiment, it had been noted that a
minority of the responses did not describe the data shown
by the graph. Several described the physical appearance of
the figure, and several created fictional explanations of the
depicted data. To reduce the number of these types of
responses, the instructions were changed slightly. Below
each graph was the instruction: "Please describe in a
sentence the relationship shown in this graph:" (rather than
simply asking for a description of what is shown). The
graph and instructions took up half a page; the other half
was printed with another unrelated question about graphs.
The graphs were printed on 8.5" x 11" paper and
distributed as part of a packet of questionnaires.
Results
Because there was good agreement among the judges about
the graph descriptions in Experiment 1, only the first author
147
Gender (discrete domain) Age (continuous domain)
Bar graph
Line graph Bar graph Line graph
Discrete comparison
28 22 28 9
Trend assessment
0
3 2 14
Table 2: Frequency of data characterization responses as a function of graph type (bar graph or
line graph) and conceptual domain (gender or age).
rated the descriptions. During rating, he was blind to the
type of graph presented.
Effects of graph type and conceptual domain on viewers’
propensity to make a discrete comparison or a continuous
trend assessment were investigated by fitting log-linear
models. We tested the effect of a factor by comparing the
simplest model that contained its interaction with the
dependent variable (description type) with a model with
that interaction removed. In each case, we estimated both
Pearson’s X
2
and the likelihood ratio Z
2,
and report the
more conservative of the two (which in this case was
always Pearson’s E2).
Both the graph type and the conceptual domain exerted
effects on viewers’ descriptions of the graphs (see Table 2).
As in Experiment 1, participants were more likely to use a
discrete comparison for a bar graph than for a line graph,
and more likely to make a trend assessment for a line graph
than a bar graph, X2(I) = 21.5, p < .001. Also,
participants were more likely to make a discrete
comparison when the conceptual domain was gender, and
more likely to use a trend judgment when the conceptual
domain was age, Z2(1) = 14.3, p < .001. (We should
somewhat skeptical of the accuracy of the Z2
approximation, given that one of the cells in Table 2
contains a zero. As a check, we performed an analysis in
which we collapsed over graph type and domain in turn and
computed Z2 tests of independence; it gave the same
results.)
Responses were in general less variable than those in the
first experiment. Descriptions were usually of the depicted
variables, with few physical characterizations or fictional
stories. Examples of the discrete comparisons and trend
assessments follow.
Discrete comparisons:
"Male’s height is higher than that of female’s"
"The average male is taller than the average female."
"Twelve yr. olds are taller than 10 yr olds."
Trend assessments:
"The graph shows a positive correlation between a
child’s increases in age and height between the ages of
10 and 12."
"Height increases with age. (from about 46 inches at 10
to 55 inches at 12)"
"The more male a person is, the taller he/she is"
The last example deserves particular comment. The fact
that some participants were willing to use a continuous
trend assessment to describe a domain that was clearly
discrete illustrates the power of the bar-line convention. (3
of the 25 participants who saw that stimulus gave such a
response.) Comparing the odds ratios for the two effects
(15.4 for graph type, 7.2 for domain) shows that the effect
of graph type was about twice as big as that of conceptual
domain. This indicates that the biasing effect of graph type
is something to be reckoned with even in more
"ecologically valid" situations.
The effect of conceptual domain on qualitative
descriptions agrees well with research showing that
manipulating the conceptual domain can lead to
quantitative distortion in the perception of graphs. In one
experiment, Tversky and Schiano (1989) showed that
labeling a figure as a graph led to distortion of a diagonal
line, while labeling the same figure as a map did not.
Experiment 3
The two previous experiments showed that the bar-line
convention systematically influenced readers’ conceptual
understanding of a graph. If readers are sensitive to this
convention, are authors as well? Experiment 3 was
designed to answer this question.
Method
Participants. 99 Stanford University undergraduates
participated in this experiment to partially fulfill a course
requirement.
Stimuli and procedure. The stimuli for this experiment
were essentially the inverse of those used in the previous
experiment. Participants were given a description of a data
pattern together with a frame for a graph, and asked to
draw a graph. The conceptual domain and the labeling of
the frames was just as it had been in Experiment 2. In one
version (the "gender" domain), the two points were labeled
"Female" and "Male." In the other version, (the "age"
domain) the points were labeled "10-year-olds" and "12-
year-olds." The vertical axis was always labeled "Height
(inches)". The descriptions were either discrete
continuous. The discrete descriptions were:
148
Gender (discrete domain) Age (continuous domain)
Discrete Trend Discrete Trend
description description description description
Bar graph
14 7 11
0
Line graph
6 13
14 24
Table 3: Frequency of graph type drawn as a function of description type and conceptual
domain.
"In the frame above, draw a graph that depicts the
following relationship:
Height for males is greater than for females."
or,
"Height for 12-year-olds is greater than for 10-year-
olds."
The continuous descriptions were:
"In the frame above, draw a graph that depicts the
following relationship:
Height increases from females to males."
Or,
"Height increases from 10-year-olds to 12-year-olds."
The graph and instructions took up half a page; the other
half was printed with another unrelated question about
graphs. The graphs were printed on 8.5" x I I" paper and
distributed as part of a packet of questionnaires.
Results
Of the 99 forms that were returned, most of the drawings
were line graphs (57) or bar graphs (32). Of the remaining
10, 6 could be described as scatter plots. Only the bar
graph and line graph responses were analyzed further.
We analyzed the data in the same fashion as for
Experiment 2, by fitting log-linear models to test
differences in goodness-of-fit. Again, the presence of an
empty cell in the frequency table is problematic for the Z
2
approximations. As a check, we again computed Z
2
tests
of independence, which gave the same results as the log-
linear analysis reported below.
The results of Experiment 3 mirror those of the previous
experiments. Given a discrete description, participants
tended to draw bar graphs; given a continuous description,
the), tended to draw line graphs, Z2(1) = 15.3, p < .001.
Also, they were more likely to use a bar graph for the
discrete conceptual domain and more likely to use a line
graph for the continuous domain, Z2(1) = 9.83, p = .002.
The data are given in Table 3.
These results show that creators of graphs are sensitive to
the bar-line convention in a fashion that parallels that of
readers. Also mirroring the results of Experiment 2, the
effect of description type was more powerful (odds ratio
6.61 ) than that of domain (odds ratio = 3.82), suggesting
that the convention exerts a significant influence in real-
world situations.
Discussion
In two experiments, participants wrote descriptions of
relations portrayed in bar or line graphs. There was a
strong tendency to describe data portrayed as bars
discretely, for example, "A is higher than B," and to
describe data portrayed as lines in terms of trends, for
example, "X increases from A to B." The second
experiment also examined effects of discrete or continuous
variables in the data. The influence of graphic display was
far greater than that of the underlying variable. In a third
experiment, participants were given the reverse task.
Given relations described discretely or continuously, they
were asked to construct graphic displays. There was a
strong tendency to portray discrete descriptions as bars and
continuous descriptions as lines.
Thus, people’s comprehension and production of graphs
conform to the principles of cognitive naturalness and
information processing ease discussed in the introduction.
They also correspond to graphic convention. Where do
graphic conventions like the bar-line convention come
from? It seems likely that they originate in these same
perceptual and cognitive propensities. However, it seems
unlikely that the perceptual-cognitive biases alone could
give rise to the striking effects observed here. The
differences in ease of information extraction between bar
and line graphs are small (Zacks et al., 1996) as are the
effects of cognitive naturalness (Tversky et al., 1991).
We believe that small perceptual-cognitive biases are
parlayed into large effects due to positive feedback exerted
by communicative convention. Conforming to the biases
would initially make graphic communications more readily
understood and the information in them more easily
extracted. Once a disposition to privilege a given
relationship between graphic displays and conceptual
messages is exploited by authors, viewers can rely on that
regularity. This further enhances the disposition and
thereby its use. This process can be likened to the way
speech conventions develop in a community of users
(Clark, 1996).
149
While the origins of the bar-line convention can be
traced to cognitive naturalness and information processing
ease, a fuller understanding emerges when we consider the
larger situation in which authors and viewers use graphs to
communicate. Cognitive scientists interested in graphic
perception have traditionally looked at perceptual-cognitive
processes form the point of view of the solitary observer.
A complete account requires expanding the picture to
include processes of communication.
Zacks, J., Levy, E., Tversky, B., & Schiano, D. (1996).
Ease of Processing with Spatial Representations:
Interaction of Rendering Technique and Conceptual Task.
Unpublished manuscript.
Acknowledgments
The authors would like to thank Interval Research
Corporation for its support of this research and the National
Science Foundation Graduate Fellowship Program for its
support of the first author. Thanks also to Duncan Hill,
Rehan Khan, and Shelly Wynecoop for their assistance
rating the responses and Terry Winograd for his valuable
comments on an earlier draft.
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... First, designing a visualization involves a series of decisions, each of which can afford different viewer takeaways [69,72]. For example, past work has shown that people tend to make magnitude comparisons when viewing bar charts and trend comparisons when viewing line charts [66,73]. However, to the best of our knowledge, prior work does not propose a comprehensive mapping between comparison intents and the best visualization for the intended comparisons. ...
... Existing work has largely focused on how visualization type can influence viewer perception and decisions [45,70,73]. For example, bar charts plot the data as discrete objects, motivating people to compare them as two distinct units (e.g., A is larger than B), while line charts plot data as one single object, eliciting the interpretation of trends, changes over time or relations (e.g., X fluctuates up and down as time passes) [73]. ...
... Existing work has largely focused on how visualization type can influence viewer perception and decisions [45,70,73]. For example, bar charts plot the data as discrete objects, motivating people to compare them as two distinct units (e.g., A is larger than B), while line charts plot data as one single object, eliciting the interpretation of trends, changes over time or relations (e.g., X fluctuates up and down as time passes) [73]. Showing difference benchmarks on bar charts can not only facilitate a wider range of comparison tasks [65] but also increase the speed and accuracy of the comparisons [47]. ...
Preprint
The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.e., data variables and attributes) as well as how these parameters are specified (i.e., explicitly or implicitly). We conducted a user study with sixteen data visualization experts and non-experts to investigate how they designed visualizations for comparisons in our design space. Based on the rich set of visualization techniques observed, we extracted key design features from the visualizations and synthesized them into a subset of sixteen representative visualization designs. We then conducted a follow-up study to validate user preferences for the sixteen representative visualizations corresponding to utterances in our design space. Findings from these studies suggest guidelines and future directions for designing natural language interfaces and recommendation tools to better support natural language comparisons in visual analytics.
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Thesis
Visualizations are an integral part of education, and the features of visualizations have been associated with differences in learning and generalization. I propose that students use the features of visualizations, such as their perceptual richness, as cues to infer the generality of the information presented in lessons. I test this proposal in 10 studies that examine the impact of perceptual richness on biology learning. Chapter 1 provides a comprehensive review of the literature on multi-media learning and how people learn with visualizations and articulates how people might use richness as a cue for generalization. Chapter 2 presents two experiments that examine how perceptual richness influences how adults learn and generalize the concept of metamorphosis, how these effects might change over time, and how they depend on the similarity of the test items to the exemplar in the lesson. Chapter 3 presents one content analysis and six experiments examining how children and adults integrate textual characteristics and the richness of visualizations when learning and generalizing animal facts. Chapter 4 presents an experiment that examines how parents and children learn and generalize the concept of evolution when they learn from a book with rich or bland images. Chapter 5 presents an integrated discussion of all of these studies that highlights how people use richness as a cue to infer the generality of lessons, and also highlights how this depends on characteristics of the lesson, learner, task and context. I also propose that a socio-cultural perspective might be a useful framework to understand how people learn and generalize from multi-media lessons, and I discuss the implications for cognitive and developmental psychology and biology education.
... The designer of a visualization has to make editorial choices that will influence how a viewer perceives the message of the graph [3]. There are many choices that a designer makes that could affect interpretation, including what variables to encode, what colors to use [5], whether to highlight and annotate, whether to add visual embellishments [6], what context to compare the data to, how to frame the title of the visualization [39,40], and what type of chart to use [80]. Whether or not the designer is aware of it, their intents, prior experience, and own personal preferences influence their design choices of the visualization. ...
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When designing communicative visualizations, we often focus on goals that seek to convey patterns, relations, or comparisons (cognitive learning objectives). We pay less attention to affective intents--those that seek to influence or leverage the audience's opinions, attitudes, or values in some way. Affective objectives may range in outcomes from making the viewer care about the subject, strengthening a stance on an opinion, or leading them to take further action. Because such goals are often considered a violation of perceived 'neutrality' or are 'political,' designers may resist or be unable to describe these intents, let alone formalize them as learning objectives. While there are notable exceptions--such as advocacy visualizations or persuasive cartography--we find that visualization designers rarely acknowledge or formalize affective objectives. Through interviews with visualization designers, we expand on prior work on using learning objectives as a framework for describing and assessing communicative intent. Specifically, we extend and revise the framework to include a set of affective learning objectives. This structured taxonomy can help designers identify and declare their goals and compare and assess designs in a more principled way. Additionally, the taxonomy can enable external critique and analysis of visualizations. We illustrate the use of the taxonomy with a critical analysis of an affective visualization.
... Note that the number of line charts did not change as much as that of bar charts. Bar charts differ from line graphs in the following ways: (1) the horizontal axis of a bar chart consists of entities or processes rather than quantities (Gross & Harmon, 2014); (2) bar graphs tend to be used to convey discrete comparisons; and (3) line graphs tend to be used to convey trends (Zacks & Tversky, 1999). However, a previous study pointed out that univariate scatterplots, box plots, and histograms 1974 1979 1984 1989 1994 1999 2004 2009 2014 numbers of graphics in 20 article percentage% Fig. 22 Change in the numbers and ratios of the tables. ...
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