Three models of human interactions with computer- displayed statistical graphics were developed and tested in an experiment which examined users' speed ...
CH1'89 PROCEEDINGS
MAY 1989
MODELS OF USER INTERACTIONS WITH G R A P H I C A L INTERFACES: I. STATISTICAL G R A P H S Douglas J. Gillan 1, Robert Lewis2, and Marianne Rudisill3 L o c k h e e d Engineering and Sciences Co. 1 2400 N A S A R o a d 1 Houston, TX 77058
Rice University2 Psych. Dept. Houston, TX
ABSTRACT
Three models of human interactions with computerdisplayed statistical graphics were developed and tested in an experiment which examined users' speed and accuracy on identification and comparison questions using 17 graph types. The results indicated that response time and accuracy were influenced by the perceptual and informational complexity of the graph, as well as the relation between the figure and axes, (Model 1); by the physical elements of the graph -- points, lines, and areas (Model 2); and by the data-ink ratio and data density (Model 3). The discussion focuses on the development of a single integrated model of interactions with graphics. KEYWORDS: Statistical Graphs, User Models, Cognitive Models, Performance Models
INTRODUCTION Only by developing performance models and cognitive models and, then, by conducting research to test those models can our understanding of the effects of computer graphics on the user keep pace with the recent rapid expansion in graphics capabilities. This paper describes the initial steps that we have undertaken at the HumanComputer Interaction Laboratory (HCIL) at NASA's Johnson Space Center to develop and test models of users' interactions with graphics, particularly statistical graphs. We have focused our initial efforts on developing a model related to statistical graphs for three reasons: (1) recent concepts by statisticians designed to provide guidance for developing statistical graphs (specifically, the work of Bertin [1] and Tufte [7]) can also serve as potential user models; (2) statistical graphs play an important role in two key areas of the human-computer interface -- user computer dialogues, e.g., direct manipulation interfaces (see [5] for a review), and taskspecific tools for presenting information, e.g., statistical graphics packages; and (3) computer-displayed graphs will be crucial for a variety of tasks for the Space Station Freedom and other future advanced spacecraft.
The models mentioned above (as well as one by Cleveland and McGill [2,3] which we will not address in detail here) propose different variables as the basic features of statistical graphs. A model based on Bertin's work [1] focuses on three constructs, (1) "implantation", 1989 A C M
N A S A J o h n s o n Space Center 3 SP34 Houston, TX
i.e., the variation in the spatial dimensions of the graphic plane as a point, line, or area; (2) "elevation", i.e., variation in the graphical element's qualities -- size, value, texture, color, orientation, or shape; and (3) "imposition", i.e., how information is represented, as in a statistical graph, a network, a geographic map, or a symbol. Tufte [7] provides a set of guidelines and concepts to be used in constructing graphs and maps. However, a user model can be developed based on two of Tufte's influential concepts, data-ink and data density. Tufte describes data-ink as "the non-erasable core of a graphic" [2, p.93] and provides a measure, the data-ink ratio, which is the "proportion of a graphic's ink devoted to the non-redundant display of data-information" [2, p.93]. Data density is the ratio of the number of data points and the area of the graphic. Tufte's guidelines call for maximizing both the data-ink ratio and, within reason, the data density. Both Bertin's and Tufte's ideas about the features of data graphs were derived from their experience as statisticians, rather than from experimental evidence. In contrast, recent research from our laboratory has used multivariate statistical techniques for empirical investigations on the basic features of a data graph [6]. That research indicated that three factors underlie people's similarity judgments about statistical graphs: (1) perceptual complexity, (2) whether the axes are required to interpret the data, that is, the relation between the figure and axes, and (3) informational complexity (i.e., the number of data dimensions). The primary purpose of the present experiment was to examine the effect on users' performance with computerdisplayed statistical graphs of the three features identified by multivariate techniques in our lab, with the focus on people's speed and accuracy. In addition, because we were interested in whether the variables' effects were conditional on a user's specific task, we asked subjects two types of questions: (1) to identify the specific value associated with a variable or the specific variable associated with a value, and (2) to compare the value of two or more variables. Although graphs may be used for more sophisticated tasks (e.g., predicting trends or
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CH1'89 PROCEEDINGS determining central tendency), a survey of human factors engineers at Johnson Space Center suggested that graphs are used most frequently for identification and comparison, accounting for 42% of user's interactions with graphs among our sample. The experimental design also permitted us to provide empirical tests of the effects of Bertin's implantation concept and of Tufte's data-ink and data density concepts. METHOD Subjects Thirty-one engineers and scientists at Lockheed-ESC and NASA JSC participated.
MAY 1989 ask questions about the graphs during this period. Subjects then completed three practice trials to further familiarize them with the procedure. Each subject received the experimental trials in a different random order. The subjects initiated the start of the first trial by selecting a soft button labelled "READY". Then a question was displayed along with another READY button. When the subject had read the question, he or she selected the button, and the statistical graph was displayed. Fifteen subjects answered the question by selecting one of three alternatives displayed with the graph. Sixteen subjects typed their response into a data field. After their response, the next question was displayed automatically. At the end of the 68 trials, each subject was debriefed.
Stimuli RESULTS Seventeen types of graphs were used as stimuli. The graphs were (1) two types of scatter plots (a range graph and density graph), (2) two types of simple line graphs (a line graph and a filled line graph), (3) five types of bar graphs (a bar graph, a checked bar graph, a checked bar graph with lines, a segmented or stacked bar graph, and a column graph), and (4) miscellaneous other graphs (a ray graph, a surface graph and a textured surface graph, a star graph, a dot graph, a 3-D graph, a pie graph, and a stick figure graph). For examples of the more unusual of these graphs see [1], [2], and [7]. These 17 graph types were the same as those used in our previous multivariate research on the features of data graphs [6] described above; consequently, the factor loadings (i.e., factor scores) on the three factors derived in that experiment were known for each graph. Experimental Design Subjects received 4 trials with each of the 17 graphs, for a total of 68 trials. On the Identification trials, the subject was asked to identify the value of a variable (e.g., What is the value of Variable A?) or to identify the variable associated with a value (e.g., Which variable has a value of 1.5?). On two trials, subjects compared the values of two or more variables (e.g., Which variable has the greatest value, A, B, or C?). For each subject, each graph type contained different data for the four questions. Procedure All stimuli were presented on a Macintosh Plus computer. Subjects received identical spoken and computer-displayed instructions which described (1) the general objective of the experiment, (2) the stimuli, (3) the overall procedure, and (4) the specific procedure on a trial. Subjects were told that both their speed and accuracy were being recorded and that they should answer each question as quickly and accurately as possible. Next, subjects were familiarized with each graph, by viewing each for as long as they wanted. During this time, the experimenter explained how information was represented on each graph and how information could be obtained from the graph; Subjects were also allowed to 376
Analysis of Multivariate Model Multiple regression analyses were performed, in which the predictor variables were the three factors derived in our previous research [6] -- perceptual complexity, figureto-axis relation, and informational complexity -- and the interactions of the factors with the type of question -Identification and Comparison. The criterion variables were the response time and accuracy. Figure 1 shows the relations between the factors and both the response time and accuracy in responding to Identification and Comparison questions. The graphs show both the individual data points for each graph, as a function of their factor scores, and the best fitting regression line for both types of questions. Increased Perceptual Complexity resulted in increased response time and decreased accuracy, Es(1,30) = 109.2 for response time and 17.2 for accuracy, both 12s
