This study investigated several factors influencing the perception of nonlinear relationships in time series graphs. To model real-world data, the graphed data represented different underlying trends and included different sample sizes and amounts of variability. Six trends (increasing and decreasing linear, exponential, asymptotic) were presented on four graph types (histogram, line graph, scatterplot, suspended bar graph). The experiment assessed how these factors affect trend discrimination, with the overall goal of judging what types of graphs lead to better discrimination. Six participants (two psychology professors, four psychology graduate students) viewed graphs on a computer screen and identified the underlying trend. All participants were familiar with the types of trends presented and were aware of the purpose of the experiment. Analysis indicated higher accuracy when variability was lower and sample size was higher. Choice accuracy was higher for nonlinear trends and was highest when line graphs were used.
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References
1.
AndreassenP. B.KrausS. J. (1990) Judgmental extrapolation and the salience of change. Journal of Forecasting, 9, 347–372.
2.
BakerK. E.UlnessJ. (1968) The dependency of intermediate size problem responses upon sets in human Ss. Psychonomic Science, 13, 75–76.
3.
BestL. A.SmithL. D.FreyR. B.StubbsD. A. (1998) Perception of graphs: Discriminability and variability. Paper presented at the Maine Biological and Medical Sciences Symposium, Biddeford, ME.
4.
BestL. A.SmithL. D.StubbsD. A.FreyR. B. (1999) Sensitivity and bias in graphical perception. Poster presented at the annual meeting of the Eastern Psychological Association, Providence, RI.
5.
ChambersJ. M.ClevelandW. S.KleinerB.TukeyP. A. (1983) Graphical methods for data analysis.Pacific Grove, CA: Wadsworth & Brooks/Cole.
6.
ClevelandW. S. (1993a) A model for studying display methods of statistical graphics. Journal of Computational and Graphical Statistics, 2, 323–343.
7.
ClevelandW. S. (1993b) Visualizing data.Summit, NJ: Hobart.
8.
ClevelandW. S. (1994) The elements of graphing data.Summit, NJ: Hobart.
9.
CochranJ. K.AlbrechtS. A.GreenY. A. (1989) Guidelines for evaluating graphical designs: A framework based on human perception skills. Technical Communication, 36, 25–32.
10.
CulbertsonH. M.PowersR. D. (1959) A study of graph comprehension difficulties. Audio-Visual Communication Review, 7, 97–100.
11.
DeProsperoA.CohenS. (1979) Inconsistent visual analyses of intrasubject data. Journal of Applied Behavior Analysis, 12, 573–579.
12.
FurlongM. J.WampoldB. E. (1982) Intervention effects and relative variation as dimensions in experts' use of visual inference. Journal of Applied Behavior Analysis, 15, 415–421.
13.
GoodwinP.WrightG. (1994) Heuristics, biases and improvement strategies in judgmental time series forecasting. Omega: The International Journal of Management Science, 22, 553–568.
14.
JonesG. V. (1977) Polynomial perception of exponential growth. Perception & Psychophysics, 21, 197–200.
15.
JonesG. V. (1979) A generalized polynomial model for perception of exponential series. Perception & Psychophysics, 25, 232–234.
16.
JonesG. V. (1985) Perception of inflation: Polynomial not exponential. Perception & Psychophysics, 36, 485–487.
17.
KerenG. (1983) Cultural differences in the misperception of exponential growth. Perception & Psychophysics, 34, 289–293.
18.
KosslynS. M. (1994) Elements of graph design.New York: Freeman.
19.
KratochwillT. R. (1978) Foundations of time series research. In KratochwillT. R. (Ed.), Single subject research: Strategies for evaluating.New York: Academic Press. Pp. 1–90.
20.
LavertyJ. J.WerboffJ.FreyR. B. (1969) Solution of intermediate size problem and discrimination transfer by rats. Journal of Comparative and Physiological Psychology, 68, 262–267.
21.
LewandowskyS.SpenceI. (1989) The perception of statistical graphs. Sociological Methods and Research, 18, 200–242.
22.
LoftusG. R. (1996) Psychology will be a much better science when we change the way we analyze data. Current Directions in Psychological Science, 5, 161–171.
23.
MacKinnonA. J.WearingA. J. (1991) Feedback and the forecasting of exponential change. Acta Psychologia, 76, 177–191.
24.
MacmillanN. A.CreelmanC. D. (1991) Detection theory: A user's guide.New York: Cambridge Univer. Press.
25.
NisbettR. E.RossL. (1980) Human inference: Strategies and shortcomings on social judgment.Englewood Cliffs, NJ: Prentice-Hall.
26.
OttenbacherK. J. (1986) Reliability and accuracy of visually analyzing graphed data from single-subjects designs. American Journal of Occupational Therapy, 40, 464–469.
27.
ParkH. S.MarascuiloL.Gaylord-RossR. (1990) Visual inspection and statistical analysis in single-case designs. Journal of Experimental Education, 58, 311–320.
28.
SandersN. R. (1992) Accuracy of judgmental forecasts: A comparison. Omega: The International Journal of Management Science, 20, 353–364.
29.
TimmersH.WagenaarW. A. (1977) Inverse statistics and misperception of exponential growth. Perception & Psychophysics, 21, 558–562.
30.
TufteE. R. (1990) Envisioning information.Cheshire, CT: Graphics Press.
31.
TukeyJ. W. (1990) Data-based graphics: Visual display in the decades to come. Statistical Science, 5, 327–339.
32.
WagenaarW. A.TimmersH. (1978) Extrapolation of exponential time series is not enhanced by having more data points. Perception & Psychophysics, 24, 182–184.
33.
WagenaarW. A.TimmersH. (1979) The pond-and-duckweed problem: Three experiments on the misperception of exponential growth. Acta Psychologia, 43, 239–251.
