A cognitive interpretation of data analysis

International Statistical Review

Volumn 82, Issue 2, 2014, Pages 184-204

  Grolemund, Garrett ^a   Wickham, Hadley ^b  

^a RStudio Inc   (United States)

^b Rice University   (United States)

Author keywords

Cognition; Cognitive process; Data analysis; Data analysis model

Indexed keywords

EID: 84905509296     PISSN: 03067734     EISSN: 17515823     Source Type: Journal    
DOI: 10.1111/insr.12028     Document Type: Article

References (88)

1. Attfield, S. & Blandford, A. (2009). Improving the cost structure of sensemaking tasks: Analysing user concepts to inform information system design. Human-Computer Interaction-INTERACT 2009.
2. Bartlett, F.C (1932). Remembering: A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press.
3. Box, G.E.P. (1976). Science and statistics. J. Amer. Statist. Assoc., 71(356), 791-799.
4. Bransford, J.D., Brown, A.L. & Cocking, R.R. (2000). How People Learn: Brain, Mind, Experience, and School. Washington, DC: National Academies Press.
5. Breiman, L. (1985). Nail finders, edifices, and oz. In Berkeley Conference in Honor of Jerzy Neyman and Jack Kiefer, Vol. 1, Eds. L.M. LeCam & R.A. Olshen, pp. 201-214. Hayward, CA: Institute of Mathematical Sciences.
6. Breiman, L. (2001). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat. Sci., 16(3), 199-231.
7. Carley, K. & Palmquist, M. (1992). Extracting, representing, and analyzing mental models. Soc. Forces, 70(3), 601-636.
8. Chatfield, C. (1995). Problem Solving: A Statistician's Guide. Boca Raton: Chapman & Hall/CRC.
9. Cobb, G.W. (2007). The introductory statistics course: A ptolemaic curriculum?Tech. Innovat. Stat. Educ., 1(1), 1-15.
10. Cook, D. & Swayne, D.F. (2007). Interactive and Dynamic Graphics for Data Analysis with R and Ggobi. New York: Springer Publishing Company, Incorporated.
11. Cowan, N. (2000). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behav. Brain. Sci., 24(01), 87-114.
12. Cox, D.R. (2001). Comment on statistical modeling: The two cultures (with comments and a rejoinder by the author). Stat. Sci., 16(3), 199-231.
13. Cox, D.R. (2007). Applied statistics: A review. Ann. Appl. Stat., 1(1), 1-16.
14. Dalal, S.R., Fowlkes, E.B. & Hoadley, B. (1989). Risk analysis of the space shuttle: Pre-challenger prediction of failure. J. Amer. Statist. Assoc., 84(408), 945-957.
15. DeGroot, A.D. (1965). Thought and Mind in Chess. The Hague: Mouton.
16. Dervin, B. (1998). Sense-making theory and practice: An overview of user interests in knowledge seeking and use. J. Knowl. Manag., 2(2), 36-46.
17. Donald, M. (1991). Origins of the Modern Mind: Three Stages in the Evolution of Culture and Cognition. Cambridge: Harvard University Press.
18. Farman, J.C., Gardiner, B.G. & Shanklin, J.D. (1985). Large losses of total ozone in antarctica reveal clox/nox interaction. Nature, 315, 207-201.
19. Freedman, D. (2009). The Development of Theory Could Hasten the Speed with Which Data Fields Adapt to Emerging Challenges, Statistical Models and Causal Inference: A Dialogue with the Social Sciences. New York: Cambridge University Press.
20. Gelman, A. & Shalizi, C.R. (2010). Philosophy and the practice of Bayesian statistics. Arxiv preprint arXiv:1006.3868.
21. Gilovich, T., Vallone, R. & Tversky, A. (1985). The hot hand in basketball: On the misperception of random sequences. Cognitive Psychol., 17(3), 295-314.
22. Goffman, E. (1974). Frame Analysis: An Essay on the Organization of Experience. Cambridge: Harvard University Press.
23. Helsdingen, A.S. & Vanden Bosch, K. (2009). Learning to make sense. Presented at the Cognitive Systems with Interactive Sensors Conference. November 16-18, 2009. Paris, France.
24. Hey, T. & Trefethen, A. (2003). The data deluge: An e-science perspective. In Grid Computing: Making the Global Infrastructure a Reality, Eds. F. Berman, G. Fox & T. Hey, pp. 809-824. Chichester, UK: John Wiley & Sons.
25. Huber, P. (1997). Speculations on the Path of Statistics. In The Practice of Data Analysis, pp.175-192. Princeton: Princeton University Press.
26. Huber, P. (2011). Data Analysis: What Can be Learned from the Past 50 Years. Hoboken, New Jersey: John Wiley & Sons, Inc.
27. Jonassen, D.H. & Henning, P. (1996). Mental models: Knowledge in the head and knowledge in the world. In Proceedings of the 1996 International Conference on Learning Sciences, pp. 433-438. Chicago: International Society of the Learning Sciences.
28. Jones, A.E. (2008). The antarctic ozone hole. Phys. Educ., 43, 358.
29. Klein, G. & Crandall, B.W. (1995). The role of mental simulation in problem solving and decision making. In Local Applications of the Ecological Approach to Human-Machine Systems, Vol.2, pp. 324-358. Boca Raton: CRC.
30. Klein, G., Phillips, J.K, Rall, E.L & Peluso, D.A. (2003). Adata/frame theory of sense making. In Expertise Out of Context: Proceedings of the Sixth International Conference on Naturalistic Decision Making, pp.113-155. New York: Lawrence Erlbaum.
31. Kuhn, T.S. (1962). The Structure of Scientific Revolutions. Chicago: Chicago University Press.
32. Lakoff, G. & Núñez, R. (1997). The metaphorical structure of mathematics: Sketching out cognitive foundations for a mind-based mathematics. In Mathematical Reasoning: Analogies, Metaphors, and Images, pp. 21-89. New York: Lawrence Erlbaum.
33. Lakoff, G. & Núñez, R. (2000). Where mathematics comes from: How the embodied mind brings mathematics into being. Basic Books.
34. Lavine, M. (1991). Problems in extrapolation illustrated with space shuttle o-ring data. J. Amer. Statist. Assoc., 86(416), 919-921.
35. Lundberg, C.G. (2000). Made sense and remembered sense: Sensemaking through abduction. J. Econ. Psychol., 21(6), 691-709.
36. Macabebe, E.Q.B, Culaba I.B. & Maquiling, J.T. (2010). Pre-conceptions of Newton's laws of motion of students in introductory physics. Aip. Conf. Proc., 1263, 106.
37. MacKay, R.J. & Oldford, R.W. (2000). Scientific method, statistical method and the speed of light. Stat. Sci., 15(3), 254-278.
38. Mallows, C. (1998). The zeroth problem. Amer. Statist., 52(1), 1-9.
39. Mallows, C. (2006). Tukey's paper after 40 years (with discussion). Technometrics, 48(3), 319-325.
40. Mallows, C. & Walley, P (1980). A theory of data analysis? In Proceedings of the Business and Economics Section, American Statistical Association, pp. 8-14. Washington, DC.
41. Miller, G.A. (1962). Some psychological studies of grammar. Amer. Psychol., 17(11), 748.
42. Minsky, M. (1975). A framework for the representation of knowledge. In The Psychology of Computer Vision, pp. 211-277. New York: McGraw-Hill.
43. Molina, M.J. & Rowland, F.S. (1974). Stratospheric sink for chlorofluoromethanes. Nature, 249, 810-812.
44. Moore, D.S. (1990). Uncertainty. In On the Shoulders of Giants: New Approaches to Numeracy, pp. 95-137. Washington, DC: National Academic Press.
45. Mulaik, S.A. (1985). Exploratory statistics and empiricism. Philos. Sci., 52(3), 410-430.
46. Neisser, U. (1967). Cognitive Psychology. New York: Appleton-Century-Crofts.
47. Neisser, U. (1976). Cognition and Reality: Principles and Implications of Cognitive Psychology. New York: WH Freeman/Times Books/Henry Holt & Co.
48. Norman, D.A. (1993). Things That Make Us Smart: Defending Human Attributes in the Age of the Machine. Number 842 Basic Books. New York: Perseus.
49. Pea, R.D. (1987). Cognitive technologies for mathematics education. In Cognitive Science and Mathematics Education, pp. 89-122. Hillsdale: Lawrence Erlbaum.
50. Peirce, C.S. (1932). Collected Papers of Charles Sanders Peirce, Vol. 1. Cambridge: Belknap Press.
51. Piaget, J. & Cook, M.T. (1952). The Origins of Intelligence in Children. New York: International Universities Press.
52. Pirolli, P. & Card, S. (2005). The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of International Conference on Intelligence Analysis, Vol. 5, pp. 2-4.
53. Presidential Commission on the Space Shuttle Challenger Accident. (1986). Report of the presidential commission on the space shuttle challenger accident (pbk). volume 1 Author, Washington, DC.
54. Qu, Y. & Furnas, G.W. (2008). Model-driven formative evaluation of exploratory search: A study under a sensemaking framework. Inform. Process. Manag., 44(2), 534-555.
55. Resnick, L.B. (1988). Treating mathematics as an ill-structured discipline. In Research Agenda for Mathematics Education: Teaching and Assessment of Mathematical Problem Solving, Eds. R. Charles & E.A. Silver. Hillsdale, NJ: Erlbaum.
56. Rosch, E. (1977). Classification of real-world objects: Origins and representations in cognition. In Thinking: Reading in Cognitive Science, Eds. P.N. Johnson-Laird & P.C. Watson, pp. 212-222. Cambridge: Cambridge University Press.
57. Rosch, E. & Mervis, C.B. (1975). Family resemblances: Studies in the internal structure of categories. Cognitive Psychol., 7(4), 573-605.
58. Rozeboom, W.W. (1997). Good science is abductive, not hypothetico-deductive. In What if There Were No significance Tests, pp. 335-392. New York: Psychology Press.
59. Rudolph, J. (2003). Into the big muddy and out again: Error persistence and crisis management in the operating room. Unpublished doctoral dissertation, Boston College, Chestnut Hill, Mass., available online at http://escholarship.bc.edu/dissertations/AAI3103269.
60. Rumelhart, D.E. & Ortony, A. (1976). The Representation of Knowledge in Memory. San Diego: Center for Human Information Processing, Dept. of Psychology, University of California.
61. Russell, D.M., Stefik, M.J., Pirolli, P. & Card, S.K. (1993). The cost structure of sensemaking. In Chi '93: Proceedings of the Interact '93 and Chi '93 Conference on Human Factors in Computing Systems, New York, NY, USA: ACM; 269-276.
62. Schank, R.C. & Abelson, R.P (1977). Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale, NJ: Lawrence Erlbaum Associates.
63. Smith, E.E. (1978). Theories of semantic memory. In Handbook of learning and cognitive processes, Vol.6, pp. 1-56. Hoboken: John Wiley & Sons.
64. Smith, P.J., Giffin, W.C., Rockwell, T.H. & Thomas, M. (1986). Modeling fault diagnosis as the activation and use of a frame system. Human Factors, 28(6), 703-716.
65. Sweller, J., van Merrienboer, J.J.G. & Paas, F.G.W.C. (1998). Cognitive architecture and instructional design. Educ. Psychol. Rev., 10(3), 251-296.
66. Tierney, J. (1991). Behind monty halls doors: Puzzle, debate and answer. New York Times, 140(1), 20.
67. Tufte, E.R. (1997). Visual Explanations: Images and Quantities, Evidence and Narrative, 1st ed. Cheshire: Graphics Press.
68. Tukey, J.W. (1960). Conclusions vs decisions. Technometrics, 2(4), 423-433.
69. Tukey, J.W. (1962). The future of data analysis. Ann. Math. Stat., 33(1), 1-67.
70. Tukey, J.W. & Wilk, M.B. (1966). Data analysis and statistics: An expository overview. In Proceedings of the November 7-10, 1966, Fall Joint Computer Conference, pp. 695-709. New York: ACM.
71. Tversky, A. & Kahneman, D. (1974). Judgement under uncertainty. Sci., 185, 1124-1131.
72. Tversky, A. & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Sci., 211(4481), 453-458.
73. Unwin, A. (2001). Patterns of data analysis? J. Korean Stat. Soc., 30(2), 219-230.
74. Varian, H. (2009). Hal Varian on how the web challenges managers. McKinsey Quarterly, 45(1), 1.
75. Velleman, P.F. (1997). The philosophical past and the digital future of data analysis. In The Practice of Data Analysis: Essays in Honor of John W. Tukey, Eds. D.R. Brillinger, L.T. Fernholz & S. Morgenthaler, pp. 317-337. Princeton: Princeton University Press.
76. Viertl, R. (2002). On the future of data analysis. Aust. J. Stat., 31(2&3), 241-244.
77. Vosniadou, S., Brewer W.F. & University of Illinois at Urbana-Champaign. Center for the Studyof Reading, University. (1989). The Concept of the Earth's Shape: A Study of Conceptual Change in Childhood. Champaign: University of Illinois at Urbana-Champaign.
78. Weick, K.E. (1995). Sensemaking in Organizations (Foundations for Organizational Science). Thousand Oaks: Sage Publications, Inc.
79. Weick, K.E., Sutcliffe, K.M. & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organ. Sci., 16(4), 409-421.
80. Wertheimer, M. (1938). Laws of organization in perceptual forms. In A Source Book of Gestalt Psychology, pp. 71-88. Gouldsboro: Gestalt Journal Press.
81. Wild, C.J. (1994). Embracing the "wider view" of statistics. Am. Stat., 48(2), 163-171.
82. Wild, C.J. & Pfannkuch, M. (1999). Statistical thinking in empirical enquiry. Int. Stat. Rev., 67(3), 223-248.
83. Woodworth, R.S. (1938). Experimental Psychology. New York: Henry Holt and Company.
84. Wu, A., Zhang, X.L. & Cai, G. (2010). An interactive sensemaking framework for mobile visual analytics. In Proceedings of the 3rd International Symposium on Visual Information Communication, p.22. New York: ACM.
85. Yi, J.S., Kang, Y.A., Stasko, J.T. & Jacko, J.A. (2008). Understanding and characterizing insights: How do people gain insights using information visualization? In Beliv '08: Proceedings of the 2008 Conference on Beyond Time and Errors, pp. 1-6. New York, NY, USA: ACM.
86. Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Sci., 21(2), 179-217.
87. Zhang, J. (2000). External representations in complex information processing tasks. Encyclopedia Libr. Inf. Sci., 68, 164-180.
88. Zhang, P. (2010). Sensemaking: Conceptual changes, cognitive mechanisms, and structural representations. A qualitative user study. Available at http://drum.lib.umd.edu/bitstream/1903/10371/1/Zhang_umd_0117E_11214.pdf.