A Test of Criteria Used to Select Human Performance Models

  • William J. Cody
  • William B. Rouse


Earlier in this volume, Rouse & Cody (1989) discussed the nature of complex system design and designers’ use of human performance models. We noted that in our experiences designers show little interest in using the types of analytical model that were the subject of the present workshop. These impressions are based on interviews that we conducted with over 60 crew system designers in field studies of the aerospace system design process (Cody, 1988; Rouse & Cody, 1988). We hypothesized that designers apply seven specific criteria when evaluating information sources, and that models are generally perceived to be weak along these criteria relative to their alternatives.


Discriminant Function Model Developer System Developer Modeling Technology Importance Rating 
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  1. Anderson, N.H., 1961, Scales and statistics: Parametric and nonparametric, Psychological Bulletin 58: 305–316.Google Scholar
  2. Bettman, J.R., 1986, Consumer psychology, Annual Review of Psychology 37: 257–290.CrossRefGoogle Scholar
  3. Bettman, J.R., 1979, An Information Processing Theory of Consumer Choice Addison-Wesley, Reading, MA.Google Scholar
  4. Bettman, J.R., and Park, C.W., 1980, Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: A protocol analysis, Journal of Consumer Research 7: 234–248.Google Scholar
  5. Blanchard, B.S., and Fabrycky, W.J., 1981, Systems Engineering and Analysis Prentice-Hall, Englewood Cliffs, N.J.Google Scholar
  6. Boff, K.R., 1988, The value of research is in the eye of the beholder, Human Factors Society Bulletin 31 (6): 1–4.Google Scholar
  7. Boneau, C.A., 1960, The effects of violations of assumptions underlying the t test, Psychological Bulletin 57: 49–64.Google Scholar
  8. Cody, W.J., 1988, “Recommendations for Supporting Helicopter Crew System Design,” Letter Report No. 351, U.S. Army Human Engineering Laboratory, Aberdeen Proving Ground, MD.Google Scholar
  9. Dawes, R.M., 1979, The robust beauty of improper linear models in decision making, American Psychologist 34: 571–582.Google Scholar
  10. Huber, J., and McCann, J., 1982, The impact of inferential beliefs on product evaluations, Journal of Marketing Research 19: 324–333.Google Scholar
  11. Kiel, G.C., and Layton, R.A., 1981, Dimensions of consumer information seeking behavior, Journal of Marketing Research 18: 233–239.Google Scholar
  12. Meyer, R.J., 1981, A model of multiattribute judgments under attribute uncertainty and information constraint, Journal of Marketing Research 18: 428–441.Google Scholar
  13. Meyer, R.J., 1982, A descriptive model of consumer information search behavior, Marketing Science 1: 93–121.Google Scholar
  14. Nisbett, R.E., Krantz, D.H., Jepson, C., and Kunda, Z., 1983, The use of statistical heuristics in everyday reasoning, Psychological Review 90: 339–363.Google Scholar
  15. Nisbett, R.E., and Wilson, T.D., 1977, Telling more than we can know: Verbal reports on mental processes, Psychological Review 84: 231–259.Google Scholar
  16. Punj, G.N., and Staelin, R., 1983, A model of consumer information search behavior for new automobiles, Journal of Consumer Research 9: 366–380.Google Scholar
  17. Reilly, M.D., and Conover, J.N., 1983, Meta-analysis: Integrating results from consumer research studies, Advances in Consumer Research 10: 510–513.Google Scholar
  18. Rouse, W.B., 1986, On the value of information in system design: A framework for understanding and aiding designers, Information Processing and Management 22: 217–228.CrossRefGoogle Scholar
  19. Rouse, W.B., 1987, Much ado about data, Human Factors Society Bulletin 30(9): 1–3. Rouse, W.B., and Cody, W.J., 1989, Designers’ criteria for choosing human performance models, this volume.Google Scholar
  20. Rouse, W.B., and Cody, W.J., 1988, On the design of man-machine systems: Principles, practices and prospects, Automatica 24: 227–238.zbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • William J. Cody
    • 1
  • William B. Rouse
    • 1
  1. 1.Search Technology, Inc.NorcrossGeorgia

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