## Abstract

The Psychometric Society is “devoted to the development of Psychology as a quantitative rational science”. Engineering is often set in contradistinction with science; art is sometimes considered different from science. Why, then, juxtapose the words in the title:*psychometric, engineering*, and*art?* Because an important aspect of quantitative psychology is problem-solving, and engineering solves problems. And an essential aspect of a good solution is beauty—hence, art. In overview and with examples, this presentation describes activities that are quantitative psychology as engineering and art—that is, as design. Extended illustrations involve systems for scoring tests in realistic contexts. Allusions are made to other examples that extend the conception of quantitative psychology as engineering and art across a wider range of psychometric activities.

## Key words

psychometrics quantitative psychology design## Preview

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## References

- Allen, N.L., Carlson, J.E., & Zelenak, C.A. (1999).
*The NAEP 1996 technical report*. Washington, DC: U.S. Department of Education, Office of Educational Research and Improvement.Google Scholar - Baker, F.B., & Harwell, M.R. (1996). Computing elementary symmetric functions and their derivatives: A didactic.
*Applied Psychological Measurement, 20*(2), 169–192.Google Scholar - Barr, A.H. (1946).
*Picasso: Fifty years of his art*. New York, NY: The Museum of Modern Art.Google Scholar - Berkson, J. (1944). Application of the logistic function to bio-assay.
*Journal of the American Statistical Association*.*39*, 357–375.Google Scholar - Berkson, J. (1953). A statistically precise and relatively simple method of estimating the bio-assay with quantal response, based on the logistic function.
*Journal of the American Statistical Association, 48*, 565–599.Google Scholar - Birnbaum, A. (1968). Some latent trait models and their use in inferring an examinee's ability. In F.M. Lord & M.R. Novick,
*Statistical theories of mental test scores*(pp. 395–479). Reading, MA: Addison-Wesley.Google Scholar - Bock, R.D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: An application of the EM algorithm.
*Psychometrika, 46*, 443–459.CrossRefGoogle Scholar - Bock, R.D., & Lieberman, M. (1970). Fitting a response model for
*n*dichotomously scored items.*Psychometrika, 35*, 179–197.Google Scholar - Bock, R.D., & Mislevy, R.J. (1981). An item response curve model for matrix-sampling data: The California grade-three assessment.
*New Directions for Testing and Measurement, 10*, 65–90.Google Scholar - Bock, R.D., & Mislevy, R.J. (1982). Adaptive EAP estimation of ability in a microcomputer environment.
*Applied Psychological Measurement, 6*, 431–444.Google Scholar - Box, G.E.P. (1979). Some problems of statistics and everday life.
*Journal of the American Statistical Association, 74*, 1–4.Google Scholar - Brooks, F.P. (1996). The computer scientist as toolsmith II.
*Communications of the ACM, 39*, 61–68.Google Scholar - Brooks, F.P. (in press). The design of design.
*Communications of the ACM*.Google Scholar - Chen, W.H. (1995).
*Estimation of item parameters for the three-parameter logistic model using the marginal likelihood of summed scores*. Unpublished doctoral dissertation, The University of North Carolina at Chapel Hill.Google Scholar - Chen, W.H., & Thissen, D. (1999). Estimation of item parameters for the three-parameter logistic model using the marginal likelihood of summed scores.
*British Journal of Mathematical and Statistical Psychology, 52*, 19–37.CrossRefGoogle Scholar - Cronbach, L.J., Gleser, G.C., Nanda, H., & Rajaratnam, N. (1972).
*The dependability of behavioral measurements: Theory of generalizability for scores and profiles*. New York, NY: John Wiley & Sons.Google Scholar - Finney, D.J. (1952).
*Probit analysis: A statistical treatment of the sigmoid response curve*. London: Cambridge University Press.Google Scholar - Fischer, G.H. (1974).
*Einführung in die Theorie psychologischer Tests*[Introduction to the theory of psychological tests]. Bern: Huber.Google Scholar - Fischer, G.H., & Allerup, P. (1968). Rechentchnische Fragen zu Raschs eindimensionalem Model [An inquiry into computational techniques for the Rasch model]. In G.H. Fischer (Ed.),
*Psychologische Testtheorie*(pp. 269–280). Bern: Huber.Google Scholar - Goldstein, A. (2001, March 12). Making another big score.
*Time, 157*, 66–67.Google Scholar - Henriques, D.B., & Steinberg, J. (2001, May 20). Errors plague testing industry.
*The New York Times*, pp. A1, A22–A23.Google Scholar - Jones, L.V. (1998). L.L. Thurstone's vision of psychology as a quantitative rational science. In G.A. Kimble & M. Wertheimer (Eds.),
*Portraits of pioneers in psychology, Vol III*(pp. 84–102). Washington, DC: American Psychological Association; Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Kelley, T.L. (1927).
*The interpretation of educational measurements*. New York, NY: World Book.Google Scholar - Kelley, T.L. (1947).
*Fundamentals of statistics*. Cambridge: Harvard University Press.Google Scholar - Lazarsfeld, P.F. (1950). The logical and mathematical foundation of latent structure analysis. In S.A. Stouffer, L. Guttman, E.A. Suchman, P.F. Lazarsfeld, S.A. Star, & J.A. Clausen,
*Measurement and prediction*(pp. 362–412). New York, NY: John Wiley & Sons.Google Scholar - Laidlaw, D.H., Fleischer, K.W., & Barr, A.H. (1995, September).
*Bayesian mixture classification of MRI data for geometric modeling and visualization*. Poster presented at the First International Workshop on Statistical Mixture Modeling, Aussois, France. (Retrieved from the Worldwide Web: http://www.gg.caltech.edu/~dhl/aussois/paper.html)Google Scholar - Lewis, B. (1996, March 15). IS survival guide.
*Infoworld, 21*, p. 96.Google Scholar - Lewis, B. (2001, March 19). IS survival guide.
*Infoworld, 23*, p. 42.Google Scholar - Lindley, D.V., & Smith, A.F.M. (1972). Bayes estimates for the linear model.
*Journal of the Royal Statistical Society, Series B, 34*, 1–41.Google Scholar - Liou, M. (1994). More on the computation of higher-order derivatives of the elementary symmetric functions in the Rasch model.
*Applied Psychological Measurement, 18*, 53–62.Google Scholar - Lord, F.M. (1953). The relation of test score to the trait underlying the test.
*Educational and Psychological Measurement, 13*, 517–548.Google Scholar - Lord, F.M., & Novick, M. (1968).
*Statistical theories of mental test scores*. Reading, MA: Addison Wesley.Google Scholar - Lord, F.M., & Wingersky, M.S. (1984). Comparison of IRT true-score and equipercentile observed-score “equatings”.
*Applied Psychological Measurement, 8*, 453–461.Google Scholar - Mislevy, R.M., Johnson, E.G., & Muraki, E. (1992). Scaling procedures in NAEP.
*Journal of Educational Statistics, 17*, 131–154.Google Scholar - Muraki, E. (1992). A generalized partial credit model: Application of an EM algorithm.
*Applied Psychological Measurement, 16*, 159–176.Google Scholar - Muraki, E. (1997). A generalized partial credit model. In W. van der Linden & R.K. Hambleton (Eds.),
*Handbook of modern item response theory*(pp. 153–164). New York, NY: Springer.Google Scholar - Novick, M.R. (1980). Statistics as psychometrics.
*Psychometrika, 45*, 411–424.CrossRefGoogle Scholar - Orlando, M. (1997).
*Item fit in the context of item response theory*. Unpublished doctoral dissertation, The University of North Carolina at Chapel Hill.Google Scholar - Orlando, M., & Thissen, D. (2000). New item fit indices for dichotomous item response theory models.
*Applied Psychological Measurement, 24*, 50–64.Google Scholar - Picasso, P. (1923). Picasso speaks—A statement by the artist.
*The Arts, 3*, 315–326.Google Scholar - Rasch, G. (1960).
*Probabilistic models for some intelligence and attainment tests*. Copenhagen: Denmarks Paedagogiske Institut. (Republished in 1980 by the University of Chicago Press of Chicago)Google Scholar - Raz, J., Turetsky, B.I., & Dickerson, L.W. (2001). Inference for a random wavelet packet model of single-channel event-related potentials.
*Journal of the American Statistical Association, 96*, 409–420.CrossRefGoogle Scholar - Robbins, H. (1952). Some aspects of the sequential design of experiments.
*Bulletin of the American Mathematical Soceity, 58*, 527–535.Google Scholar - Rosa, K., Swygert, K., Nelson, L., & Thissen, D. (2001). Item response theory applied to combinations of multiple-choice and constructed-response items—scale scores for patterns of summed scores. In D. Thissen & H. Wainer (Eds),
*Test scoring*(pp. 253–292). Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores.
*Psychometric Monograph*, No. 17.Google Scholar - Samejima, F. (1997). Graded response model. In W. van der Linden & R.K. Hambleton (Eds.),
*Handbook of modern item response theory*(pp. 85–100). New York, NY: Springer.Google Scholar - Thissen, D., Nelson, L., Rosa, K., & McLeod, L.D. (2001). Item response theory for items scored in more than two categories. In D. Thissen & H. Wainer (Eds),
*Test scoring*(pp. 141–186). Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Thissen, D., Nelson, L., & Swygert, K. (2001). Item response theory applied to combinations of multiple-choice and constructed-response items—Approximation methods for scale scores. In D. Thissen & H. Wainer (Eds),
*Test scoring*(pp. 293–341). Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Thissen, D., & Orlando, M. (2001). Item response theory for items scored in two categories. In D. Thissen & H. Wainer (Eds),
*Test scoring*(pp. 73–140). Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Thissen, D., Pommerich, M., Billeaud, K., & Williams, V.S.L. (1995). Item response theory for scores on tests including polytomous items with ordered responses.
*Applied Psychological Measurement, 19*, 39–49.Google Scholar - Thissen, D. & Wainer, H. (Eds.) (2001)
*Test scoring*. Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Thurstone, L.L. (1925). A method of scaling psychological and educational tests.
*Journal of Educational Psychology, 16*, 433–449.Google Scholar - Thurstone, L.L. (1927). The law of comparative judgment.
*Psychological Review, 34*, 278–286.Google Scholar - Thurstone, L.L. (1937). Psychology as a quantitative rational science.
*Science, 85*, 227–232.Google Scholar - Thurstone, L.L. (1938).
*Primary mental abilities*. Chicago, IL: University of Chicago Press.Google Scholar - Tukey, J.W. (1961).
*Data analysis and behavioral science or learning to bear the quantitative man's burden by shunning badmandments*. Unpublished manuscript. (Reprinted in*The collected works of John W. Tukey, Vol III, Philosophy and principles of data analysis: 1949–1964*, pp. 187–389 by L.V. Jones (Ed.), 1986, Monterey, CA: Wadsworth & Brooks-Cole)Google Scholar - Tukey, J.W. (1962). The future of data analysis.
*Annals of Mathematical Statistics, 33*, 1–67. (Reprinted in*The collected works of John W. Tukey, Vol III, Philosophy and principles of data analysis: 1949–1964*, pp. 391–484 by L.V. Jones (Ed.), 1986, Monterey, CA: Wadsworth & Brooks-Cole)Google Scholar - Verhelst, N.D., & Veldhuijzen, N.H. (1991).
*A new algorithm for computing elementary symmetric functions and their first and second derivatives*(Measurement and Research Department Rep. 91-1). Arnhem, The Netherlands: Netherlands Central Bureau of Statistics.Google Scholar - Wainer, H., Vevea, J.L., Camacho, F., Reeve, B, Rosa, K., Nelson, L., Swygert, K., & Thissen, D. (2001). Augmented scores—“borrowing strength” to compute scores based on small numbers of items. In D. Thissen & H. Wainer (Eds),
*Test scoring*(pp. 343–387). Mahwah, NJ: Lawrence Erlbaum & Associates.Google Scholar - Williams, V.S.L., Pommerich, M., & Thissen, D. (1998). A comparison of developmental scales based on Thurstone methods and item response theory.
*Journal of Educational Measurement, 35*, 93–107.Google Scholar - Yen, W.M. (1984). Obtaining maximum likelihood trait estimates from number-correct scores for the three-parameter logistic model.
*Journal of Educational Measurement, 21*, 93–111.CrossRefGoogle Scholar