- Stanford University, Stanford, California, 1958-62. B.A., Psychology
- University of Southern California, Los Angeles, California, 1963-67. M.A., Psychology; Ph.D., Psychometrics
- Assistant Professor, Psychology, University of North Carolina (1967-72)
- Associate Professor, Psychology, University of North Carolina (1972-77)
- Director, Cognitive Science Program, University of North Carolina (1989-92)
- Professor, Biostatistics, University of North Carolina (1991-94)
- Professor, Psychology, University of North Carolina (1977-2001)
- Professor Emeritus, Psychology, University of North Carolina (2001-Present)
- University of California at Irvine, Irvine, California (1973)
- State University of Leiden, Leiden, The Netherlands (1973-74)
- University of Queensland, Brisbane, Australia (1980)
- University of Southern California, Los Angeles, California (1981)
- Ohio State University, Columbus, Ohio (1982)
- Australian Graduate School of Management, Sydney, Australia (1984-85)
- National University of Singapore, Singapore (1992-93)
- University of Valencia, Valencia, Spain (2000-2001)
Long-Term Computational Statistics Consultantships
- SAS Institute, Inc., Cary, NC (1980-87)
- BMDP Statistical Software, Inc., Los Angeles, CA (1987-88)
- Bell Telephone Laboratories, Holmdel, NJ (1989-90)
- Statistical Sciences, Inc., Seattle, WA (1991-96)
Forrest W. Young, Professor of Psychometrics at the University of North Carolina at Chapel Hill, received his PhD in Psychometrics from the University of Southern California in 1967. He has been on the faculty of UNC-CH ever since.
Prof. Young's teaching interests focus on "Seeing what your data seem to say". This visually intuitive approach to statistics helps to clarify the meaning of data. His courses, ranging from his introductory undergraduate course on Psychological Statistics, to his advanced graduate courses on Data Analysis, Visualization and Exploration, reflect this focus.
To make the process of understanding data visually intuitive, the burden is moved from the person to the computer. You don't need to make an intensive effort to understand your data: Rather, your computer makes intensive calculations so that the data can be shown to you in a visually comprehensible way.
This approach to the role of computers is based on the intelligence augmentation (IA) philosophy of Computer Science: Your computer is a device which should augment your intelligence. It is also based on a Cognitive Science theory for the construction of an environment for data analysis.
Prof Young and his students, over the course of a 10-year research and development project, have created ViSta, a visual statistics system instantiating Prof. Young's theories concerning visual environments for statistical analysis.
ViSta is a freely available system that is being used by researchers around the world to better understand their data, by teachers of introductory, itermediate, multivariate, graphical and computational statistics, and for advanced research and development in graphical and computational statistics.
ViSta is based not only on Prof. Young's theory-based approach to data analysis, but also on his 30-year career in computational and graphical statistics.
Prof. Young's early research interests focused on Multidimensional and Nonlinear Multivariate Data Analysis (for which he was elected the President of the Psychometric Society, and received the American Market Research Association's O'Dell award, both in 1981). Via these research interests, Prof. Young became involved in software development early in his career.
Prof. Young has served as a professional consultant on statistical system interface design with SAS Institute, Statistical Sciences (the S-Plus system), and BMDP Inc. He has written or designed data analysis modules for the SAS, SPSS and IMSL systems. He is a member of the American Statistical Association's sections on Computational and Graphical Statistics.
Prof. Young is retired from teaching, but continues to spend much of his time working on the final version of ViSta, and with his colleagues Michael Friendly, Pedro Valero, and Gabriel Molina, on a book entitled “Seeing Your Data’s Story”.
Young, F.W. A multidimensional similarities analysis of twelve choice probability learning with payoffs. Dissertation Abstracts, 1967, 27, 308-B.
Schiffman, S.S., Reynolds, M.L. & Young, F.W. (1981). Introduction to Multidimensional Scaling. New York: Academic Press.
Young, F.W. & Sarle, W.S. (1982). Exploratory Multivariate Data Analysis. Cary, NC: SAS Institute, Inc.
Young, F.W. & Hamer, R.M. (1987). Multidimensional Scaling: History, Theory and Applications. New York: Erlbaum Associates. (reprinted, 1994)
Young, F.W., Valero, P. & Friendly, M. (in preparation). Seeing the Story in Your Data
Young, F.W. & Rheingans, P. (1989) Visualizing Multivariate Structure with VISUALS/Pxpl. Chapel Hill, NC: UNC Psychometric Laboratory.
Major Articles on Computational & Graphical Statistics
(research focus, 1990-present)
Young, F.W. Visualizing Six-Dimensional Structure with Dynamic Statistical Graphics. Chance, 1989, 2, 22-30.
Young, F.W., Kent, D.P. & Kuhfeld, W.F. Dynamic Graphics for Exploring Multivariate Data. In: Cleveland, W.S. & McGill, M. Dynamic Graphics for Statistics. Wadsworth. 1990, 391-424.
Young, F.W. & Rheingans, P. High-Dimensional Depth Cuing for Guided Tours of Multivariate Data. In: Buja, A. & Tukey, P.A. (Eds.) Computing and Graphics in Statistics, 1991, 36, 239-252. New York: Springer-Verlag.
Young, F.W. & Smith, J.B. Towards a Structured Data Analysis Environment: A Cognition-Based Design. In: Buja, A. & Tukey, P.A. (Eds.) Computing and Graphics in Statistics, 1991, 36, 253-279. New York: Springer-Verlag.
Young, F.W. & Rheingans, P. Visual Interpretation of Hyperdimensional Data. IBM Journal of Research and Development (special issue on Visual Interpretation of Data), 1991, 35, 97-107
Young, F.W., Faldowski, R.A. & McFarlane, M.M. Multivariate Statistical Visualization. In: Rao, C.R. (Ed.) Handbook of Statistics, 1993, 9, 959-998.
McFarlane, M. & Young, F.W. Graphical Sensitivity Analysis for Multidimensional Scaling. J. Computational and Graphical Statistics, 1994, 3, 23-34.
Young, F.W. ViSta: The Visual Statistics System. L.L. Thurstone Psychometric Laboratory, Research Memorandum 94-1. (1994, Revised 1996)
Young, F.W. & Lubinsky, D.J. Guiding Data Analysts with Visual Statistical Strategies. J. Computational and Graphical Statistics, 4(4), 1995, pp. 229-250.
Young, F.W. & Bann, Carla, M. Data Analysis using ViSta. In: Stine, R.A. & Fox, J. (Eds.), Statistical Computing Environments for Social Research. Sage Publications. 1996, pp. 207-236.
Valero-Mora, P.M., Young, F.W. & Friendly, M. Visualizing Categorical Data in ViSta. Proceedings of the Data Visualization Conference, 2001.
Young, F. W., Ledesma, R., Molina, J. G. y Valero, P. (2001). ViSta "The VIsual STAtistics system". Revista de Metodologica de Encuestas. Vol. 3. N. 1, 127-133. SIPIE.
Ledesma, R., Valero, P. & Young, F. W. (2002). Analisis de homogeneidad en ViSta "The VIsual STAtistics system". Metodologica de las Ciencias del Comportamiento. Vol. 4 N. 1. 139-149. AEMCCO
Valero, P., & Young, F. W. (2002). Computing and Visualizing Log-linear analysis interactively. Journal of Statistical Software. Vol. 7. N6.
Young, F.W., Valero-Mora, P., Faldowski, R.A. & Bann, C. Gossip: On the Archiecture of SpreadPlots. J. Computational and Graphical Statistics, (2003, in press)
Valero, P., Young, F. W. & Friendly, M. . Categorical Data Analysis in ViSta. Computational Statistics and Data Analysis (2003, in press)
de Leeuw, J., Young, F.W. & Takane, Y. Additive structure in qualitative data: An alternatng least squares method with optimal scaling features. Psychometrika, 1976, 41, 471-502.
Young, F.W., de Leeuw, J. & Takane, Y. Multiple and canonical regression with a mix of quantitative and qualitative variables: An alternating least squares method with optimal scaling features. Psychometrika, 1976, 41, 505-530.
Young, F.W., Takane, Y. & de Leeuw, J. The principal components of mixed measurement level multivariate data: An alternating least squares algorithm with optimal scaling features. Psychometrika, 1978, 43, 279-282.
Takane, Y., Young, F.W. & de Leeuw, J. Nonmetric common factor analysis: An alternating least squares method with optimal scaling. Behaviormetrika, 1979, 6, 45-56.
Perreault, W.D. & Young, F.W. Alternating least squares optimal scaling in market research. Journal of Marketing Research, 1980, 17, 1-13. Reprinted in Fornell, C. (Ed.) A second generation of multivariate analysis: Vol. II. New York: Praeger, 1982.
Sands, R. & Young, F.W. Component models for three-way data: An alternating least squares algorithm with optimal scaling features. Psychometrika, 1980, 45, 39-67.
Takane, Y., Young, F.W., de Leeuw, J. An individual differences additive model: An alternating least squares method with optimal scaling features. Psychometrika, 1980, 45, 183-209.
Young, F.W. Quantitative analysis of qualitative date. Psychometrika, 1981, 46, 357-388.
Tenenhaus, M. & Young, F.W. An analysis and synthesis of multiple correspondence analysis, optimal scaling, dual scaling, homogeneity analysis, and other methods for quantifying categorical multivariate data. Psychometrika, 1985, 50, 91-119.
Cliff, N. & Young, F.W. On the relation between unidimensional judgments and multidimensional scaling. Organizational Behavior and Human Performance, 1968, 3, 269-285.
Young, F.W. Nonmetric multidimensional scaling: Recovery of metric information. Psychometrika, 1970, 35 455-474.
Jones, L.E. & Young, F.W. The structure of a social environment: A longitudinal individual differences scaling of an intact group. Journal of Personality and Social Psychology, 1972, 24 108-121.
Young, F.W. A model for polynomial conjoint analysis algorithms. In R. Shepard, A.D. Romney & S. Nerlove (Eds.) Multidimensional Scaling: Theory and applications in the social sciences. Volume I. Theory. New York: Seminar Press, 1972, 69-104.
Young, F.W. Scaling replicated conditional rank order data. In D. Heisse (Ed.) Sociological methodology. American Sociological Association, 1974, 129-170.
Young, F.W. Methods for describing ordinal data with cardinal models. Journal of Mathematical Psychology, 1975, 12, 416-436.
Takane, Y., Young, F.W. & de Leeuw, J. Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features. Psychometrika, 1977, 42, 7-67.
Young, F.W. Scaling. Annual Review of Psychology, 1984, 35, 55-81.
Young, F.W. The General Euclidean model. In Law, H.G., Snyder, C.W., Hattie, J.A. & McDonald, R.P. (Eds.), Research Methods for Multivariate Data Analysis. New York: Praeger, 1984, pp. 440-469.
Young, F.W. Multidimensional scaling. In: Kotz, S. & Johnson, N.L. (Eds.), Encyclopedia of Statistical Sciences, Vol. 5. New York: Wiley, 1985, 649-659.
Young, F.W. & Harris, D.F. Multidimensional Scaling: Procedure ALSCAL. In: Norusis, J.J. SPSS Base System User's Guide. 1990, 396-461. SPSS Inc., Chicago, IL. Reprinted in: Norusis, J.J. SPSS Profressonal Statistics, 1994, 155-222. SPSS Inc., Chicago, IL.