2000-- Publications (Reverse chronological order; full paper available with some summaries)

• Harshman, R. A., Hong, S., & Lundy, M. E. (2003). Shifted factor analysis--Part I: Models and properties. J. Chemometrics , 17, 363-378.
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• Hong, S., & Harshman, R. A. (2003). Shifted factor analysis--Part II: Algorithms. J. Chemometrics, 17, 379-388.
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• Hong, S., & Harshman, R. A. (2003). Shifted factor analysis--Part III: N-way generalization and application. J. Chemometrics , 17, 389-399.
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• Swartzman, L. C., Harshman, R. A., Burkell, J., & Lundy, M. E. (2002). What accounts for the appeal of complementary/alternative medicine, and what makes complementary/alternative medicine "alternative"?. Medical Decision Making, 22, 431-450.
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• Thomas, C. G., Harshman, R. A., & Menon, R. S. (2002). Noise reduction in BOLD-based fMRI using component analysis. NeuroImage, 17, 1521-1537.
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• Harshman, R. A., & Hong, S. (2002). 'Stretch' vs. 'slice' methods for representing three-way structure via matrix notation. Journal of Chemometrics, 16, 198-205.
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• Harshman, R. A. (2001). An index formalism that generalizes the capabilities of matrix notation and algebra to n-way arrays. Journal of Chemometrics, 15, 689-714.
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1990-1999 Publications (Reverse chronological order)

• Do, T., McIntyre, N. S., Harshman, R. A., Lundy, M. E., & Splinter, S. J. (1999). Application of Parallel Factor Analysis and X-ray photoelectron spectroscopy to the initial stages in oxidation of aluminium. Surface and Interface Analysis, 27, 618-628.
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• Hopke, P. K., Paatero, P., Jia, H., Ross, R. T., & Harshman, R. A. (1998). Three-way (PARAFAC) factor analysis: Examination and comparison of alternative computational methods as applied to ill-conditioned data. Chemometrics and Intelligent Laboratory Systems, 43, 25-42.
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• Mulaik, S. A., Raju, N. S., & Harshman, R. A. (1997). There is a time and place for significance testing. In L. L. Harlow, S. A. Mulaik & J. J. Steiger (Eds.), What if there were no significance tests? Multivariate applications: Vol.1 (pp. 65-115). Mahwah, N.J.: Lawrence Erlbaum Associates.
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• Kiers, H. A. L., & Harshman, R. A. (1997). Relating two proposed methods for speedup of algorithms for fitting two- and three-way principal component and related multilinear models. Chemometrics and Intelligent Laboratory Systems, 36, 31-40.
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• Harshman, R. A., & Lundy, M. E. (1996). Uniqueness proof for a family of models sharing features of Tucker's three-mode factor analysis and PARAFAC/ CANDECOMP. Psychometrika, 61, 133-154.
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• Harshman, R. A., and Lundy, M. E. (1994). PARAFAC: Parallel factor analysis. Computational Statistics and Data Analysis, 18, 39-72.
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• Harshman, R. A. (1994). Substituting statistical for physical decomposition: Are there applications for parallel factor analysis (PARAFAC) in non-destructive evaluation? In X. P. V. Maldague (Ed.), Advances in signal processing for nondestructive evaluation of materials (pp. 469-483). NATO ASI Series E: Applied Sciences, Vol. 262. Dordrecht, The Netherlands: Kluwer Academic Publishers.
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• Harshman, R. A., & Lundy, M. E. (1990). Multidimensional analysis of preference structures. In A. deFontenay, M.H. Shugard, & D.S. Sibley (Eds.), Contributions to economic analysis: Vol. 187. Telecommunications demand modeling: An integrated view (pp.185-204). Amsterdam: Elsevier.
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• Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41, 391-407.
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1980-1989 Publications (Reverse chronological order)

• Kruskal, J. B., Harshman, R. A., & Lundy, M. E. (1989). How 3-MFA can cause degenerate PARAFAC solutions, among other relationships. In R. Coppi & S. Bolasco (Eds.), Multiway data analysis (pp. 115-121). Amsterdam: Elsevier.
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• Lundy, M. E., Harshman, R. A., & Kruskal, J. B. (1989). A two-stage procedure incorporating good features of both trilinear and quadrilinear methods. In R. Coppi & S. Bolasco (Eds.), Multiway data analysis (pp. 123-130). Amsterdam: Elsevier.
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• Dawson, M. R., & Harshman, R. A. (1986). The multidimensional analysis of asymmetries in alphabetic confusion matrices: Evidence for global-to-local and local-to-global processing. Perception and Psychophysics, 40, 370-383.
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• Harshman, R. A. (1986). Review of Factor analysis: An applied approach. Journal of the American Statistical Association, 81, 864-865.
• Harshman, R. A. (1985). Review of Three-mode principal component analysis. Applied Psychological Measurement, 9, 327-332.
• DeSarbo, W. S., & Harshman, R. A. (1985). Celebrity-brand congruence analysis. Current Issues and Research in Advertising, 1, 17-52.
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• Harshman, R. A., & Lundy, M. E. (1984). The PARAFAC model for three-way factor analysis and multidimensional scaling. In H. G. Law, C. W. Snyder, Jr., J. Hattie, & R. P. McDonald (Eds.), Research methods for multimode data analysis (pp. 122-215). New York: Praeger.
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• Harshman, R. A., & Lundy, M. E. (1984). Data preprocessing and the extended PARAFAC model. In H. G. Law, C. W. Snyder, Jr., J. Hattie, & R. P. McDonald (Eds.), Research methods for multimode data analysis (pp.216-284). New York: Praeger.
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• Harshman, R. A., & DeSarbo, W. S. (1984). An application of PARAFAC to a small sample problem, demonstrating preprocessing, orthogonality constraints, and split-half diagnostic techniques. In H. G. Law, C. W. Snyder, Jr., J. Hattie, & R. P. McDonald (Eds.), Research methods for multimode data analysis (pp.602-642). New York: Praeger.
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• Harshman, R. A. (1984). "How can I know if it's real?" A catalogue of diagnostics for use with three-mode factor analysis and multidimensional scaling. In H. G. Law, C. W. Snyder, Jr., J. Hattie, & R. P. McDonald (Eds.), Research methods for multimode data analysis (pp. 566-591). New York: Praeger.
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• Harshman, R. A., Green, P. E., Wind, Y., & Lundy, M. E. (1982). A model for the analysis of asymmetric data in marketing research. Marketing Science, 1, 205-242.
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• Dunn, T., & Harshman, R. A. (1982). A multidimensional scaling model of the size-weight illusion. Psychometrika, 47, 25-45.
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• Harshman, R. A., & Berenbaum, S. A. (1981). Basic concepts underlying the PARAFAC- CANDECOMP three-way factor analysis model and its application to longitudinal data. In D. H. Eichorn, J. A. Clausen, N. Haan, M. P. Honzik, & P. H. Mussen (Eds.), Present and past in middle life (pp. 435-459). New York: Academic Press.
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1970-1979 Publications (Reverse chronological order)

• Gandour, J., & Harshman, R. A. (1978). Cross-language differences in tone perception: A multidimensional scaling investigation. Language and Speech, 21, 1-33.
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• Ladefoged, P., Harshman, R., Goldstein, L., & Rice, L. (1978). Generating vocal tract shapes from formant frequencies. Journal of the Acoustical Society of America, 64, 1027-1035.
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• Harshman, R. A., Ladefoged, P., & Goldstein, L. (1977). Factor analysis of tongue shapes. Journal of the Acoustical Society of America, 62, 693-707.
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• Harshman, R. A. (1972). PARAFAC2: Mathematical and technical notes. UCLA Working Papers in Phonetics, 22, 30-47. (University Microfilms, Ann Arbor, No. 10,085).
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• Harshman, R. A. (1972). Determination and proof of minimum uniqueness conditions for PARAFAC1. UCLA Working Papers in Phonetics, 22, 111-117. (University Microfilms, Ann Arbor, No. 10,085).
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• Lindau, M., Harshman, R., & Ladefoged, P. (1971). Factor analysis of formant frequencies of vowels. UCLA Working Papers in Phonetics, 19, 17-25. (University Microfilms, Ann Arbor, No. 10,085).
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• Harshman, R. A. (1970). Foundations of the PARAFAC procedure: Models and conditions for an "explanatory" multi-modal factor analysis. UCLA Working Papers in Phonetics, 16, 84 pp. (University Microfilms, Ann Arbor, No. 10,085).
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