IDEAS home Printed from https://ideas.repec.org/a/jss/jstsof/v073i05.html
   My bibliography  Save this article

Biometrics and Psychometrics: Origins, Commonalities and Differences

Author

Listed:
  • Gower, John

Abstract

Starting with the common origins of biometrics and psychometrics at the beginning of the twentieth century, the paper compares and contrasts subsequent developments, informed by the author's 35 years at Rothamsted Experimental Station followed by a period with the data theory group in Leiden and thereafter. Although the methods used by biometricians and psychometricians have much in common, there are important differences arising from the different fields of study. Similar differences arise wherever data are generated and may be regarded as a major driving force in the development of statistical ideas.

Suggested Citation

  • Gower, John, 2016. "Biometrics and Psychometrics: Origins, Commonalities and Differences," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 73(i05).
  • Handle: RePEc:jss:jstsof:v:073:i05
    DOI: http://hdl.handle.net/10.18637/jss.v073.i05
    as

    Download full text from publisher

    File URL: https://www.jstatsoft.org/index.php/jss/article/view/v073i05/v73i05.pdf
    Download Restriction: no

    File URL: https://libkey.io/http://hdl.handle.net/10.18637/jss.v073.i05?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Pieter Kroonenberg & Jan Leeuw, 1980. "Principal component analysis of three-mode data by means of alternating least squares algorithms," Psychometrika, Springer;The Psychometric Society, vol. 45(1), pages 69-97, March.
    2. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    3. Albers, Casper J. & Gower, John C., 2014. "A contribution to the visualisation of three-way arrays," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 1-8.
    4. Jan Leeuw, 1982. "Generalized eigenvalue problems with positive semi-definite matrices," Psychometrika, Springer;The Psychometric Society, vol. 47(1), pages 87-93, March.
    5. David R. Cox, 2015. "A Conversation with John C. Gower," International Statistical Review, International Statistical Institute, vol. 83(3), pages 339-356, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    2. Henk Kiers, 1991. "Hierarchical relations among three-way methods," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 449-470, September.
    3. Willem Kloot & Pieter Kroonenberg, 1985. "External analysis with three-mode principal component models," Psychometrika, Springer;The Psychometric Society, vol. 50(4), pages 479-494, December.
    4. Pieter M. Kroonenberg & Cornelis J. Lammers & Ineke Stoop, 1985. "Three-Mode Principal Component Analysis of Multivariate Longitudinal Organizational Data," Sociological Methods & Research, , vol. 14(2), pages 99-136, November.
    5. Elisa Frutos-Bernal & Ángel Martín del Rey & Irene Mariñas-Collado & María Teresa Santos-Martín, 2022. "An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition," Mathematics, MDPI, vol. 10(7), pages 1-17, March.
    6. Giuseppe Brandi & Ruggero Gramatica & Tiziana Di Matteo, 2019. "Unveil stock correlation via a new tensor-based decomposition method," Papers 1911.06126, arXiv.org, revised Apr 2020.
    7. Wilderjans, Tom & Ceulemans, Eva & Van Mechelen, Iven, 2009. "Simultaneous analysis of coupled data blocks differing in size: A comparison of two weighting schemes," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1086-1098, February.
    8. Kiers, Henk A. L., 1998. "Three-way SIMPLIMAX for oblique rotation of the three-mode factor analysis core to simple structure," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 307-324, September.
    9. Henk Kiers & Pieter Kroonenberg & Jos Berge, 1992. "An efficient algorithm for TUCKALS3 on data with large numbers of observation units," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 415-422, September.
    10. Kohei Adachi, 2009. "Joint Procrustes Analysis for Simultaneous Nonsingular Transformation of Component Score and Loading Matrices," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 667-683, December.
    11. Michel Velden & Tammo Bijmolt, 2006. "Generalized canonical correlation analysis of matrices with missing rows: a simulation study," Psychometrika, Springer;The Psychometric Society, vol. 71(2), pages 323-331, June.
    12. Paolo Giordani & Roberto Rocci & Giuseppe Bove, 2020. "Factor Uniqueness of the Structural Parafac Model," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 555-574, September.
    13. Alwin Stegeman & Tam Lam, 2014. "Three-Mode Factor Analysis by Means of Candecomp/Parafac," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 426-443, July.
    14. Caterina Liberati & Paolo Mariani, 2012. "Banking customer satisfaction evaluation: a three-way factor perspective," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 6(4), pages 323-336, December.
    15. Kenta Mitsushita & Shin Murakoshi & Masato Koyama, 2023. "How are various natural disasters cognitively represented?: a psychometric study of natural disaster risk perception applying three-mode principal component analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(1), pages 977-1000, March.
    16. Werner Kunz, 2007. "Visualization of competitive market structure by means of choice data," Computational Statistics, Springer, vol. 22(4), pages 521-531, December.
    17. Roberto Rocci & Jos Berge, 2002. "Transforming three-way arrays to maximal simplicity," Psychometrika, Springer;The Psychometric Society, vol. 67(3), pages 351-365, September.
    18. Bilian Chen & Zhening Li & Shuzhong Zhang, 2015. "On optimal low rank Tucker approximation for tensors: the case for an adjustable core size," Journal of Global Optimization, Springer, vol. 62(4), pages 811-832, August.
    19. Timmerman, Marieke E. & Kiers, Henk A. L., 2002. "Three-way component analysis with smoothness constraints," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 447-470, September.
    20. Jos Berge & Jan Leeuw & Pieter Kroonenberg, 1987. "Some additional results on principal components analysis of three-mode data by means of alternating least squares algorithms," Psychometrika, Springer;The Psychometric Society, vol. 52(2), pages 183-191, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jss:jstsof:v:073:i05. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.jstatsoft.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.