IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v41y1976i2p177-188.html
   My bibliography  Save this article

Effects on indscal of non-orthogonal perceptions of object space dimensions

Author

Listed:
  • Robert MacCallum

Abstract

No abstract is available for this item.

Suggested Citation

  • Robert MacCallum, 1976. "Effects on indscal of non-orthogonal perceptions of object space dimensions," Psychometrika, Springer;The Psychometric Society, vol. 41(2), pages 177-188, June.
    • Handle: RePEc:spr:psycho:v:41:y:1976:i:2:p:177-188
    DOI: 10.1007/BF02291837
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02291837
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02291837?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    2. C. Horan, 1969. "Multidimensional scaling: Combining observations when individuals have different perceptual structures," Psychometrika, Springer;The Psychometric Society, vol. 34(2), pages 139-165, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Warren Torgerson, 1986. "Scaling and Psychometrika: Spatial and alternative representations of similarity data," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 57-63, March.
    2. Robert MacCallum & Edwin Cornelius, 1977. "A monte carlo investigation of recovery of structure by alscal," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 401-428, September.

    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. Yoshio Takane & Forrest Young & Jan Leeuw, 1977. "Nonmetric individual differences multidimensional scaling: An alternating least squares method with optimal scaling features," Psychometrika, Springer;The Psychometric Society, vol. 42(1), pages 7-67, March.
    2. 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.
    3. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised Jul 2024.
    4. 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.
    5. Xinhai Liu & Wolfgang Glänzel & Bart De Moor, 2011. "Hybrid clustering of multi-view data via Tucker-2 model and its application," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(3), pages 819-839, September.
    6. Dawn Iacobucci & Doug Grisaffe, 2018. "Perceptual maps via enhanced correspondence analysis: representing confidence regions to clarify brand positions," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(3), pages 72-83, September.
    7. D'Urso, Pierpaolo & Giordani, Paolo, 2003. "A least squares approach to Principal Component Analysis for interval valued data," Economics & Statistics Discussion Papers esdp03013, University of Molise, Department of Economics.
    8. 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.
    9. Robert MacCallum & Edwin Cornelius, 1977. "A monte carlo investigation of recovery of structure by alscal," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 401-428, September.
    10. Eric Jondeau & Emmanuel Jurczenko & Michael Rockinger, 2018. "Moment Component Analysis: An Illustration With International Stock Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(4), pages 576-598, October.
    11. Ventura L. Charlin & Steve Sussman & Clyde W. Dent & Alan W. Stacy & John W. Graham & Marny Barovich & Ginger Hahn & Dee Burton & Brian R. Flay, 1990. "Three Methods of Assessing Adolescent School-Level Experimentation of Tobacco Products," Evaluation Review, , vol. 14(3), pages 297-307, June.
    12. 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.
    13. Namgil Lee & Jong-Min Kim, 2018. "Block tensor train decomposition for missing data estimation," Statistical Papers, Springer, vol. 59(4), pages 1283-1305, December.
    14. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "Determining the number of latent factors in statistical multi-relational learning," LSE Research Online Documents on Economics 102110, London School of Economics and Political Science, LSE Library.
    15. Rosaria Lombardo & Eric J. Beh & Luis Guerrero, 2019. "Analysis of three-way non-symmetrical association of food concepts in cross-cultural marketing," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2323-2337, September.
    16. Zhang, Shuang & Han, Le, 2023. "Robust tensor recovery with nonconvex and nonsmooth regularization," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    17. Domino, Krzysztof, 2020. "Multivariate cumulants in outlier detection for financial data analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    18. 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.
    19. 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.
    20. 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.

    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:spr:psycho:v:41:y:1976:i:2:p:177-188. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.