IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v29y1964i2p167-176.html
   My bibliography  Save this article

A generalization of the interpoint distance model

Author

Listed:
  • John Ross
  • Norman Cliff

Abstract

No abstract is available for this item.

Suggested Citation

  • John Ross & Norman Cliff, 1964. "A generalization of the interpoint distance model," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 167-176, June.
    • Handle: RePEc:spr:psycho:v:29:y:1964:i:2:p:167-176
    DOI: 10.1007/BF02289698
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF02289698
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF02289698?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.

    Citations

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


    Cited by:

    1. Wayne DeSarbo & Vithala Rao, 1984. "GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 147-186, December.
    2. Peter Schönemann, 1970. "On metric multidimensional unfolding," Psychometrika, Springer;The Psychometric Society, vol. 35(3), pages 349-366, September.
    3. Gordon Bechtel & Ledyard Tucker & Wei-Ching Chang, 1971. "A scalar product model for the multidimensional scaling of choice," Psychometrika, Springer;The Psychometric Society, vol. 36(4), pages 369-388, December.
    4. Forrest Young & Norman Cliff, 1972. "Interactive scaling with individual subjects," Psychometrika, Springer;The Psychometric Society, vol. 37(4), pages 385-415, December.
    5. Michael Greenacre & Michael Browne, 1986. "An efficient alternating least-squares algorithm to perform multidimensional unfolding," Psychometrika, Springer;The Psychometric Society, vol. 51(2), pages 241-250, June.
    6. Polak, Marike & Heiser, Willem J. & de Rooij, Mark, 2009. "Two types of single-peaked data: Correspondence analysis as an alternative to principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3117-3128, 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:spr:psycho:v:29:y:1964:i:2:p:167-176. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.