IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v1y1984i1p147-186.html
   My bibliography  Save this article

GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data

Author

Listed:
  • Wayne DeSarbo
  • Vithala Rao

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:jclass:v:1:y:1984:i:1:p:147-186
    DOI: 10.1007/BF01890122
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01890122
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01890122?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. Peter Schönemann & Ming Wang, 1972. "An individual difference model for the multidimensional analysis of preference data," Psychometrika, Springer;The Psychometric Society, vol. 37(3), pages 275-309, September.
    2. John Ross & Norman Cliff, 1964. "A generalization of the interpoint distance model," Psychometrika, Springer;The Psychometric Society, vol. 29(2), pages 167-176, June.
    3. J. Douglas Carroll & Sandra Pruzansky & Joseph Kruskal, 1980. "Candelinc: A general approach to multidimensional analysis of many-way arrays with linear constraints on parameters," Psychometrika, Springer;The Psychometric Society, vol. 45(1), pages 3-24, March.
    4. 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.
    5. John Davidson, 1973. "A geometrical analysis of the unfolding model: General solutions," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 305-336, September.
    6. V. Srinivasan & Allan Shocker, 1973. "Linear programming techniques for multidimensional analysis of preferences," Psychometrika, Springer;The Psychometric Society, vol. 38(3), pages 337-369, September.
    7. Wayne S. DeSarbo & J. Douglas Carroll & Donald R. Lehmann & John O'Shaughnessy, 1982. "Three-Way Multivariate Conjoint Analysis," Marketing Science, INFORMS, vol. 1(4), pages 323-350.
    8. J. Kruskal, 1964. "Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis," Psychometrika, Springer;The Psychometric Society, vol. 29(1), pages 1-27, March.
    9. Ingwer Borg & James Lingoes, 1980. "A model and algorithm for multidimensional scaling with external constraints on the distances," Psychometrika, Springer;The Psychometric Society, vol. 45(1), pages 25-38, March.
    10. Joseph Bennett & William Hays, 1960. "Multidimensional unfolding: Determining the dimensionality of ranked preference data," Psychometrika, Springer;The Psychometric Society, vol. 25(1), pages 27-43, March.
    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. Junghyun Park & Minki Kim & Pradeep K Chintagunta, 2022. "Mapping Consumers’ Context-Dependent Consumption Preferences: A Multidimensional Unfolding Approach [An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations o," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 49(2), pages 202-228.
    2. van de Velden, M. & de Beuckelaer, A. & Groenen, P.J.F. & Busing, F.M.T.A., 2011. "Nonmetric Unfolding of Marketing Data: Degeneracy and Stability," ERIM Report Series Research in Management ERS-2011-006-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    3. Kamel Jedidi & Wayne DeSarbo, 1991. "A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 471-494, September.
    4. Joonwook Park & Priyali Rajagopal & Wayne DeSarbo, 2012. "A New Heterogeneous Multidimensional Unfolding Procedure," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 263-287, April.
    5. Yoshio Takane & Tadashi Shibayama, 1991. "Principal component analysis with external information on both subjects and variables," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 97-120, March.
    6. Shobhit Agarwal & A. K. Dey, 2010. "Perception Mapping of Travelers: Case of Six Indian Domestic Airlines," American Journal of Economics and Business Administration, Science Publications, vol. 2(2), pages 141-146, June.
    7. DeSarbo Wayne S., 2010. "A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-24, April.
    8. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
    9. Martin Natter & Andreas Mild & Udo Wagner & Alfred Taudes, 2008. "—Planning New Tariffs at tele.ring: The Application and Impact of an Integrated Segmentation, Targeting, and Positioning Tool," Marketing Science, INFORMS, vol. 27(4), pages 600-609, 07-08.
    10. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    11. Douglas Clarkson & Richard Gonzalez, 2001. "Random effects diagonal metric multidimensional scaling models," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 25-43, March.
    12. Thomas Eckes & Peter Orlik, 1993. "An error variance approach to two-mode hierarchical clustering," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 51-74, January.
    13. Wayne DeSarbo & Donald Lehmann & Gregory Carpenter & Indrajit Sinha, 1996. "A stochastic multidimensional unfolding approach for representing phased decision outcomes," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 485-508, September.
    14. Geert Soete & J. Carroll & Anil Chaturvedi, 1993. "A modified CANDECOMP method for fitting the extended INDSCAL model," Journal of Classification, Springer;The Classification Society, vol. 10(1), pages 75-92, January.
    15. Wayne DeSarbo & Kamel Jedidi & Joel Steckel, 1991. "A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 279-307, June.
    16. Frank Busing & Mark Rooij, 2009. "Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 329-360, December.
    17. Wayne DeSarbo & Robert Madrigal, 2012. "Exploring the Demand Aspects of Sports Consumption and Fan Avidity," Interfaces, INFORMS, vol. 42(2), pages 199-212, April.
    18. Yoshio Takane & Haruo Yanai & Shinichi Mayekawa, 1991. "Relationships among several methods of linearly constrained correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 667-684, December.
    19. Wayne DeSarbo & Michael Johnson & Ajay Manrai & Lalita Manrai & Elizabeth Edwards, 1992. "Tscale: A new multidimensional scaling procedure based on tversky's contrast model," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 43-69, March.

    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. Wayne DeSarbo & Kamel Jedidi & Joel Steckel, 1991. "A stochastic multidimensional scaling procedure for the empirical determination of convex indifference curves for preference/choice analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 279-307, June.
    2. J. Carroll, 1985. "Review," Psychometrika, Springer;The Psychometric Society, vol. 50(1), pages 133-140, March.
    3. Mark Davison, 1976. "Fitting and testing carroll's weighted unfolding model for preferences," Psychometrika, Springer;The Psychometric Society, vol. 41(2), pages 233-247, June.
    4. Wayne DeSarbo & Michael Johnson & Ajay Manrai & Lalita Manrai & Elizabeth Edwards, 1992. "Tscale: A new multidimensional scaling procedure based on tversky's contrast model," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 43-69, March.
    5. DeSarbo, Wayne S. & Selin Atalay, A. & Blanchard, Simon J., 2009. "A three-way clusterwise multidimensional unfolding procedure for the spatial representation of context dependent preferences," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3217-3230, June.
    6. Mark Davison, 1988. "A reformulation of the general Euclidean model for the external analysis of preference data," Psychometrika, Springer;The Psychometric Society, vol. 53(3), pages 305-320, September.
    7. Geert Soete & Willem Heiser, 1993. "A latent class unfolding model for analyzing single stimulus preference ratings," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 545-565, December.
    8. John Gower & Michael Greenacre, 1996. "Unfolding a symmetric matrix," Journal of Classification, Springer;The Classification Society, vol. 13(1), pages 81-105, March.
    9. Duncan Fong & Wayne DeSarbo & Zhe Chen & Zhuying Xu, 2015. "A Bayesian Vector Multidimensional Scaling Procedure Incorporating Dimension Reparameterization with Variable Selection," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 1043-1065, December.
    10. Yoshio Takane & Tadashi Shibayama, 1991. "Principal component analysis with external information on both subjects and variables," Psychometrika, Springer;The Psychometric Society, vol. 56(1), pages 97-120, March.
    11. Yoshio Takane & Haruo Yanai & Shinichi Mayekawa, 1991. "Relationships among several methods of linearly constrained correspondence analysis," Psychometrika, Springer;The Psychometric Society, vol. 56(4), pages 667-684, December.
    12. Joonwook Park & Priyali Rajagopal & Wayne DeSarbo, 2012. "A New Heterogeneous Multidimensional Unfolding Procedure," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 263-287, April.
    13. Keith Poole, 1990. "Least squares metric, unidimensional scaling of multivariate linear models," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 123-149, March.
    14. DeSarbo Wayne S., 2010. "A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-24, April.
    15. George Rabinowitz, 1976. "A procedure for ordering object pairs consistent with the multidimensional unfolding model," Psychometrika, Springer;The Psychometric Society, vol. 41(3), pages 349-373, September.
    16. Kamel Jedidi & Wayne DeSarbo, 1991. "A stochastic multidimensional scaling procedure for the spatial representation of three-mode, three-way pick any/J data," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 471-494, September.
    17. U. Umesh & Sanjay Mishra, 1990. "A Monte Carlo investigation of conjoint analysis index-of-fit: Goodness of fit, significance and power," Psychometrika, Springer;The Psychometric Society, vol. 55(1), pages 33-44, March.
    18. 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.
    19. Shizuhiko Nishisato, 1996. "Gleaning in the field of dual scaling," Psychometrika, Springer;The Psychometric Society, vol. 61(4), pages 559-599, December.
    20. Frank Busing & Mark Rooij, 2009. "Unfolding Incomplete Data: Guidelines for Unfolding Row-Conditional Rank Order Data with Random Missings," Journal of Classification, Springer;The Classification Society, vol. 26(3), pages 329-360, December.

    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:jclass:v:1:y:1984:i:1:p:147-186. 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.