IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v78y2013i2p322-340.html
   My bibliography  Save this article

A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification

Author

Listed:
  • Simon Blanchard
  • Wayne DeSarbo

Abstract

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic example and a consumer psychology study involving categories of restaurant brands illustrate how the application of the proposed methodology to the new sorting task can account for a variety of categorization phenomena including multiple category memberships and for heterogeneity through individual differences in the saliency of latent category structures. Copyright The Psychometric Society 2013

Suggested Citation

  • Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    • Handle: RePEc:spr:psycho:v:78:y:2013:i:2:p:322-340
    DOI: 10.1007/s11336-012-9315-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-012-9315-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-012-9315-z?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. Loken, Barbara & Ward, James C, 1990. "Alternative Approaches to Understanding the Determinants of Typicality," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(2), pages 111-126, September.
    2. Kaye Basford & Geoffrey McLachlan, 1985. "The mixture method of clustering applied to three-way data," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 109-125, December.
    3. Simon Blanchard & Wayne DeSarbo & A. Atalay & Nukhet Harmancioglu, 2012. "Identifying consumer heterogeneity in unobserved categories," Marketing Letters, Springer, vol. 23(1), pages 177-194, March.
    4. J. Carroll & Phipps Arabie, 1983. "Indclus: An individual differences generalization of the adclus model and the mapclus algorithm," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 157-169, June.
    5. J. Carroll & Linda Clark & Wayne DeSarbo, 1984. "The representation of three-way proximity data by single and multiple tree structure models," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 25-74, December.
    6. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-238, July-Sept.
    7. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    8. T. Klastorin, 1980. "Merging groups to maximize object partition comparison," Psychometrika, Springer;The Psychometric Society, vol. 45(4), pages 425-433, December.
    9. Richard Degerman, 1982. "Ordered binary trees constructed through an application of Kendall's tau," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 523-527, December.
    10. John Daws, 1996. "The analysis of free-sorting data: Beyond pairwise cooccurrences," Journal of Classification, Springer;The Classification Society, vol. 13(1), pages 57-80, March.
    11. Chih-Chien Yang & Chih-Chiang Yang, 2007. "Separating Latent Classes by Information Criteria," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 183-203, September.
    12. Moreau, C Page & Markman, Arthur B & Lehmann, Donald R, 2001. ""What Is It?" Categorization Flexibility and Consumers' Responses to Really New Products," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(4), pages 489-498, March.
    13. Juliano Laran & Chris Janiszewski & Marcus Cunha Jr., 2008. "Context-Dependent Effects of Goal Primes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 35(4), pages 653-667, September.
    14. Suzanne Winsberg & Geert Soete, 1993. "A latent class approach to fitting the weighted Euclidean model, clascal," Psychometrika, Springer;The Psychometric Society, vol. 58(2), pages 315-330, June.
    15. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    16. Simon Blanchard & Daniel Aloise & Wayne DeSarbo, 2012. "The Heterogeneous P-Median Problem for Categorization Based Clustering," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 741-762, October.
    17. Suzanne Winsberg & J. Douglas Carroll, 1989. "A quasi-nonmetric method for multidimensional scaling VIA an extended euclidean model," Psychometrika, Springer;The Psychometric Society, vol. 54(2), pages 217-229, June.
    18. Kurt A. Carlson & Margaret G. Meloy & J. Edward Russo, 2006. "Leader-Driven Primacy: Using Attribute Order to Affect Consumer Choice," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 32(4), pages 513-518, March.
    19. Vermunt, Jeroen K., 2007. "A hierarchical mixture model for clustering three-way data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5368-5376, July.
    20. J. Ramsay, 1977. "Maximum likelihood estimation in multidimensional scaling," Psychometrika, Springer;The Psychometric Society, vol. 42(2), pages 241-266, June.
    21. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    22. Venkatram Ramaswamy & Eugene W. Anderson & Wayne S. DeSarbo, 1994. "A Disaggregate Negative Binomial Regression Procedure for Count Data Analysis," Management Science, INFORMS, vol. 40(3), pages 405-417, 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. Santi, Éverton & Aloise, Daniel & Blanchard, Simon J., 2016. "A model for clustering data from heterogeneous dissimilarities," European Journal of Operational Research, Elsevier, vol. 253(3), pages 659-672.
    2. Rebecca Hamilton & Debora Thompson & Zachary Arens & Simon Blanchard & Gerald Häubl & P. Kannan & Uzma Khan & Donald Lehmann & Margaret Meloy & Neal Roese & Manoj Thomas, 2014. "Consumer substitution decisions: an integrative framework," Marketing Letters, Springer, vol. 25(3), pages 305-317, 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. Bocci, Laura & Vicari, Donatella & Vichi, Maurizio, 2006. "A mixture model for the classification of three-way proximity data," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1625-1654, April.
    2. Simon Blanchard & Wayne DeSarbo & A. Atalay & Nukhet Harmancioglu, 2012. "Identifying consumer heterogeneity in unobserved categories," Marketing Letters, Springer, vol. 23(1), pages 177-194, March.
    3. Simon Blanchard & Daniel Aloise & Wayne DeSarbo, 2012. "The Heterogeneous P-Median Problem for Categorization Based Clustering," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 741-762, October.
    4. Martin Young & Wayne DeSarbo, 1995. "A parametric procedure for ultrametric tree estimation from conditional rank order proximity data," Psychometrika, Springer;The Psychometric Society, vol. 60(1), pages 47-75, March.
    5. Wedel, Michel & DeSarbo, Wayne S., 1996. "Semiparametric estimation of (constrained) ultrametric trees," Research Report 96B34, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    6. Suzanne Winsberg & Geert Soete, 1993. "A latent class approach to fitting the weighted Euclidean model, clascal," Psychometrika, Springer;The Psychometric Society, vol. 58(2), pages 315-330, June.
    7. repec:dgr:rugsom:96b34 is not listed on IDEAS
    8. Vichi, Maurizio, 1998. "Principal classifications analysis: a method for generating consensus dendrograms and its application to three-way data," Computational Statistics & Data Analysis, Elsevier, vol. 27(3), pages 311-331, May.
    9. Willem Heiser, 2013. "In memoriam, J. Douglas Carroll 1939–2011," Psychometrika, Springer;The Psychometric Society, vol. 78(1), pages 5-13, January.
    10. Vera, J. Fernando & Macas, Rodrigo & Heiser, Willem J., 2009. "A dual latent class unfolding model for two-way two-mode preference rating data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3231-3244, June.
    11. Gabriele Pizzi & Gian Luca Marzocchi, 2020. "Consumer-defined assortments: application of card-sorting to category management," Italian Journal of Marketing, Springer, vol. 2020(1), pages 67-84, March.
    12. Bijmolt, T.H.A. & Wedel, M. & DeSarbo, W.S., 2002. "Adaptive Multidimensional Scaling : The Spatial Representation of Brand Consideration and Dissimilarity Judgments," Other publications TiSEM 26b65f04-0d5f-42d6-8a85-8, Tilburg University, School of Economics and Management.
    13. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2014. "Identifying Small Market Segments with Mixture Regression Models," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(4), pages 812-812.
    14. J. Vera & Rodrigo Macías & Willem Heiser, 2009. "A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data," Psychometrika, Springer;The Psychometric Society, vol. 74(2), pages 297-315, June.
    15. Groenen, P.J.F. & Winsberg, S. & Rodriguez, O. & Diday, E., 2006. "I-Scal: Multidimensional scaling of interval dissimilarities," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 360-378, November.
    16. Kohn, Hans-Friedrich, 2006. "Combinatorial individual differences scaling within the city-block metric," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 931-946, November.
    17. Honghu Liu & Yan Zheng & Jie Shen, 2008. "Goodness-of-fit measures of R2 for repeated measures mixed effect models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1081-1092.
    18. Bartolucci, Francesco & Bacci, Silvia & Pigini, Claudia, 2017. "Misspecification test for random effects in generalized linear finite-mixture models for clustered binary and ordered data," Econometrics and Statistics, Elsevier, vol. 3(C), pages 112-131.
    19. Bijmolt, T.H.A. & Wedel, M. & DeSarbo, W.S., 2002. "Adaptive Multidimensional Scaling : The Spatial Representation of Brand Consideration and Dissimilarity Judgments," Discussion Paper 2002-82, Tilburg University, Center for Economic Research.
    20. Santi, Éverton & Aloise, Daniel & Blanchard, Simon J., 2016. "A model for clustering data from heterogeneous dissimilarities," European Journal of Operational Research, Elsevier, vol. 253(3), pages 659-672.
    21. Adri Smaling & Geert Soete, 1992. "Reviews," Psychometrika, Springer;The Psychometric Society, vol. 57(3), pages 451-457, September.

    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:78:y:2013:i:2:p:322-340. 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.