IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v80y2015i4p968-994.html
   My bibliography  Save this article

Constrained Dual Scaling for Detecting Response Styles in Categorical Data

Author

Listed:
  • Pieter Schoonees
  • Michel Velden
  • Patrick Groenen

Abstract

Dual scaling (DS) is a multivariate exploratory method equivalent to correspondence analysis when analysing contingency tables. However, for the analysis of rating data, different proposals appear in the DS and correspondence analysis literature. It is shown here that a peculiarity of the DS method can be exploited to detect differences in response styles. Response styles occur when respondents use rating scales differently for reasons not related to the questions, often biasing results. A spline-based constrained version of DS is devised which can detect the presence of four prominent types of response styles, and is extended to allow for multiple response styles. An alternating nonnegative least squares algorithm is devised for estimating the parameters. The new method is appraised both by simulation studies and an empirical application. Copyright The Author(s) 2015

Suggested Citation

  • Pieter Schoonees & Michel Velden & Patrick Groenen, 2015. "Constrained Dual Scaling for Detecting Response Styles in Categorical Data," Psychometrika, Springer;The Psychometric Society, vol. 80(4), pages 968-994, December.
    • Handle: RePEc:spr:psycho:v:80:y:2015:i:4:p:968-994
    DOI: 10.1007/s11336-015-9458-9
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-015-9458-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-015-9458-9?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hand, David J. & Krzanowski, Wojtek J., 2005. "Optimising k-means clustering results with standard software packages," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 969-973, June.
    2. van de Velden, Michel & Groenen, Patrick J.F. & Poblome, Jeroen, 2009. "Seriation by constrained correspondence analysis: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3129-3138, June.
    3. Hofert, Marius & Maechler, Martin, 2011. "Nested Archimedean Copulas Meet R: The nacopula Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i09).
    4. Timothy Johnson, 2003. "On the use of heterogeneous thresholds ordinal regression models to account for individual differences in response style," Psychometrika, Springer;The Psychometric Society, vol. 68(4), pages 563-583, December.
    5. van de Velden, M., 2007. "Detecting response styles by using dual scaling of successive categories," Econometric Institute Research Papers EI 2007-41, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Martijn Jong & Jan-Benedict Steenkamp, 2010. "Finite Mixture Multilevel Multidimensional Ordinal IRT Models for Large Scale Cross-Cultural Research," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 3-32, March.
    7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    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. Pieter C. Schoonees & Patrick J. F. Groenen & Michel Velden, 2022. "Least-squares bilinear clustering of three-way data," 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. 16(4), pages 1001-1037, December.

    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. Michael Brusco & Douglas Steinley, 2007. "A Comparison of Heuristic Procedures for Minimum Within-Cluster Sums of Squares Partitioning," Psychometrika, Springer;The Psychometric Society, vol. 72(4), pages 583-600, December.
    2. Joeri Hofmans & Eva Ceulemans & Douglas Steinley & Iven Mechelen, 2015. "On the Added Value of Bootstrap Analysis for K-Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 268-284, July.
    3. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    4. Tsai, Chieh-Yuan & Chiu, Chuang-Cheng, 2008. "Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4658-4672, June.
    5. Schepers, Jan & van Mechelen, Iven & Ceulemans, Eva, 2006. "Three-mode partitioning," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1623-1642, December.
    6. Miriam Aparicio, 2021. "Resiliency and Cooperation or Regarding Social and Collective Competencies for University Achievement. An Analysis from a Systemic Perspective," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 8, ejser_v8_.
    7. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    8. Wu, Han-Ming & Tien, Yin-Jing & Chen, Chun-houh, 2010. "GAP: A graphical environment for matrix visualization and cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 767-778, March.
    9. José E. Chacón, 2021. "Explicit Agreement Extremes for a 2 × 2 Table with Given Marginals," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 257-263, July.
    10. F. Marta L. Di Lascio & Andrea Menapace & Roberta Pappadà, 2024. "A spatially‐weighted AMH copula‐based dissimilarity measure for clustering variables: An application to urban thermal efficiency," Environmetrics, John Wiley & Sons, Ltd., vol. 35(1), February.
    11. Yifan Zhu & Chongzhi Di & Ying Qing Chen, 2019. "Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 238-261, July.
    12. Irene Vrbik & Paul McNicholas, 2015. "Fractionally-Supervised Classification," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 359-381, October.
    13. Maurizio Vichi & Carlo Cavicchia & Patrick J. F. Groenen, 2022. "Hierarchical Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 553-577, November.
    14. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    15. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    16. Patrick D. Shay & Stephen S. Farnsworth Mick, 2017. "Clustered and distinct: a taxonomy of local multihospital systems," Health Care Management Science, Springer, vol. 20(3), pages 303-315, September.
    17. Roberto Rocci & Stefano Antonio Gattone & Roberto Di Mari, 2018. "A data driven equivariant approach to constrained Gaussian mixture modeling," 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. 12(2), pages 235-260, June.
    18. Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
    19. Matthijs Warrens, 2010. "Inequalities Between Kappa and Kappa-Like Statistics for k×k Tables," Psychometrika, Springer;The Psychometric Society, vol. 75(1), pages 176-185, March.
    20. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

    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:80:y:2015:i:4:p:968-994. 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.