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Constrained Dual Scaling for Detecting Response Styles in Categorical Data

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  • Schoonees, P.C.
  • van de Velden, M.
  • Groenen, P.J.F.

Abstract

Dual scaling 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 dual scaling and correspondence analysis literature. It is shown here that a peculiarity of the dual scaling 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 dual scaling 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.

Suggested Citation

  • Schoonees, P.C. & van de Velden, M. & Groenen, P.J.F., 2013. "Constrained Dual Scaling for Detecting Response Styles in Categorical Data," Econometric Institute Research Papers EI 2013-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:39181
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    1. 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.
    2. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    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, 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.
    6. 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.
    7. 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.
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    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.

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