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Decreasing Respondent Heterogeneity by Likert Scales Adjustment via Multipoles

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  • Stan Lipovetsky

    (GfK North America, 13417 Inverness Rd., Minnetonka, MN 55305, USA)

  • Michael Conklin

    (GfK North America, 13417 Inverness Rd., Minnetonka, MN 55305, USA)

Abstract

A description of Likert scales can be given using the multipoles technique known in quantum physics and applied to behavioral sciences data. This paper considers decomposition of Likert scales by the multipoles for the application of decreasing the respondents’ heterogeneity. Due to cultural and language differences, different respondents habitually use the lower end, the mid-scale, or the upper end of the Likert scales which can lead to distortion and inconsistency in data across respondents. A big impact of different kinds of respondent is well known, for instance, in international studies, and it is called the problem of high and low raters. Application of a multipoles technique to the row-data smoothing via prediction of individual rates by the histogram of the Likert scale tiers produces better results than standard row-centering in data. A numerical example by marketing research data shows that the results are encouraging: while a standard row-centering produces a poor outcome, the dipole-adjustment noticeably improves the obtained segmentation results.

Suggested Citation

  • Stan Lipovetsky & Michael Conklin, 2018. "Decreasing Respondent Heterogeneity by Likert Scales Adjustment via Multipoles," Stats, MDPI, vol. 1(1), pages 1-7, November.
    • Handle: RePEc:gam:jstats:v:1:y:2018:i:1:p:12-175:d:182738
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    References listed on IDEAS

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    Cited by:

    1. Shuang Ren & Di Fan & Guiyao Tang, 2023. "Organizations’ Management Configurations Towards Environment and Market Performances," Journal of Business Ethics, Springer, vol. 188(2), pages 239-257, November.
    2. Stan Lipovetsky, 2023. "Quantum-like Data Modeling in Applied Sciences: Review," Stats, MDPI, vol. 6(1), pages 1-9, February.

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