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Detecting Biased Items When Developing a Scale: A Quantitative Method

Author

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  • Jean-Charles Pillet

    (ESC Grenoble - Ecole Supérieure de Commerce de Grenoble - EESC-GEM Grenoble Ecole de Management)

  • Claudio Vitari

    (AMU - Aix Marseille Université)

  • Federico Pigni

    (CETIC asbl - Centre d’Excellence en Technologies de l’Information et de la Communication)

  • Kevin Carillo

Abstract

In survey research, it is well known that the quality of responses is significantly altered by apparently trivial variations in the linguistic or grammatical properties of survey items. Yet numerous seemingly minor changes are made to survey items in the course of the scale development process so that they comply with other requirements (e.g., content validity). As a result, researchers may inadvertently introduce systematic measurement error that is not accounted for in the final model. Remedies to this problem are widely known, but reliable methods to diagnose it do not readily exist. In an effort to address this shortcoming, we develop a quantitative method to detect biased items and reinforce the reliability of IS measurement instruments. In this paper, we provide step by step implementation guidelines and show how to apply the method and interpret the output results.

Suggested Citation

  • Jean-Charles Pillet & Claudio Vitari & Federico Pigni & Kevin Carillo, 2018. "Detecting Biased Items When Developing a Scale: A Quantitative Method," Post-Print halshs-01923612, HAL.
    • Handle: RePEc:hal:journl:halshs-01923612
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01923612
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    References listed on IDEAS

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    1. Fuller, Christie M. & Simmering, Marcia J. & Atinc, Guclu & Atinc, Yasemin & Babin, Barry J., 2016. "Common methods variance detection in business research," Journal of Business Research, Elsevier, vol. 69(8), pages 3192-3198.
    2. Dorothy Watson, 1992. "Correcting for Acquiescent Response Bias in the Absence of a Balanced Scale," Sociological Methods & Research, , vol. 21(1), pages 52-88, August.
    3. Grace Yao & Chia-huei Wu & Cheng-ta Yang, 2008. "Examining the content validity of the WHOQOL-BREF from respondents’ perspective by quantitative methods," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 85(3), pages 483-498, February.
    4. Zaichkowsky, Judith Lynne, 1985. "Measuring the Involvement Construct," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(3), pages 341-352, December.
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