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Mitigating the Effects of Social Desirability Bias in Self-Report Surveys: Classical and New Techniques

Mitigating the Effects of Social Desirability Bias in Self-Report Surveys: Classical and New Techniques

Ahmet Durmaz, İnci Dursun, Ebru Tümer Kabadayi
Copyright: © 2020 |Pages: 40
ISBN13: 9781799810254|ISBN10: 1799810259|ISBN13 Softcover: 9781799810261|EISBN13: 9781799810278
DOI: 10.4018/978-1-7998-1025-4.ch007
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MLA

Durmaz, Ahmet, et al. "Mitigating the Effects of Social Desirability Bias in Self-Report Surveys: Classical and New Techniques." Applied Social Science Approaches to Mixed Methods Research, edited by Mette Lise Baran and Janice Elisabeth Jones, IGI Global, 2020, pp. 146-185. https://doi.org/10.4018/978-1-7998-1025-4.ch007

APA

Durmaz, A., Dursun, İ., & Kabadayi, E. T. (2020). Mitigating the Effects of Social Desirability Bias in Self-Report Surveys: Classical and New Techniques. In M. Baran & J. Jones (Eds.), Applied Social Science Approaches to Mixed Methods Research (pp. 146-185). IGI Global. https://doi.org/10.4018/978-1-7998-1025-4.ch007

Chicago

Durmaz, Ahmet, İnci Dursun, and Ebru Tümer Kabadayi. "Mitigating the Effects of Social Desirability Bias in Self-Report Surveys: Classical and New Techniques." In Applied Social Science Approaches to Mixed Methods Research, edited by Mette Lise Baran and Janice Elisabeth Jones, 146-185. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1025-4.ch007

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Abstract

Self-reporting is a frequently used method to measure various constructs in many areas of social science research. Literature holds abundant evidence that social desirability bias (SDB), which is a special kind of response bias, can severely plague the validity and accuracy of the self-report survey measurements. However, in many areas of behavioral research, there is little or no alternative to self-report surveys for collecting data about specific constructs that only the respondents may have the information about. Thus, researchers need to detect or minimize SDB to improve the quality of overall data and their deductions drawn from them. Literature provides a number of techniques for minimizing SDB during survey procedure and statistical measurement methods to detect and minimize the validity-destructive impact of SDB. This study aims to explicate the classical and new techniques for mitigating the SDB and to provide a guideline for the researchers, especially for those who focus on socially sensitive constructs.

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